Gordon Smyth
| Snapshot Date: 2024-03-27 14:05:05 -0400 (Wed, 27 Mar 2024) |
| git_url: https://git.bioconductor.org/packages/limma |
| git_branch: RELEASE_3_18 |
| git_last_commit: 27e81a0 |
| git_last_commit_date: 2023-10-30 16:50:54 -0400 (Mon, 30 Oct 2023) |
| Back to Multiple platform build/check report for BioC 3.18: simplified long |
|
This page was generated on 2024-03-29 11:37:52 -0400 (Fri, 29 Mar 2024).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.3.3 (2024-02-29) -- "Angel Food Cake" | 4669 |
| palomino4 | Windows Server 2022 Datacenter | x64 | 4.3.3 (2024-02-29 ucrt) -- "Angel Food Cake" | 4404 |
| merida1 | macOS 12.7.1 Monterey | x86_64 | 4.3.3 (2024-02-29) -- "Angel Food Cake" | 4427 |
| Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X | ||||
| Package 1094/2266 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| limma 3.58.1 (landing page) Gordon Smyth
| nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | OK | | ||||||||
| palomino4 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | | ||||||||
| merida1 | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | | ||||||||
| kjohnson1 | macOS 13.6.1 Ventura / arm64 | see weekly results here | ||||||||||||
|
To the developers/maintainers of the limma package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/limma.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
| Package: limma |
| Version: 3.58.1 |
| Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:limma.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings limma_3.58.1.tar.gz |
| StartedAt: 2024-03-28 04:38:48 -0400 (Thu, 28 Mar 2024) |
| EndedAt: 2024-03-28 04:42:22 -0400 (Thu, 28 Mar 2024) |
| EllapsedTime: 214.3 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: limma.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### Running command:
###
### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:limma.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings limma_3.58.1.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.18-bioc/meat/limma.Rcheck’
* using R version 4.3.3 (2024-02-29)
* using platform: x86_64-apple-darwin20 (64-bit)
* R was compiled by
Apple clang version 14.0.0 (clang-1400.0.29.202)
GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘limma/DESCRIPTION’ ... OK
* this is package ‘limma’ version ‘3.58.1’
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘limma’ can be installed ... OK
* used C compiler: ‘Apple clang version 14.0.0 (clang-1400.0.29.202)’
* used SDK: ‘MacOSX11.3.sdk’
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking startup messages can be suppressed ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking compiled code ... NOTE
Note: information on .o files is not available
* checking sizes of PDF files under ‘inst/doc’ ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
alias2Symbol 10.788 0.644 12.743
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
Running ‘limma-Tests.R’
Comparing ‘limma-Tests.Rout’ to ‘limma-Tests.Rout.save’ ... OK
OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 1 NOTE
See
‘/Users/biocbuild/bbs-3.18-bioc/meat/limma.Rcheck/00check.log’
for details.
limma.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL limma ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library’ * installing *source* package ‘limma’ ... ** using staged installation ** libs using C compiler: ‘Apple clang version 14.0.0 (clang-1400.0.29.202)’ using SDK: ‘MacOSX11.3.sdk’ clang -arch x86_64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/x86_64/include -fPIC -falign-functions=64 -Wall -g -O2 -c init.c -o init.o clang -arch x86_64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/x86_64/include -fPIC -falign-functions=64 -Wall -g -O2 -c normexp.c -o normexp.o clang -arch x86_64 -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG -I/opt/R/x86_64/include -fPIC -falign-functions=64 -Wall -g -O2 -c weighted_lowess.c -o weighted_lowess.o clang -arch x86_64 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -L/Library/Frameworks/R.framework/Resources/lib -L/opt/R/x86_64/lib -o limma.so init.o normexp.o weighted_lowess.o -F/Library/Frameworks/R.framework/.. -framework R -Wl,-framework -Wl,CoreFoundation installing to /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library/00LOCK-limma/00new/limma/libs ** R ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** checking absolute paths in shared objects and dynamic libraries ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (limma)
limma.Rcheck/tests/limma-Tests.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(limma)
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE,width=120)
>
> set.seed(0); u <- runif(100)
>
> ### strsplit2
>
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
[,1] [,2] [,3]
[1,] "ab" "cd" "efg"
[2,] "abc" "def" ""
[3,] "z" "" ""
[4,] "" "" ""
>
> ### removeext
>
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1" "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"
>
> ### printorder
>
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
[1] 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15 14 13 24 23 22 21 20 19 30 29 28 27
[29] 26 25 36 35 34 33 32 31 42 41 40 39 38 37 48 47 46 45 44 43 6 5 4 3 2 1 12 11
[57] 10 9 8 7 18 17 16 15 14 13 24 23 22 21 20 19 30 29 28 27 26 25 36 35 34 33 32 31
[85] 42 41 40 39 38 37 48 47 46 45 44 43 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15
[113] 14 13 24 23 22 21 20 19 30 29 28 27 26 25 36 35 34 33 32 31 42 41 40 39 38 37 48 47
[141] 46 45 44 43 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15 14 13 24 23 22 21 20 19
[169] 30 29 28 27 26 25 36 35 34 33 32 31 42 41 40 39 38 37 48 47 46 45 44 43 54 53 52 51
[197] 50 49 60 59 58 57 56 55 66 65 64 63 62 61 72 71 70 69 68 67 78 77 76 75 74 73 84 83
[225] 82 81 80 79 90 89 88 87 86 85 96 95 94 93 92 91 54 53 52 51 50 49 60 59 58 57 56 55
[253] 66 65 64 63 62 61 72 71 70 69 68 67 78 77 76 75 74 73 84 83 82 81 80 79 90 89 88 87
[281] 86 85 96 95 94 93 92 91 54 53 52 51 50 49 60 59 58 57 56 55 66 65 64 63 62 61 72 71
[309] 70 69 68 67 78 77 76 75 74 73 84 83 82 81 80 79 90 89 88 87 86 85 96 95 94 93 92 91
[337] 54 53 52 51 50 49 60 59 58 57 56 55 66 65 64 63 62 61 72 71 70 69 68 67 78 77 76 75
[365] 74 73 84 83 82 81 80 79 90 89 88 87 86 85 96 95 94 93 92 91 102 101 100 99 98 97 108 107
[393] 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[421] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100 99 98 97 108 107 106 105 104 103 114 113 112 111
[449] 110 109 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133 144 143
[477] 142 141 140 139 102 101 100 99 98 97 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100 99
[533] 98 97 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121 132 131
[561] 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139 150 149 148 147 146 145 156 155 154 153 152 151
[589] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183
[617] 182 181 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157 168 167
[645] 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[673] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171
[701] 170 169 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145 156 155
[729] 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187
$plate
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[58] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[115] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[172] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[229] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[286] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[343] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[400] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[457] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[514] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[571] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[628] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[685] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[742] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
$plate.r
[1] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[39] 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[77] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[115] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[153] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[191] 1 1 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
[229] 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
[267] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[305] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5
[343] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[381] 5 5 5 5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[419] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[457] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[495] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 9 9 9 9
[533] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
[571] 9 9 9 9 9 9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[609] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[647] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14 14 14 14 14 14 14 14 14 14
[685] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13
[723] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[761] 13 13 13 13 13 13 13 13
$plate.c
[1] 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21
[39] 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14
[77] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7
[115] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6
[153] 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23
[191] 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16
[229] 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15
[267] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8
[305] 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1
[343] 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24
[381] 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17
[419] 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10
[457] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9
[495] 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2
[533] 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19
[571] 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18
[609] 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11
[647] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4
[685] 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3
[723] 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20
[761] 19 19 24 24 23 23 22 22
$plateposition
[1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05" "p1D05" "p1D04" "p1D04" "p1D09" "p1D09"
[15] "p1D08" "p1D08" "p1D07" "p1D07" "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14" "p1D14"
[29] "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16" "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19"
[43] "p1D24" "p1D24" "p1D23" "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01" "p1C06" "p1C06"
[57] "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08" "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11"
[71] "p1C10" "p1C10" "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17" "p1C17" "p1C16" "p1C16"
[85] "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19" "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03"
[99] "p1B02" "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04" "p1B09" "p1B09" "p1B08" "p1B08"
[113] "p1B07" "p1B07" "p1B12" "p1B12" "p1B11" "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20" "p1B20" "p1B19" "p1B19" "p1B24" "p1B24"
[141] "p1B23" "p1B23" "p1B22" "p1B22" "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05" "p1A05"
[155] "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07" "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10"
[169] "p1A15" "p1A15" "p1A14" "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16" "p1A21" "p1A21"
[183] "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23" "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02"
[197] "p1H01" "p1H01" "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08" "p1H08" "p1H07" "p1H07"
[211] "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10" "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18"
[225] "p1H17" "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19" "p1H24" "p1H24" "p1H23" "p1H23"
[239] "p1H22" "p1H22" "p1G03" "p1G03" "p1G02" "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11" "p1G11" "p1G10" "p1G10" "p1G15" "p1G15"
[267] "p1G14" "p1G14" "p1G13" "p1G13" "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20" "p1G20"
[281] "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22" "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01"
[295] "p1F06" "p1F06" "p1F05" "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07" "p1F12" "p1F12"
[309] "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14" "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17"
[323] "p1F16" "p1F16" "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23" "p1F23" "p1F22" "p1F22"
[337] "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01" "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09"
[351] "p1E08" "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10" "p1E15" "p1E15" "p1E14" "p1E14"
[365] "p1E13" "p1E13" "p1E18" "p1E18" "p1E17" "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02" "p1L02" "p1L01" "p1L01" "p1L06" "p1L06"
[393] "p1L05" "p1L05" "p1L04" "p1L04" "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11" "p1L11"
[407] "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13" "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16"
[421] "p1L21" "p1L21" "p1L20" "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22" "p1K03" "p1K03"
[435] "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05" "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08"
[449] "p1K07" "p1K07" "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14" "p1K14" "p1K13" "p1K13"
[463] "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16" "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24"
[477] "p1K23" "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01" "p1J06" "p1J06" "p1J05" "p1J05"
[491] "p1J04" "p1J04" "p1J09" "p1J09" "p1J08" "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17" "p1J17" "p1J16" "p1J16" "p1J21" "p1J21"
[519] "p1J20" "p1J20" "p1J19" "p1J19" "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02" "p1I02"
[533] "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04" "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07"
[547] "p1I12" "p1I12" "p1I11" "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13" "p1I18" "p1I18"
[561] "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20" "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23"
[575] "p1I22" "p1I22" "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05" "p1P05" "p1P04" "p1P04"
[589] "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07" "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15"
[603] "p1P14" "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16" "p1P21" "p1P21" "p1P20" "p1P20"
[617] "p1P19" "p1P19" "p1P24" "p1P24" "p1P23" "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08" "p1O08" "p1O07" "p1O07" "p1O12" "p1O12"
[645] "p1O11" "p1O11" "p1O10" "p1O10" "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17" "p1O17"
[659] "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19" "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22"
[673] "p1N03" "p1N03" "p1N02" "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04" "p1N09" "p1N09"
[687] "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11" "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14"
[701] "p1N13" "p1N13" "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20" "p1N20" "p1N19" "p1N19"
[715] "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22" "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06"
[729] "p1M05" "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07" "p1M12" "p1M12" "p1M11" "p1M11"
[743] "p1M10" "p1M10" "p1M15" "p1M15" "p1M14" "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23" "p1M23" "p1M22" "p1M22"
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
[39] 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[77] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
[115] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8
[153] 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
[191] 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
[229] 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[267] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
[305] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6
[343] 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
[381] 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
[419] 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[457] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14
[495] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4
[533] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
[571] 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
[609] 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
[647] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12
[685] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2
[723] 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
[761] 41 42 43 44 45 46 47 48
$plate
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1
[58] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[115] 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[172] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
[229] 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[286] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1
[343] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[400] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[457] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2
[514] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[571] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1
[628] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[685] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[742] 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
$plate.r
[1] 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8 8 8 8 12 12
[39] 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3 3 3
[77] 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10
[115] 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1 1 5 5
[153] 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13
[191] 13 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8 8 8 8
[229] 12 12 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3
[267] 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10
[305] 10 10 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1 1
[343] 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13
[381] 13 13 13 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8 8
[419] 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15
[457] 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6 10 10
[495] 10 10 10 10 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1
[533] 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9
[571] 13 13 13 13 13 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8
[609] 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15
[647] 15 15 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6
[685] 10 10 10 10 10 10 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1
[723] 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9
[761] 9 9 13 13 13 13 13 13
$plate.c
[1] 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5
[39] 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13
[77] 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21
[115] 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5
[153] 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13
[191] 17 21 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22
[229] 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6
[267] 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14
[305] 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22
[343] 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6
[381] 10 14 18 22 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15
[419] 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23
[457] 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7
[495] 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15
[533] 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23
[571] 3 7 11 15 19 23 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8
[609] 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16
[647] 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24
[685] 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8
[723] 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16
[761] 20 24 4 8 12 16 20 24
$plateposition
[1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09" "p1H13" "p1H17" "p1H21" "p1L01" "p1L05"
[15] "p1L09" "p1L13" "p1L17" "p1L21" "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09" "p2D13"
[29] "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21" "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21"
[43] "p2P01" "p2P05" "p2P09" "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21" "p1G01" "p1G05"
[57] "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09" "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13"
[71] "p1O17" "p1O21" "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09" "p2G13" "p2G17" "p2G21"
[85] "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21" "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05"
[99] "p1B09" "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21" "p1J01" "p1J05" "p1J09" "p1J13"
[113] "p1J17" "p1J21" "p1N01" "p1N05" "p1N09" "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09" "p2J13" "p2J17" "p2J21" "p2N01" "p2N05"
[141] "p2N09" "p2N13" "p2N17" "p2N21" "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09" "p1E13"
[155] "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21" "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21"
[169] "p2A01" "p2A05" "p2A09" "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21" "p2I01" "p2I05"
[183] "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09" "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14"
[197] "p1D18" "p1D22" "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10" "p1L14" "p1L18" "p1L22"
[211] "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22" "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06"
[225] "p2H10" "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22" "p2P02" "p2P06" "p2P10" "p2P14"
[239] "p2P18" "p2P22" "p1C02" "p1C06" "p1C10" "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10" "p1O14" "p1O18" "p1O22" "p2C02" "p2C06"
[267] "p2C10" "p2C14" "p2C18" "p2C22" "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10" "p2K14"
[281] "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22" "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22"
[295] "p1F02" "p1F06" "p1F10" "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22" "p1N02" "p1N06"
[309] "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10" "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14"
[323] "p2F18" "p2F22" "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10" "p2N14" "p2N18" "p2N22"
[337] "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22" "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06"
[351] "p1I10" "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22" "p2A02" "p2A06" "p2A10" "p2A14"
[365] "p2A18" "p2A22" "p2E02" "p2E06" "p2E10" "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11" "p1D15" "p1D19" "p1D23" "p1H03" "p1H07"
[393] "p1H11" "p1H15" "p1H19" "p1H23" "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11" "p1P15"
[407] "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23" "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23"
[421] "p2L03" "p2L07" "p2L11" "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23" "p1C03" "p1C07"
[435] "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11" "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15"
[449] "p1K19" "p1K23" "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11" "p2C15" "p2C19" "p2C23"
[463] "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23" "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07"
[477] "p2O11" "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23" "p1F03" "p1F07" "p1F11" "p1F15"
[491] "p1F19" "p1F23" "p1J03" "p1J07" "p1J11" "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11" "p2F15" "p2F19" "p2F23" "p2J03" "p2J07"
[519] "p2J11" "p2J15" "p2J19" "p2J23" "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11" "p1A15"
[533] "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23" "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23"
[547] "p1M03" "p1M07" "p1M11" "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23" "p2E03" "p2E07"
[561] "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11" "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15"
[575] "p2M19" "p2M23" "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12" "p1H16" "p1H20" "p1H24"
[589] "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24" "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08"
[603] "p2D12" "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24" "p2L04" "p2L08" "p2L12" "p2L16"
[617] "p2L20" "p2L24" "p2P04" "p2P08" "p2P12" "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12" "p1K16" "p1K20" "p1K24" "p1O04" "p1O08"
[645] "p1O12" "p1O16" "p1O20" "p1O24" "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12" "p2G16"
[659] "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24" "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24"
[673] "p1B04" "p1B08" "p1B12" "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24" "p1J04" "p1J08"
[687] "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12" "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16"
[701] "p2B20" "p2B24" "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12" "p2J16" "p2J20" "p2J24"
[715] "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24" "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08"
[729] "p1E12" "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24" "p1M04" "p1M08" "p1M12" "p1M16"
[743] "p1M20" "p1M24" "p2A04" "p2A08" "p2A12" "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12" "p2M16" "p2M20" "p2M24"
>
> ### merge.rglist
>
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
[,1] [,2] [,3] [,4]
a 11 11 22 22
a 12 12 23 23
b 13 13 21 21
c 14 14 24 24
$G
[,1] [,2] [,3] [,4]
a 11 11 22 22
a 12 12 23 23
b 13 13 21 21
c 14 14 24 24
> merge(RG2,RG1)
An object of class "RGList"
$R
[,1] [,2] [,3] [,4]
b 21 21 13 13
a 22 22 11 11
a 23 23 12 12
c 24 24 14 14
$G
[,1] [,2] [,3] [,4]
b 21 21 13 13
a 22 22 11 11
a 23 23 12 12
c 24 24 14 14
>
> ### background correction
>
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
[,1]
[1,] -1
[2,] 0
[3,] 1
[4,] 2
$G
[,1]
[1,] -1
[2,] 0
[3,] 1
[4,] 2
> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
$G
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
$G
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
[,1]
[1,] 4
[2,] 5
[3,] 6
[4,] 7
$G
[,1]
[1,] 4
[2,] 5
[3,] 6
[4,] 7
>
> ### loessFit
>
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residuals)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residuals)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residuals)
> data.frame(f1,f2,f3,f4,f5)
f1 f2 f3 f4 f5
0% -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25% -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50% -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75% 0.01507921 -0.008145125 -0.01857508 0.04030634 0.07839396
100% 0.21653837 0.145106033 0.19214597 0.21417361 0.51836274
> data.frame(r1,r2,r3,r4,r5)
r1 r2 r3 r4 r5
0% -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25% -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50% 0.05874864 0.04514326 0.08335198 -0.001769806 0.06785517
75% 0.56010750 0.55124530 0.57618740 0.561454370 0.65383830
100% 2.57936026 2.64549799 2.57549257 2.402324533 2.28648835
>
> ### normalizeWithinArrays
>
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
V1 V2 V3 V4
Min. :0.01626 Min. :0.01213 Min. :0.0000 Min. :0.0000
1st Qu.:0.35497 1st Qu.:0.29133 1st Qu.:0.2745 1st Qu.:0.3953
Median :0.71793 Median :0.70294 Median :0.6339 Median :0.8223
Mean :0.90184 Mean :1.00122 Mean :0.9454 Mean :1.1324
3rd Qu.:1.16891 3rd Qu.:1.33139 3rd Qu.:1.4059 3rd Qu.:1.4221
Max. :4.56267 Max. :6.37947 Max. :5.0486 Max. :6.6295
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
V1 V2 V3 V4
Min. :0.01701 Min. :0.01255 Min. :0.0000 Min. :0.0000
1st Qu.:0.35423 1st Qu.:0.29118 1st Qu.:0.2745 1st Qu.:0.3953
Median :0.71719 Median :0.70280 Median :0.6339 Median :0.8223
Mean :0.90118 Mean :1.00110 Mean :0.9454 Mean :1.1324
3rd Qu.:1.16817 3rd Qu.:1.33124 3rd Qu.:1.4059 3rd Qu.:1.4221
Max. :4.56193 Max. :6.37932 Max. :5.0486 Max. :6.6295
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
V1 V2
Min. :-5.8801 Min. :-5.66985
1st Qu.:-1.1850 1st Qu.:-1.57014
Median :-0.2164 Median : 0.04823
Mean : 0.0349 Mean :-0.05481
3rd Qu.: 1.4964 3rd Qu.: 1.45113
Max. : 7.0751 Max. : 6.19744
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
>
> ### normalizeBetweenArrays
>
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
[,1] [,2]
[1,] -1.1669590 4.5558123
[2,] 0.8969743 0.3296544
[3,] 2.8245754 1.4249960
[4,] -1.8532747 0.4804851
[5,] 1.9159023 -5.5087631
> MA$A[1:5,]
[,1] [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200 0.2071043
[4,] 0.09281027 -1.3880965
[5,] 0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
[,1] [,2]
[1,] -1.1669590 4.5558123
[2,] 0.8969743 0.3296544
[3,] 2.8245754 1.4249960
[4,] -1.8532747 0.4804851
[5,] 1.9159023 -5.5087631
> MA$A[1:5,]
[,1] [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200 0.2071043
[4,] 0.09281027 -1.3880965
[5,] 0.22385828 -3.0855818
>
> ### unwrapdups
>
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
[,1] [,2]
[1,] 1 7
[2,] 2 8
[3,] 3 9
[4,] 4 10
[5,] 5 11
[6,] 6 12
> unwrapdups(M,ndups=2)
[,1] [,2] [,3] [,4]
[1,] 1 2 7 8
[2,] 3 4 9 10
[3,] 5 6 11 12
> unwrapdups(M,ndups=3)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 2 3 7 8 9
[2,] 4 5 6 10 11 12
> unwrapdups(M,ndups=2,spacing=3)
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
>
> ### trigammaInverse
>
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06 NA 4.961687e-01 1.000001e-03
>
> ### lmFit, eBayes, topTable
>
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
First3 Last3 Last3.First3 AveExpr F P.Value adj.P.Val
A 1.77602021 0.06025114 -1.71576906 0.918135675 50.91471061 7.727200e-23 7.727200e-22
D -0.05454069 0.39127869 0.44581938 0.168369004 2.51638838 8.075072e-02 3.758388e-01
F -0.16249607 -0.33009728 -0.16760121 -0.246296671 2.18256779 1.127516e-01 3.758388e-01
G 0.30852468 -0.06873462 -0.37725930 0.119895035 1.61088775 1.997102e-01 4.992756e-01
H -0.16942269 0.20578118 0.37520387 0.018179245 1.14554368 3.180510e-01 6.361019e-01
J 0.21417623 0.07074940 -0.14342683 0.142462814 0.82029274 4.403027e-01 7.338379e-01
C -0.12236781 0.15095948 0.27332729 0.014295836 0.60885003 5.439761e-01 7.382414e-01
B -0.11982833 0.13529287 0.25512120 0.007732271 0.52662792 5.905931e-01 7.382414e-01
E 0.01897934 0.10434934 0.08536999 0.061664340 0.18136849 8.341279e-01 9.268088e-01
I -0.04720963 0.03996397 0.08717360 -0.003622829 0.06168476 9.401792e-01 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
logFC AveExpr t P.Value adj.P.Val B
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
logFC AveExpr t P.Value adj.P.Val B
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort.by="logFC",resort.by="t")
logFC AveExpr t P.Value adj.P.Val B
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
logFC AveExpr t P.Value adj.P.Val B
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5,sort.by="none")
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> contrasts.fit(fit[1:3,],contrast.matrix[,0])
An object of class "MArrayLM"
$coefficients
A
B
C
$rank
[1] 2
$assign
NULL
$qr
$qr
First3Arrays Last3Arrays
[1,] -1.7320508 0.0000000
[2,] 0.5773503 -1.7320508
[3,] 0.5773503 0.0000000
[4,] 0.0000000 0.5773503
[5,] 0.0000000 0.5773503
[6,] 0.0000000 0.5773503
$qraux
[1] 1.57735 1.00000
$pivot
[1] 1 2
$tol
[1] 1e-07
$rank
[1] 2
$df.residual
[1] 4 4 4
$sigma
A B C
0.3299787 0.3323336 0.2315815
$cov.coefficients
<0 x 0 matrix>
$stdev.unscaled
A
B
C
$pivot
[1] 1 2
$Amean
A B C
0.918135675 0.007732271 0.014295836
$method
[1] "ls"
$design
First3Arrays Last3Arrays
[1,] 1 0
[2,] 1 0
[3,] 1 0
[4,] 0 1
[5,] 0 1
[6,] 0 1
$contrasts
[1,]
[2,]
> fit$coefficients[1,1] <- NA
> contrasts.fit(fit[1:3,],contrast.matrix)$coefficients
First3 Last3 Last3-First3
A NA 0.06025114 NA
B -0.1198283 0.13529287 0.2551212
C -0.1223678 0.15095948 0.2733273
>
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)
Null Two Three
5 3 2
>
> ### marray object
>
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> # data(swirl)
> # snorm = maNorm(swirl)
> # fit <- lmFit(snorm, design = c(1,-1,-1,1))
> # fit <- eBayes(fit)
> # topTable(fit,resort.by="AveExpr")
> #}
>
> ### duplicateCorrelation
>
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130 0.4088967 -0.1964978 -0.6093769 0.3730118
>
> ### gls.series
>
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
First3Arrays Last3Arrays
[1,] 0.82809594 0.09777201
[2,] -0.08845425 0.27111909
[3,] -0.07175836 -0.11287397
[4,] 0.06955100 0.06852328
[5,] 0.08348330 0.05535668
> fit$stdev.unscaled
First3Arrays Last3Arrays
[1,] 0.3888215 0.3888215
[2,] 0.3888215 0.3888215
[3,] 0.3888215 0.3888215
[4,] 0.3888215 0.3888215
[5,] 0.3888215 0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
>
> ### mrlm
>
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
'rlm' failed to converge in 20 steps
> fit$coefficients
First3Arrays Last3Arrays
A 1.75138894 0.06025114
B -0.11982833 0.10322039
C -0.09302502 0.15095948
D -0.05454069 0.33700045
E 0.07927938 0.10434934
F -0.16249607 -0.34010852
G 0.30852468 -0.06873462
H -0.16942269 0.24392984
I -0.04720963 0.03996397
J 0.21417623 -0.05679272
> fit$stdev.unscaled
First3Arrays Last3Arrays
A 0.5933418 0.5773503
B 0.5773503 0.6096497
C 0.6017444 0.5773503
D 0.5773503 0.6266021
E 0.6307703 0.5773503
F 0.5773503 0.5846707
G 0.5773503 0.5773503
H 0.5773503 0.6544564
I 0.5773503 0.5773503
J 0.5773503 0.6689776
> fit$sigma
[1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945 0.2267556 0.3537469 0.2172409
> fit$df.residual
[1] 4 4 4 4 4 4 4 4 4 4
>
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
alpha-beta mu+alpha mu+beta
[1,] 3.537333 1.677465 -1.859868
[2,] 4.355578 2.372554 -1.983024
[3,] 3.197645 1.053584 -2.144061
[4,] 2.697734 1.611443 -1.086291
[5,] 3.502304 2.051995 -1.450309
$stdev.unscaled
alpha-beta mu+alpha mu+beta
[1,] 0.8164966 0.5773503 0.5773503
[2,] 0.8164966 0.5773503 0.5773503
[3,] 0.8164966 0.5773503 0.5773503
[4,] 0.8164966 0.5773503 0.5773503
[5,] 1.1547005 0.8368633 0.8368633
$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509
$df.residual
[1] 6 6 6 6 4
$cov.coefficients
alpha-beta mu+alpha mu+beta
alpha-beta 0.6666667 3.333333e-01 -3.333333e-01
mu+alpha 0.3333333 3.333333e-01 1.110223e-16
mu+beta -0.3333333 1.110223e-16 3.333333e-01
$pivot
[1] 1 2 3
$rank
[1] 3
$Amean
[1] 0.2034961 0.1954604 -0.2863347 0.1188659 0.1784593
$method
[1] "ls"
$design
mu alpha beta
1 1 0 0
2 1 0 0
3 1 0 0
4 1 1 0
5 1 1 0
6 1 1 0
7 1 0 1
8 1 0 1
9 1 0 1
$contrasts
alpha-beta mu+alpha mu+beta
mu 0 1 1
alpha 1 1 0
beta -1 0 1
$df.prior
[1] 9.306153
$s2.prior
[1] 0.923179
$var.prior
[1] 17.33142 17.33142 12.26855
$proportion
[1] 0.01
$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980
$t
alpha-beta mu+alpha mu+beta
[1,] 3.847656 2.580411 -2.860996
[2,] 6.637308 5.113018 -4.273553
[3,] 3.692066 1.720376 -3.500994
[4,] 3.464003 2.926234 -1.972606
[5,] 3.175181 2.566881 -1.814221
$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615
$p.value
alpha-beta mu+alpha mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630
$lods
alpha-beta mu+alpha mu+beta
[1,] -1.013417 -3.702133 -3.0332393
[2,] 3.981496 1.283349 -0.2615911
[3,] -1.315036 -5.168621 -1.7864101
[4,] -1.757103 -3.043209 -4.6191869
[5,] -2.257358 -3.478267 -4.5683738
$F
[1] 7.421911 22.203107 7.608327 6.227010 5.060579
$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02
>
> ### uniquegenelist
>
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
>
> ### classifyTests
>
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] -1 -1 1
[4,] 0 0 0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 1 0
[3,] 0 0 0
[4,] 0 0 0
>
> ### avereps
>
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
S1 S2 S3
b -0.2353018 0.5220094 0.2302895
a -0.4347701 0.6453498 -0.6758914
c 0.3482980 -0.4820695 -0.3841313
>
> ### roast
>
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
Active.Prop P.Value
Down 0 0.997999500
Up 1 0.002250563
UpOrDown 1 0.004500000
Mixed 1 0.004500000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
Active.Prop P.Value
Down 0 0.998749687
Up 1 0.001500375
UpOrDown 1 0.003000000
Mixed 1 0.003000000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
Active.Prop P.Value
Down 0 0.996999250
Up 1 0.003250813
UpOrDown 1 0.006500000
Mixed 1 0.006500000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.0055 0.0105 0.0055 0.0105
set2 5 0 0 Up 0.2025 0.2025 0.4715 0.4715
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.0050 0.0095 0.005 0.0095
set2 5 0 0 Up 0.6845 0.6845 0.642 0.6420
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1.0 Up 0.0030 0.0055 0.003 0.0055
set2 5 0 0.2 Down 0.9615 0.9615 0.496 0.4960
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1.0 Up 0.0025 0.0045 0.0025 0.0045
set2 5 0 0.2 Down 0.8930 0.8930 0.4380 0.4380
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 Up 0.001568924 0.003137848 0.0001156464 0.0002312929
set2 5 Down 0.932105219 0.932105219 0.4315499569 0.4315499569
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes Direction PValue PValue.Mixed
set1 5 Up 0.001568924 0.0001156464
>
> ### camera
>
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue
set1 5 -0.2481655 Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue FDR
set1 5 -0.2481655 Up 0.0009047749 0.00180955
set2 5 0.1719094 Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
NGenes Direction PValue
set1 5 Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
NGenes Direction PValue FDR
set1 5 Up 7.334400e-12 1.466880e-11
set2 5 Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
NGenes Direction PValue
set1 5 Up 7.3344e-12
>
> ### with EList arg
>
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
Active.Prop P.Value
Down 0 0.996999250
Up 1 0.003250813
UpOrDown 1 0.006500000
Mixed 1 0.006500000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue
set1 5 -0.2481655 Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
NGenes Direction PValue
set1 5 Up 7.3344e-12
>
> ### eBayes with trend
>
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene2 3.729512 1.73488969 4.865697 0.0004854886 0.02902331 0.1596831
Gene3 3.488703 1.03931081 4.754954 0.0005804663 0.02902331 -0.0144071
Gene4 2.696676 1.74060725 3.356468 0.0063282637 0.21094212 -2.3434702
Gene1 2.391846 1.72305203 3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5 2.387967 1.63066783 2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39 1.366141 -0.27360750 2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074 1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50 1.034777 0.01608433 2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
Gene1 Gene2 Gene3 Gene4 Gene5 Gene6 Gene7 Gene8 Gene9 Gene10 Gene11 Gene12
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 0.2985145 0.2832520 0.3232434 0.3279710
Gene13 Gene14 Gene15 Gene16 Gene17 Gene18 Gene19 Gene20 Gene21 Gene22 Gene23 Gene24
0.2816081 0.2943502 0.3127994 0.2894802 0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541
Gene25 Gene26 Gene27 Gene28 Gene29 Gene30 Gene31 Gene32 Gene33 Gene34 Gene35 Gene36
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 0.2988244 0.3450090 0.3048596 0.3089086
Gene37 Gene38 Gene39 Gene40 Gene41 Gene42 Gene43 Gene44 Gene45 Gene46 Gene47 Gene48
0.3104534 0.4551549 0.3220008 0.2813286 0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003
Gene49 Gene50 Gene51 Gene52 Gene53 Gene54 Gene55 Gene56 Gene57 Gene58 Gene59 Gene60
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 0.2955317 0.2815907 0.2919420 0.2849675
Gene61 Gene62 Gene63 Gene64 Gene65 Gene66 Gene67 Gene68 Gene69 Gene70 Gene71 Gene72
0.3540805 0.3491713 0.2975019 0.2939325 0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850
Gene73 Gene74 Gene75 Gene76 Gene77 Gene78 Gene79 Gene80 Gene81 Gene82 Gene83 Gene84
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 0.3413700 0.2946268 0.3029565 0.2920284
Gene85 Gene86 Gene87 Gene88 Gene89 Gene90 Gene91 Gene92 Gene93 Gene94 Gene95 Gene96
0.2926205 0.2818046 0.3425116 0.2882936 0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784
Gene97 Gene98 Gene99 Gene100
0.2846345 0.2819651 0.3137551 0.2856081
> summary(fit$s2.post)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2335 0.2603 0.2997 0.3375 0.3655 0.7812
>
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene3 3.488703 1.03931081 4.604490 0.0007644061 0.07644061 -0.2333915
Gene2 3.729512 1.73488969 4.158038 0.0016033158 0.08016579 -0.9438583
Gene4 2.696676 1.74060725 2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5 2.387967 1.63066783 2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39 1.366141 -0.27360750 2.440729 0.0328318388 0.46902627 -3.8172597
Gene1 2.638272 1.47993643 2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074 1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50 1.034777 0.01608433 2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
[1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052 0.3074225 0.2985517 0.2786374 0.3267121
[12] 0.3316013 0.2766404 0.2932679 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412 0.2761282
[23] 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389
[34] 0.3483845 0.3062977 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311 0.2776653 0.3201314
[45] 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306
[56] 0.2870975 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804 0.2927300 0.2987764 0.3301406
[67] 0.3437962 0.3430762 0.2761729 0.2763094 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
[78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616 0.2910320 0.2769211 0.3459946 0.2859057
[89] 0.2935193 0.3097398 0.2865663 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484 0.3164981
[100] 0.2817725
> summary(fit$s2.post)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2296 0.2581 0.3003 0.3453 0.3652 0.9158
>
> ### eBayes with robust
>
> fitr <- lmFit(y,design)
> fitr <- eBayes(fitr,robust=TRUE)
> summary(fitr$df.prior)
Min. 1st Qu. Median Mean 3rd Qu. Max.
6.717 9.244 9.244 9.194 9.244 9.244
> topTable(fitr,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene2 3.729512 1.73488969 7.108463 1.752774e-05 0.001752774 3.3517310
Gene3 3.488703 1.03931081 5.041209 3.526138e-04 0.017630688 0.4056329
Gene4 2.696676 1.74060725 4.697690 6.150508e-04 0.020501693 -0.1463315
Gene5 2.387967 1.63066783 3.451807 5.245019e-03 0.131125480 -2.2678836
Gene1 2.638272 1.47993643 3.317593 8.651142e-03 0.173022847 -2.4400000
Gene33 -1.492317 -0.07525287 -2.716431 1.970991e-02 0.297950865 -3.5553166
Gene95 -1.907074 1.26297763 -2.685067 2.085656e-02 0.297950865 -3.6094982
Gene80 -1.839760 -0.32802306 -2.535926 2.727440e-02 0.340929958 -3.8653107
Gene39 1.366141 -0.27360750 2.469570 3.071854e-02 0.341317083 -3.9779817
Gene50 1.034777 0.01608433 1.973040 7.357960e-02 0.632875126 -4.7877548
> fitr <- eBayes(fitr,trend=TRUE,robust=TRUE)
> summary(fitr$df.prior)
Min. 1st Qu. Median Mean 3rd Qu. Max.
7.809 8.972 8.972 8.949 8.972 8.972
> topTable(fitr,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene2 3.729512 1.73488969 4.754160 0.0005999064 0.05999064 -0.0218247
Gene3 3.488703 1.03931081 3.761219 0.0031618743 0.15809372 -1.6338257
Gene4 2.696676 1.74060725 3.292262 0.0071993347 0.23997782 -2.4295326
Gene33 -1.492317 -0.07525287 -3.063180 0.0108203134 0.27050784 -2.8211394
Gene50 1.034777 0.01608433 2.645717 0.0228036320 0.38815282 -3.5304767
Gene5 2.387967 1.63066783 2.633901 0.0232891695 0.38815282 -3.5503445
Gene1 2.638272 1.47993643 2.204116 0.0550613420 0.58959402 -4.0334169
Gene80 -1.839760 -0.32802306 -2.332729 0.0397331916 0.56761702 -4.0496640
Gene39 1.366141 -0.27360750 2.210665 0.0492211477 0.58959402 -4.2469578
Gene95 -1.907074 1.26297763 -2.106861 0.0589594023 0.58959402 -4.4117140
>
> ### voom
>
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E" "weights" "design" "targets"
> summary(v$E)
V1 V2 V3 V4
Min. :12.38 Min. :12.32 Min. :12.17 Min. :12.08
1st Qu.:13.11 1st Qu.:13.05 1st Qu.:13.11 1st Qu.:13.03
Median :13.34 Median :13.28 Median :13.35 Median :13.35
Mean :13.29 Mean :13.29 Mean :13.28 Mean :13.28
3rd Qu.:13.48 3rd Qu.:13.54 3rd Qu.:13.48 3rd Qu.:13.50
Max. :14.01 Max. :13.95 Max. :14.03 Max. :14.05
> summary(v$weights)
V1 V2 V3 V4
Min. : 7.729 Min. : 7.729 Min. : 7.729 Min. : 7.729
1st Qu.:13.859 1st Qu.:15.067 1st Qu.:14.254 1st Qu.:13.592
Median :15.913 Median :16.621 Median :16.081 Median :16.028
Mean :16.773 Mean :18.525 Mean :18.472 Mean :17.112
3rd Qu.:18.214 3rd Qu.:20.002 3rd Qu.:18.475 3rd Qu.:18.398
Max. :34.331 Max. :34.331 Max. :34.331 Max. :34.331
>
> ### goana
>
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,number=10,truncate.term=30)
Term Ont N Up Down P.Up P.Down
GO:0032502 developmental process BP 24 4 7 0.871310423 0.001532528
GO:0070062 extracellular exosome CC 8 0 4 1.000000000 0.003047199
GO:0065010 extracellular membrane-boun... CC 8 0 4 1.000000000 0.003047199
GO:0043230 extracellular organelle CC 8 0 4 1.000000000 0.003047199
GO:1903561 extracellular vesicle CC 8 0 4 1.000000000 0.003047199
GO:0006915 apoptotic process BP 5 4 1 0.009503355 0.416247633
GO:0008219 cell death BP 5 4 1 0.009503355 0.416247633
GO:0012501 programmed cell death BP 5 4 1 0.009503355 0.416247633
GO:0042981 regulation of apoptotic pro... BP 5 4 1 0.009503355 0.416247633
GO:0043067 regulation of programmed ce... BP 5 4 1 0.009503355 0.416247633
> topGO(go,number=10,truncate.term=30,sort="down")
Term Ont N Up Down P.Up P.Down
GO:0032502 developmental process BP 24 4 7 0.8713104 0.001532528
GO:0070062 extracellular exosome CC 8 0 4 1.0000000 0.003047199
GO:0065010 extracellular membrane-boun... CC 8 0 4 1.0000000 0.003047199
GO:0043230 extracellular organelle CC 8 0 4 1.0000000 0.003047199
GO:1903561 extracellular vesicle CC 8 0 4 1.0000000 0.003047199
GO:0032501 multicellular organismal pr... BP 33 7 7 0.7048432 0.013750585
GO:0016485 protein processing BP 7 1 3 0.8497705 0.020760307
GO:0031982 vesicle CC 20 3 5 0.8982815 0.025464546
GO:0009887 animal organ morphogenesis BP 3 0 2 1.0000000 0.025788497
GO:0043009 chordate embryonic developm... BP 3 0 2 1.0000000 0.025788497
>
> proc.time()
user system elapsed
10.359 0.902 12.414
limma.Rcheck/tests/limma-Tests.Rout.save
R version 4.3.1 (2023-06-16 ucrt) -- "Beagle Scouts"
Copyright (C) 2023 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(limma)
> options(warnPartialMatchArgs=TRUE,warnPartialMatchAttr=TRUE,warnPartialMatchDollar=TRUE,width=120)
>
> set.seed(0); u <- runif(100)
>
> ### strsplit2
>
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
[,1] [,2] [,3]
[1,] "ab" "cd" "efg"
[2,] "abc" "def" ""
[3,] "z" "" ""
[4,] "" "" ""
>
> ### removeext
>
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1" "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"
>
> ### printorder
>
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
[1] 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15 14 13 24 23 22 21 20 19 30 29 28 27
[29] 26 25 36 35 34 33 32 31 42 41 40 39 38 37 48 47 46 45 44 43 6 5 4 3 2 1 12 11
[57] 10 9 8 7 18 17 16 15 14 13 24 23 22 21 20 19 30 29 28 27 26 25 36 35 34 33 32 31
[85] 42 41 40 39 38 37 48 47 46 45 44 43 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15
[113] 14 13 24 23 22 21 20 19 30 29 28 27 26 25 36 35 34 33 32 31 42 41 40 39 38 37 48 47
[141] 46 45 44 43 6 5 4 3 2 1 12 11 10 9 8 7 18 17 16 15 14 13 24 23 22 21 20 19
[169] 30 29 28 27 26 25 36 35 34 33 32 31 42 41 40 39 38 37 48 47 46 45 44 43 54 53 52 51
[197] 50 49 60 59 58 57 56 55 66 65 64 63 62 61 72 71 70 69 68 67 78 77 76 75 74 73 84 83
[225] 82 81 80 79 90 89 88 87 86 85 96 95 94 93 92 91 54 53 52 51 50 49 60 59 58 57 56 55
[253] 66 65 64 63 62 61 72 71 70 69 68 67 78 77 76 75 74 73 84 83 82 81 80 79 90 89 88 87
[281] 86 85 96 95 94 93 92 91 54 53 52 51 50 49 60 59 58 57 56 55 66 65 64 63 62 61 72 71
[309] 70 69 68 67 78 77 76 75 74 73 84 83 82 81 80 79 90 89 88 87 86 85 96 95 94 93 92 91
[337] 54 53 52 51 50 49 60 59 58 57 56 55 66 65 64 63 62 61 72 71 70 69 68 67 78 77 76 75
[365] 74 73 84 83 82 81 80 79 90 89 88 87 86 85 96 95 94 93 92 91 102 101 100 99 98 97 108 107
[393] 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[421] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100 99 98 97 108 107 106 105 104 103 114 113 112 111
[449] 110 109 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133 144 143
[477] 142 141 140 139 102 101 100 99 98 97 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100 99
[533] 98 97 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121 132 131
[561] 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139 150 149 148 147 146 145 156 155 154 153 152 151
[589] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183
[617] 182 181 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157 168 167
[645] 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[673] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171
[701] 170 169 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145 156 155
[729] 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187
$plate
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[58] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[115] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[172] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[229] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[286] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[343] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[400] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[457] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[514] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[571] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[628] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[685] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[742] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
$plate.r
[1] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
[39] 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[77] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[115] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[153] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[191] 1 1 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8
[229] 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7
[267] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[305] 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5
[343] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5
[381] 5 5 5 5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[419] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[457] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[495] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 9 9 9 9
[533] 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
[571] 9 9 9 9 9 9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[609] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[647] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14 14 14 14 14 14 14 14 14 14
[685] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13
[723] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[761] 13 13 13 13 13 13 13 13
$plate.c
[1] 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21
[39] 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14
[77] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7
[115] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6
[153] 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23
[191] 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16
[229] 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15
[267] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8
[305] 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1
[343] 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24
[381] 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17
[419] 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10
[457] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9
[495] 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2
[533] 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19
[571] 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18
[609] 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11
[647] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3 2 2 1 1 6 6 5 5 4 4
[685] 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22 3 3
[723] 2 2 1 1 6 6 5 5 4 4 9 9 8 8 7 7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20
[761] 19 19 24 24 23 23 22 22
$plateposition
[1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05" "p1D05" "p1D04" "p1D04" "p1D09" "p1D09"
[15] "p1D08" "p1D08" "p1D07" "p1D07" "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14" "p1D14"
[29] "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16" "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19"
[43] "p1D24" "p1D24" "p1D23" "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01" "p1C06" "p1C06"
[57] "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08" "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11"
[71] "p1C10" "p1C10" "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17" "p1C17" "p1C16" "p1C16"
[85] "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19" "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03"
[99] "p1B02" "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04" "p1B09" "p1B09" "p1B08" "p1B08"
[113] "p1B07" "p1B07" "p1B12" "p1B12" "p1B11" "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20" "p1B20" "p1B19" "p1B19" "p1B24" "p1B24"
[141] "p1B23" "p1B23" "p1B22" "p1B22" "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05" "p1A05"
[155] "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07" "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10"
[169] "p1A15" "p1A15" "p1A14" "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16" "p1A21" "p1A21"
[183] "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23" "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02"
[197] "p1H01" "p1H01" "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08" "p1H08" "p1H07" "p1H07"
[211] "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10" "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18"
[225] "p1H17" "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19" "p1H24" "p1H24" "p1H23" "p1H23"
[239] "p1H22" "p1H22" "p1G03" "p1G03" "p1G02" "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11" "p1G11" "p1G10" "p1G10" "p1G15" "p1G15"
[267] "p1G14" "p1G14" "p1G13" "p1G13" "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20" "p1G20"
[281] "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22" "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01"
[295] "p1F06" "p1F06" "p1F05" "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07" "p1F12" "p1F12"
[309] "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14" "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17"
[323] "p1F16" "p1F16" "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23" "p1F23" "p1F22" "p1F22"
[337] "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01" "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09"
[351] "p1E08" "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10" "p1E15" "p1E15" "p1E14" "p1E14"
[365] "p1E13" "p1E13" "p1E18" "p1E18" "p1E17" "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02" "p1L02" "p1L01" "p1L01" "p1L06" "p1L06"
[393] "p1L05" "p1L05" "p1L04" "p1L04" "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11" "p1L11"
[407] "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13" "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16"
[421] "p1L21" "p1L21" "p1L20" "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22" "p1K03" "p1K03"
[435] "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05" "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08"
[449] "p1K07" "p1K07" "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14" "p1K14" "p1K13" "p1K13"
[463] "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16" "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24"
[477] "p1K23" "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01" "p1J06" "p1J06" "p1J05" "p1J05"
[491] "p1J04" "p1J04" "p1J09" "p1J09" "p1J08" "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17" "p1J17" "p1J16" "p1J16" "p1J21" "p1J21"
[519] "p1J20" "p1J20" "p1J19" "p1J19" "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02" "p1I02"
[533] "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04" "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07"
[547] "p1I12" "p1I12" "p1I11" "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13" "p1I18" "p1I18"
[561] "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20" "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23"
[575] "p1I22" "p1I22" "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05" "p1P05" "p1P04" "p1P04"
[589] "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07" "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15"
[603] "p1P14" "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16" "p1P21" "p1P21" "p1P20" "p1P20"
[617] "p1P19" "p1P19" "p1P24" "p1P24" "p1P23" "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08" "p1O08" "p1O07" "p1O07" "p1O12" "p1O12"
[645] "p1O11" "p1O11" "p1O10" "p1O10" "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17" "p1O17"
[659] "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19" "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22"
[673] "p1N03" "p1N03" "p1N02" "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04" "p1N09" "p1N09"
[687] "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11" "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14"
[701] "p1N13" "p1N13" "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20" "p1N20" "p1N19" "p1N19"
[715] "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22" "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06"
[729] "p1M05" "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07" "p1M12" "p1M12" "p1M11" "p1M11"
[743] "p1M10" "p1M10" "p1M15" "p1M15" "p1M14" "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23" "p1M23" "p1M22" "p1M22"
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
[39] 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[77] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
[115] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8
[153] 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46
[191] 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
[229] 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[267] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
[305] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6
[343] 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
[381] 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
[419] 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[457] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14
[495] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4
[533] 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
[571] 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
[609] 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
[647] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2 3 4 5 6 7 8 9 10 11 12
[685] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 1 2
[723] 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
[761] 41 42 43 44 45 46 47 48
$plate
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1
[58] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[115] 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[172] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2
[229] 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[286] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1
[343] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[400] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[457] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2
[514] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[571] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1
[628] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[685] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[742] 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
$plate.r
[1] 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8 8 8 8 12 12
[39] 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3 3 3
[77] 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10
[115] 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1 1 5 5
[153] 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13
[191] 13 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8 8 8 8
[229] 12 12 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 3 3
[267] 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10
[305] 10 10 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1 1 1
[343] 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13
[381] 13 13 13 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8 8 8
[419] 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15
[457] 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6 10 10
[495] 10 10 10 10 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1 1 1
[533] 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9
[571] 13 13 13 13 13 13 4 4 4 4 4 4 8 8 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 4 4 4 4 4 4 8 8
[609] 8 8 8 8 12 12 12 12 12 12 16 16 16 16 16 16 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15
[647] 15 15 3 3 3 3 3 3 7 7 7 7 7 7 11 11 11 11 11 11 15 15 15 15 15 15 2 2 2 2 2 2 6 6 6 6 6 6
[685] 10 10 10 10 10 10 14 14 14 14 14 14 2 2 2 2 2 2 6 6 6 6 6 6 10 10 10 10 10 10 14 14 14 14 14 14 1 1
[723] 1 1 1 1 5 5 5 5 5 5 9 9 9 9 9 9 13 13 13 13 13 13 1 1 1 1 1 1 5 5 5 5 5 5 9 9 9 9
[761] 9 9 13 13 13 13 13 13
$plate.c
[1] 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5
[39] 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13
[77] 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21
[115] 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5
[153] 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13
[191] 17 21 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22
[229] 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6
[267] 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14
[305] 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22
[343] 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6 10 14 18 22 2 6
[381] 10 14 18 22 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15
[419] 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23
[457] 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7
[495] 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15
[533] 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23 3 7 11 15 19 23
[571] 3 7 11 15 19 23 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8
[609] 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16
[647] 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24
[685] 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8
[723] 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16 20 24 4 8 12 16
[761] 20 24 4 8 12 16 20 24
$plateposition
[1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09" "p1H13" "p1H17" "p1H21" "p1L01" "p1L05"
[15] "p1L09" "p1L13" "p1L17" "p1L21" "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09" "p2D13"
[29] "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21" "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21"
[43] "p2P01" "p2P05" "p2P09" "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21" "p1G01" "p1G05"
[57] "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09" "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13"
[71] "p1O17" "p1O21" "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09" "p2G13" "p2G17" "p2G21"
[85] "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21" "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05"
[99] "p1B09" "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21" "p1J01" "p1J05" "p1J09" "p1J13"
[113] "p1J17" "p1J21" "p1N01" "p1N05" "p1N09" "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09" "p2J13" "p2J17" "p2J21" "p2N01" "p2N05"
[141] "p2N09" "p2N13" "p2N17" "p2N21" "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09" "p1E13"
[155] "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21" "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21"
[169] "p2A01" "p2A05" "p2A09" "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21" "p2I01" "p2I05"
[183] "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09" "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14"
[197] "p1D18" "p1D22" "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10" "p1L14" "p1L18" "p1L22"
[211] "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22" "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06"
[225] "p2H10" "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22" "p2P02" "p2P06" "p2P10" "p2P14"
[239] "p2P18" "p2P22" "p1C02" "p1C06" "p1C10" "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10" "p1O14" "p1O18" "p1O22" "p2C02" "p2C06"
[267] "p2C10" "p2C14" "p2C18" "p2C22" "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10" "p2K14"
[281] "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22" "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22"
[295] "p1F02" "p1F06" "p1F10" "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22" "p1N02" "p1N06"
[309] "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10" "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14"
[323] "p2F18" "p2F22" "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10" "p2N14" "p2N18" "p2N22"
[337] "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22" "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06"
[351] "p1I10" "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22" "p2A02" "p2A06" "p2A10" "p2A14"
[365] "p2A18" "p2A22" "p2E02" "p2E06" "p2E10" "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11" "p1D15" "p1D19" "p1D23" "p1H03" "p1H07"
[393] "p1H11" "p1H15" "p1H19" "p1H23" "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11" "p1P15"
[407] "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23" "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23"
[421] "p2L03" "p2L07" "p2L11" "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23" "p1C03" "p1C07"
[435] "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11" "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15"
[449] "p1K19" "p1K23" "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11" "p2C15" "p2C19" "p2C23"
[463] "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23" "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07"
[477] "p2O11" "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23" "p1F03" "p1F07" "p1F11" "p1F15"
[491] "p1F19" "p1F23" "p1J03" "p1J07" "p1J11" "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11" "p2F15" "p2F19" "p2F23" "p2J03" "p2J07"
[519] "p2J11" "p2J15" "p2J19" "p2J23" "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11" "p1A15"
[533] "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23" "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23"
[547] "p1M03" "p1M07" "p1M11" "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23" "p2E03" "p2E07"
[561] "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11" "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15"
[575] "p2M19" "p2M23" "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12" "p1H16" "p1H20" "p1H24"
[589] "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24" "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08"
[603] "p2D12" "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24" "p2L04" "p2L08" "p2L12" "p2L16"
[617] "p2L20" "p2L24" "p2P04" "p2P08" "p2P12" "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12" "p1K16" "p1K20" "p1K24" "p1O04" "p1O08"
[645] "p1O12" "p1O16" "p1O20" "p1O24" "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12" "p2G16"
[659] "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24" "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24"
[673] "p1B04" "p1B08" "p1B12" "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24" "p1J04" "p1J08"
[687] "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12" "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16"
[701] "p2B20" "p2B24" "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12" "p2J16" "p2J20" "p2J24"
[715] "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24" "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08"
[729] "p1E12" "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24" "p1M04" "p1M08" "p1M12" "p1M16"
[743] "p1M20" "p1M24" "p2A04" "p2A08" "p2A12" "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12" "p2M16" "p2M20" "p2M24"
>
> ### merge.rglist
>
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
[,1] [,2] [,3] [,4]
a 11 11 22 22
a 12 12 23 23
b 13 13 21 21
c 14 14 24 24
$G
[,1] [,2] [,3] [,4]
a 11 11 22 22
a 12 12 23 23
b 13 13 21 21
c 14 14 24 24
> merge(RG2,RG1)
An object of class "RGList"
$R
[,1] [,2] [,3] [,4]
b 21 21 13 13
a 22 22 11 11
a 23 23 12 12
c 24 24 14 14
$G
[,1] [,2] [,3] [,4]
b 21 21 13 13
a 22 22 11 11
a 23 23 12 12
c 24 24 14 14
>
> ### background correction
>
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
[,1]
[1,] -1
[2,] 0
[3,] 1
[4,] 2
$G
[,1]
[1,] -1
[2,] 0
[3,] 1
[4,] 2
> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
$G
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
$G
[,1]
[1,] 0.5
[2,] 0.5
[3,] 1.0
[4,] 2.0
> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
[,1]
[1,] 4
[2,] 5
[3,] 6
[4,] 7
$G
[,1]
[1,] 4
[2,] 5
[3,] 6
[4,] 7
>
> ### loessFit
>
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residuals)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residuals)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residuals)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residuals)
> data.frame(f1,f2,f3,f4,f5)
f1 f2 f3 f4 f5
0% -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25% -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50% -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75% 0.01507921 -0.008145125 -0.01857508 0.04030634 0.07839396
100% 0.21653837 0.145106033 0.19214597 0.21417361 0.51836274
> data.frame(r1,r2,r3,r4,r5)
r1 r2 r3 r4 r5
0% -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25% -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50% 0.05874864 0.04514326 0.08335198 -0.001769806 0.06785517
75% 0.56010750 0.55124530 0.57618740 0.561454370 0.65383830
100% 2.57936026 2.64549799 2.57549257 2.402324533 2.28648835
>
> ### normalizeWithinArrays
>
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
V1 V2 V3 V4
Min. :0.01626 Min. :0.01213 Min. :0.0000 Min. :0.0000
1st Qu.:0.35497 1st Qu.:0.29133 1st Qu.:0.2745 1st Qu.:0.3953
Median :0.71793 Median :0.70294 Median :0.6339 Median :0.8223
Mean :0.90184 Mean :1.00122 Mean :0.9454 Mean :1.1324
3rd Qu.:1.16891 3rd Qu.:1.33139 3rd Qu.:1.4059 3rd Qu.:1.4221
Max. :4.56267 Max. :6.37947 Max. :5.0486 Max. :6.6295
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
V1 V2 V3 V4
Min. :0.01701 Min. :0.01255 Min. :0.0000 Min. :0.0000
1st Qu.:0.35423 1st Qu.:0.29118 1st Qu.:0.2745 1st Qu.:0.3953
Median :0.71719 Median :0.70280 Median :0.6339 Median :0.8223
Mean :0.90118 Mean :1.00110 Mean :0.9454 Mean :1.1324
3rd Qu.:1.16817 3rd Qu.:1.33124 3rd Qu.:1.4059 3rd Qu.:1.4221
Max. :4.56193 Max. :6.37932 Max. :5.0486 Max. :6.6295
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
V1 V2
Min. :-5.8801 Min. :-5.66985
1st Qu.:-1.1850 1st Qu.:-1.57014
Median :-0.2164 Median : 0.04823
Mean : 0.0349 Mean :-0.05481
3rd Qu.: 1.4964 3rd Qu.: 1.45113
Max. : 7.0751 Max. : 6.19744
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
>
> ### normalizeBetweenArrays
>
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
[,1] [,2]
[1,] -1.1669590 4.5558123
[2,] 0.8969743 0.3296544
[3,] 2.8245754 1.4249960
[4,] -1.8532747 0.4804851
[5,] 1.9159023 -5.5087631
> MA$A[1:5,]
[,1] [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200 0.2071043
[4,] 0.09281027 -1.3880965
[5,] 0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
[,1] [,2]
[1,] -1.1669590 4.5558123
[2,] 0.8969743 0.3296544
[3,] 2.8245754 1.4249960
[4,] -1.8532747 0.4804851
[5,] 1.9159023 -5.5087631
> MA$A[1:5,]
[,1] [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200 0.2071043
[4,] 0.09281027 -1.3880965
[5,] 0.22385828 -3.0855818
>
> ### unwrapdups
>
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
[,1] [,2]
[1,] 1 7
[2,] 2 8
[3,] 3 9
[4,] 4 10
[5,] 5 11
[6,] 6 12
> unwrapdups(M,ndups=2)
[,1] [,2] [,3] [,4]
[1,] 1 2 7 8
[2,] 3 4 9 10
[3,] 5 6 11 12
> unwrapdups(M,ndups=3)
[,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 2 3 7 8 9
[2,] 4 5 6 10 11 12
> unwrapdups(M,ndups=2,spacing=3)
[,1] [,2] [,3] [,4]
[1,] 1 4 7 10
[2,] 2 5 8 11
[3,] 3 6 9 12
>
> ### trigammaInverse
>
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06 NA 4.961687e-01 1.000001e-03
>
> ### lmFit, eBayes, topTable
>
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
First3 Last3 Last3.First3 AveExpr F P.Value adj.P.Val
A 1.77602021 0.06025114 -1.71576906 0.918135675 50.91471061 7.727200e-23 7.727200e-22
D -0.05454069 0.39127869 0.44581938 0.168369004 2.51638838 8.075072e-02 3.758388e-01
F -0.16249607 -0.33009728 -0.16760121 -0.246296671 2.18256779 1.127516e-01 3.758388e-01
G 0.30852468 -0.06873462 -0.37725930 0.119895035 1.61088775 1.997102e-01 4.992756e-01
H -0.16942269 0.20578118 0.37520387 0.018179245 1.14554368 3.180510e-01 6.361019e-01
J 0.21417623 0.07074940 -0.14342683 0.142462814 0.82029274 4.403027e-01 7.338379e-01
C -0.12236781 0.15095948 0.27332729 0.014295836 0.60885003 5.439761e-01 7.382414e-01
B -0.11982833 0.13529287 0.25512120 0.007732271 0.52662792 5.905931e-01 7.382414e-01
E 0.01897934 0.10434934 0.08536999 0.061664340 0.18136849 8.341279e-01 9.268088e-01
I -0.04720963 0.03996397 0.08717360 -0.003622829 0.06168476 9.401792e-01 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
logFC AveExpr t P.Value adj.P.Val B
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
logFC AveExpr t P.Value adj.P.Val B
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort.by="logFC",resort.by="t")
logFC AveExpr t P.Value adj.P.Val B
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
logFC AveExpr t P.Value adj.P.Val B
A -1.71576906 0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D 0.44581938 0.168369004 1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930 0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H 0.37520387 0.018179245 1.5065768 1.397766e-01 3.494414e-01 -5.785971
C 0.27332729 0.014295836 1.0975061 2.789833e-01 5.196681e-01 -6.313399
B 0.25512120 0.007732271 1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683 0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I 0.08717360 -0.003622829 0.3500330 7.281504e-01 7.335508e-01 -6.849117
E 0.08536999 0.061664340 0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5)
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p.value=0.2,lfc=0.5,sort.by="none")
logFC AveExpr t P.Value adj.P.Val B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> contrasts.fit(fit[1:3,],contrast.matrix[,0])
An object of class "MArrayLM"
$coefficients
A
B
C
$rank
[1] 2
$assign
NULL
$qr
$qr
First3Arrays Last3Arrays
[1,] -1.7320508 0.0000000
[2,] 0.5773503 -1.7320508
[3,] 0.5773503 0.0000000
[4,] 0.0000000 0.5773503
[5,] 0.0000000 0.5773503
[6,] 0.0000000 0.5773503
$qraux
[1] 1.57735 1.00000
$pivot
[1] 1 2
$tol
[1] 1e-07
$rank
[1] 2
$df.residual
[1] 4 4 4
$sigma
A B C
0.3299787 0.3323336 0.2315815
$cov.coefficients
<0 x 0 matrix>
$stdev.unscaled
A
B
C
$pivot
[1] 1 2
$Amean
A B C
0.918135675 0.007732271 0.014295836
$method
[1] "ls"
$design
First3Arrays Last3Arrays
[1,] 1 0
[2,] 1 0
[3,] 1 0
[4,] 0 1
[5,] 0 1
[6,] 0 1
$contrasts
[1,]
[2,]
> fit$coefficients[1,1] <- NA
> contrasts.fit(fit[1:3,],contrast.matrix)$coefficients
First3 Last3 Last3-First3
A NA 0.06025114 NA
B -0.1198283 0.13529287 0.2551212
C -0.1223678 0.15095948 0.2733273
>
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)
Null Two Three
5 3 2
>
> ### marray object
>
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> # data(swirl)
> # snorm = maNorm(swirl)
> # fit <- lmFit(snorm, design = c(1,-1,-1,1))
> # fit <- eBayes(fit)
> # topTable(fit,resort.by="AveExpr")
> #}
>
> ### duplicateCorrelation
>
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130 0.4088967 -0.1964978 -0.6093769 0.3730118
>
> ### gls.series
>
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
First3Arrays Last3Arrays
[1,] 0.82809594 0.09777201
[2,] -0.08845425 0.27111909
[3,] -0.07175836 -0.11287397
[4,] 0.06955100 0.06852328
[5,] 0.08348330 0.05535668
> fit$stdev.unscaled
First3Arrays Last3Arrays
[1,] 0.3888215 0.3888215
[2,] 0.3888215 0.3888215
[3,] 0.3888215 0.3888215
[4,] 0.3888215 0.3888215
[5,] 0.3888215 0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
>
> ### mrlm
>
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
'rlm' failed to converge in 20 steps
> fit$coefficients
First3Arrays Last3Arrays
A 1.75138894 0.06025114
B -0.11982833 0.10322039
C -0.09302502 0.15095948
D -0.05454069 0.33700045
E 0.07927938 0.10434934
F -0.16249607 -0.34010852
G 0.30852468 -0.06873462
H -0.16942269 0.24392984
I -0.04720963 0.03996397
J 0.21417623 -0.05679272
> fit$stdev.unscaled
First3Arrays Last3Arrays
A 0.5933418 0.5773503
B 0.5773503 0.6096497
C 0.6017444 0.5773503
D 0.5773503 0.6266021
E 0.6307703 0.5773503
F 0.5773503 0.5846707
G 0.5773503 0.5773503
H 0.5773503 0.6544564
I 0.5773503 0.5773503
J 0.5773503 0.6689776
> fit$sigma
[1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945 0.2267556 0.3537469 0.2172409
> fit$df.residual
[1] 4 4 4 4 4 4 4 4 4 4
>
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
alpha-beta mu+alpha mu+beta
[1,] 3.537333 1.677465 -1.859868
[2,] 4.355578 2.372554 -1.983024
[3,] 3.197645 1.053584 -2.144061
[4,] 2.697734 1.611443 -1.086291
[5,] 3.502304 2.051995 -1.450309
$stdev.unscaled
alpha-beta mu+alpha mu+beta
[1,] 0.8164966 0.5773503 0.5773503
[2,] 0.8164966 0.5773503 0.5773503
[3,] 0.8164966 0.5773503 0.5773503
[4,] 0.8164966 0.5773503 0.5773503
[5,] 1.1547005 0.8368633 0.8368633
$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509
$df.residual
[1] 6 6 6 6 4
$cov.coefficients
alpha-beta mu+alpha mu+beta
alpha-beta 0.6666667 3.333333e-01 -3.333333e-01
mu+alpha 0.3333333 3.333333e-01 1.110223e-16
mu+beta -0.3333333 1.110223e-16 3.333333e-01
$pivot
[1] 1 2 3
$rank
[1] 3
$Amean
[1] 0.2034961 0.1954604 -0.2863347 0.1188659 0.1784593
$method
[1] "ls"
$design
mu alpha beta
1 1 0 0
2 1 0 0
3 1 0 0
4 1 1 0
5 1 1 0
6 1 1 0
7 1 0 1
8 1 0 1
9 1 0 1
$contrasts
alpha-beta mu+alpha mu+beta
mu 0 1 1
alpha 1 1 0
beta -1 0 1
$df.prior
[1] 9.306153
$s2.prior
[1] 0.923179
$var.prior
[1] 17.33142 17.33142 12.26855
$proportion
[1] 0.01
$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980
$t
alpha-beta mu+alpha mu+beta
[1,] 3.847656 2.580411 -2.860996
[2,] 6.637308 5.113018 -4.273553
[3,] 3.692066 1.720376 -3.500994
[4,] 3.464003 2.926234 -1.972606
[5,] 3.175181 2.566881 -1.814221
$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615
$p.value
alpha-beta mu+alpha mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630
$lods
alpha-beta mu+alpha mu+beta
[1,] -1.013417 -3.702133 -3.0332393
[2,] 3.981496 1.283349 -0.2615911
[3,] -1.315036 -5.168621 -1.7864101
[4,] -1.757103 -3.043209 -4.6191869
[5,] -2.257358 -3.478267 -4.5683738
$F
[1] 7.421911 22.203107 7.608327 6.227010 5.060579
$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02
>
> ### uniquegenelist
>
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
>
> ### classifyTests
>
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 0 0
[3,] -1 -1 1
[4,] 0 0 0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
[,1] [,2] [,3]
[1,] 0 1 0
[2,] 0 1 0
[3,] 0 0 0
[4,] 0 0 0
>
> ### avereps
>
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
S1 S2 S3
b -0.2353018 0.5220094 0.2302895
a -0.4347701 0.6453498 -0.6758914
c 0.3482980 -0.4820695 -0.3841313
>
> ### roast
>
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
Active.Prop P.Value
Down 0 0.997999500
Up 1 0.002250563
UpOrDown 1 0.004500000
Mixed 1 0.004500000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
Active.Prop P.Value
Down 0 0.998749687
Up 1 0.001500375
UpOrDown 1 0.003000000
Mixed 1 0.003000000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
Active.Prop P.Value
Down 0 0.996999250
Up 1 0.003250813
UpOrDown 1 0.006500000
Mixed 1 0.006500000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.0055 0.0105 0.0055 0.0105
set2 5 0 0 Up 0.2025 0.2025 0.4715 0.4715
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1 Up 0.0050 0.0095 0.005 0.0095
set2 5 0 0 Up 0.6845 0.6845 0.642 0.6420
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1.0 Up 0.0030 0.0055 0.003 0.0055
set2 5 0 0.2 Down 0.9615 0.9615 0.496 0.4960
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes PropDown PropUp Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 0 1.0 Up 0.0025 0.0045 0.0025 0.0045
set2 5 0 0.2 Down 0.8930 0.8930 0.4380 0.4380
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes Direction PValue FDR PValue.Mixed FDR.Mixed
set1 5 Up 0.001568924 0.003137848 0.0001156464 0.0002312929
set2 5 Down 0.932105219 0.932105219 0.4315499569 0.4315499569
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
NGenes Direction PValue PValue.Mixed
set1 5 Up 0.001568924 0.0001156464
>
> ### camera
>
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue
set1 5 -0.2481655 Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue FDR
set1 5 -0.2481655 Up 0.0009047749 0.00180955
set2 5 0.1719094 Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
NGenes Direction PValue
set1 5 Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
NGenes Direction PValue FDR
set1 5 Up 7.334400e-12 1.466880e-11
set2 5 Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
NGenes Direction PValue
set1 5 Up 7.3344e-12
>
> ### with EList arg
>
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
Active.Prop P.Value
Down 0 0.996999250
Up 1 0.003250813
UpOrDown 1 0.006500000
Mixed 1 0.006500000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
NGenes Correlation Direction PValue
set1 5 -0.2481655 Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
NGenes Direction PValue
set1 5 Up 7.3344e-12
>
> ### eBayes with trend
>
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene2 3.729512 1.73488969 4.865697 0.0004854886 0.02902331 0.1596831
Gene3 3.488703 1.03931081 4.754954 0.0005804663 0.02902331 -0.0144071
Gene4 2.696676 1.74060725 3.356468 0.0063282637 0.21094212 -2.3434702
Gene1 2.391846 1.72305203 3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5 2.387967 1.63066783 2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39 1.366141 -0.27360750 2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074 1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50 1.034777 0.01608433 2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
Gene1 Gene2 Gene3 Gene4 Gene5 Gene6 Gene7 Gene8 Gene9 Gene10 Gene11 Gene12
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 0.2985145 0.2832520 0.3232434 0.3279710
Gene13 Gene14 Gene15 Gene16 Gene17 Gene18 Gene19 Gene20 Gene21 Gene22 Gene23 Gene24
0.2816081 0.2943502 0.3127994 0.2894802 0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541
Gene25 Gene26 Gene27 Gene28 Gene29 Gene30 Gene31 Gene32 Gene33 Gene34 Gene35 Gene36
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 0.2988244 0.3450090 0.3048596 0.3089086
Gene37 Gene38 Gene39 Gene40 Gene41 Gene42 Gene43 Gene44 Gene45 Gene46 Gene47 Gene48
0.3104534 0.4551549 0.3220008 0.2813286 0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003
Gene49 Gene50 Gene51 Gene52 Gene53 Gene54 Gene55 Gene56 Gene57 Gene58 Gene59 Gene60
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 0.2955317 0.2815907 0.2919420 0.2849675
Gene61 Gene62 Gene63 Gene64 Gene65 Gene66 Gene67 Gene68 Gene69 Gene70 Gene71 Gene72
0.3540805 0.3491713 0.2975019 0.2939325 0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850
Gene73 Gene74 Gene75 Gene76 Gene77 Gene78 Gene79 Gene80 Gene81 Gene82 Gene83 Gene84
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 0.3413700 0.2946268 0.3029565 0.2920284
Gene85 Gene86 Gene87 Gene88 Gene89 Gene90 Gene91 Gene92 Gene93 Gene94 Gene95 Gene96
0.2926205 0.2818046 0.3425116 0.2882936 0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784
Gene97 Gene98 Gene99 Gene100
0.2846345 0.2819651 0.3137551 0.2856081
> summary(fit$s2.post)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2335 0.2603 0.2997 0.3375 0.3655 0.7812
>
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene3 3.488703 1.03931081 4.604490 0.0007644061 0.07644061 -0.2333915
Gene2 3.729512 1.73488969 4.158038 0.0016033158 0.08016579 -0.9438583
Gene4 2.696676 1.74060725 2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5 2.387967 1.63066783 2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39 1.366141 -0.27360750 2.440729 0.0328318388 0.46902627 -3.8172597
Gene1 2.638272 1.47993643 2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074 1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50 1.034777 0.01608433 2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
[1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052 0.3074225 0.2985517 0.2786374 0.3267121
[12] 0.3316013 0.2766404 0.2932679 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412 0.2761282
[23] 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389
[34] 0.3483845 0.3062977 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311 0.2776653 0.3201314
[45] 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306
[56] 0.2870975 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804 0.2927300 0.2987764 0.3301406
[67] 0.3437962 0.3430762 0.2761729 0.2763094 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
[78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616 0.2910320 0.2769211 0.3459946 0.2859057
[89] 0.2935193 0.3097398 0.2865663 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484 0.3164981
[100] 0.2817725
> summary(fit$s2.post)
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.2296 0.2581 0.3003 0.3453 0.3652 0.9158
>
> ### eBayes with robust
>
> fitr <- lmFit(y,design)
> fitr <- eBayes(fitr,robust=TRUE)
> summary(fitr$df.prior)
Min. 1st Qu. Median Mean 3rd Qu. Max.
6.717 9.244 9.244 9.194 9.244 9.244
> topTable(fitr,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene2 3.729512 1.73488969 7.108463 1.752774e-05 0.001752774 3.3517310
Gene3 3.488703 1.03931081 5.041209 3.526138e-04 0.017630688 0.4056329
Gene4 2.696676 1.74060725 4.697690 6.150508e-04 0.020501693 -0.1463315
Gene5 2.387967 1.63066783 3.451807 5.245019e-03 0.131125480 -2.2678836
Gene1 2.638272 1.47993643 3.317593 8.651142e-03 0.173022847 -2.4400000
Gene33 -1.492317 -0.07525287 -2.716431 1.970991e-02 0.297950865 -3.5553166
Gene95 -1.907074 1.26297763 -2.685067 2.085656e-02 0.297950865 -3.6094982
Gene80 -1.839760 -0.32802306 -2.535926 2.727440e-02 0.340929958 -3.8653107
Gene39 1.366141 -0.27360750 2.469570 3.071854e-02 0.341317083 -3.9779817
Gene50 1.034777 0.01608433 1.973040 7.357960e-02 0.632875126 -4.7877548
> fitr <- eBayes(fitr,trend=TRUE,robust=TRUE)
> summary(fitr$df.prior)
Min. 1st Qu. Median Mean 3rd Qu. Max.
7.809 8.972 8.972 8.949 8.972 8.972
> topTable(fitr,coef=2)
logFC AveExpr t P.Value adj.P.Val B
Gene2 3.729512 1.73488969 4.754160 0.0005999064 0.05999064 -0.0218247
Gene3 3.488703 1.03931081 3.761219 0.0031618743 0.15809372 -1.6338257
Gene4 2.696676 1.74060725 3.292262 0.0071993347 0.23997782 -2.4295326
Gene33 -1.492317 -0.07525287 -3.063180 0.0108203134 0.27050784 -2.8211394
Gene50 1.034777 0.01608433 2.645717 0.0228036320 0.38815282 -3.5304767
Gene5 2.387967 1.63066783 2.633901 0.0232891695 0.38815282 -3.5503445
Gene1 2.638272 1.47993643 2.204116 0.0550613420 0.58959402 -4.0334169
Gene80 -1.839760 -0.32802306 -2.332729 0.0397331916 0.56761702 -4.0496640
Gene39 1.366141 -0.27360750 2.210665 0.0492211477 0.58959402 -4.2469578
Gene95 -1.907074 1.26297763 -2.106861 0.0589594023 0.58959402 -4.4117140
>
> ### voom
>
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E" "weights" "design" "targets"
> summary(v$E)
V1 V2 V3 V4
Min. :12.38 Min. :12.32 Min. :12.17 Min. :12.08
1st Qu.:13.11 1st Qu.:13.05 1st Qu.:13.11 1st Qu.:13.03
Median :13.34 Median :13.28 Median :13.35 Median :13.35
Mean :13.29 Mean :13.29 Mean :13.28 Mean :13.28
3rd Qu.:13.48 3rd Qu.:13.54 3rd Qu.:13.48 3rd Qu.:13.50
Max. :14.01 Max. :13.95 Max. :14.03 Max. :14.05
> summary(v$weights)
V1 V2 V3 V4
Min. : 7.729 Min. : 7.729 Min. : 7.729 Min. : 7.729
1st Qu.:13.859 1st Qu.:15.067 1st Qu.:14.254 1st Qu.:13.592
Median :15.913 Median :16.621 Median :16.081 Median :16.028
Mean :16.773 Mean :18.525 Mean :18.472 Mean :17.112
3rd Qu.:18.214 3rd Qu.:20.002 3rd Qu.:18.475 3rd Qu.:18.398
Max. :34.331 Max. :34.331 Max. :34.331 Max. :34.331
>
> ### goana
>
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,number=10,truncate.term=30)
Term Ont N Up Down P.Up P.Down
GO:0032502 developmental process BP 24 4 7 0.871310423 0.001532528
GO:0070062 extracellular exosome CC 8 0 4 1.000000000 0.003047199
GO:0065010 extracellular membrane-boun... CC 8 0 4 1.000000000 0.003047199
GO:0043230 extracellular organelle CC 8 0 4 1.000000000 0.003047199
GO:1903561 extracellular vesicle CC 8 0 4 1.000000000 0.003047199
GO:0006915 apoptotic process BP 5 4 1 0.009503355 0.416247633
GO:0008219 cell death BP 5 4 1 0.009503355 0.416247633
GO:0012501 programmed cell death BP 5 4 1 0.009503355 0.416247633
GO:0042981 regulation of apoptotic pro... BP 5 4 1 0.009503355 0.416247633
GO:0043067 regulation of programmed ce... BP 5 4 1 0.009503355 0.416247633
> topGO(go,number=10,truncate.term=30,sort="down")
Term Ont N Up Down P.Up P.Down
GO:0032502 developmental process BP 24 4 7 0.8713104 0.001532528
GO:0070062 extracellular exosome CC 8 0 4 1.0000000 0.003047199
GO:0065010 extracellular membrane-boun... CC 8 0 4 1.0000000 0.003047199
GO:0043230 extracellular organelle CC 8 0 4 1.0000000 0.003047199
GO:1903561 extracellular vesicle CC 8 0 4 1.0000000 0.003047199
GO:0032501 multicellular organismal pr... BP 33 7 7 0.7048432 0.013750585
GO:0016485 protein processing BP 7 1 3 0.8497705 0.020760307
GO:0031982 vesicle CC 20 3 5 0.8982815 0.025464546
GO:0009887 animal organ morphogenesis BP 3 0 2 1.0000000 0.025788497
GO:0043009 chordate embryonic developm... BP 3 0 2 1.0000000 0.025788497
>
> proc.time()
user system elapsed
3.00 0.42 3.60
limma.Rcheck/limma-Ex.timings
| name | user | system | elapsed | |
| EList | 0.014 | 0.002 | 0.017 | |
| LargeDataObject | 0.000 | 0.000 | 0.001 | |
| PrintLayout | 0.001 | 0.002 | 0.001 | |
| TestResults | 0.000 | 0.001 | 0.001 | |
| alias2Symbol | 10.788 | 0.644 | 12.743 | |
| arrayWeights | 0.021 | 0.004 | 0.026 | |
| arrayWeightsQuick | 0.001 | 0.001 | 0.002 | |
| asMatrixWeights | 0.000 | 0.002 | 0.002 | |
| auROC | 0.002 | 0.000 | 0.002 | |
| avearrays | 0.001 | 0.001 | 0.003 | |
| avereps | 0.001 | 0.000 | 0.002 | |
| backgroundcorrect | 0.011 | 0.003 | 0.015 | |
| barcodeplot | 0.073 | 0.008 | 0.096 | |
| beadCountWeights | 0.000 | 0.000 | 0.001 | |
| blockDiag | 0.001 | 0.001 | 0.002 | |
| camera | 0.050 | 0.006 | 0.061 | |
| cbind | 0.007 | 0.002 | 0.010 | |
| changelog | 0.051 | 0.113 | 0.175 | |
| channel2M | 0.002 | 0.001 | 0.003 | |
| chooseLowessSpan | 0.009 | 0.003 | 0.016 | |
| classifytestsF | 0.005 | 0.001 | 0.008 | |
| contrastAsCoef | 0.007 | 0.001 | 0.009 | |
| contrasts.fit | 0.023 | 0.002 | 0.028 | |
| controlStatus | 0.045 | 0.007 | 0.056 | |
| coolmap | 0.203 | 0.021 | 0.242 | |
| cumOverlap | 0.001 | 0.001 | 0.003 | |
| detectionPValue | 0.000 | 0.000 | 0.001 | |
| diffSplice | 0.000 | 0.001 | 0.001 | |
| dim | 0.001 | 0.001 | 0.002 | |
| dupcor | 0.244 | 0.007 | 0.276 | |
| ebayes | 0.018 | 0.004 | 0.025 | |
| fitGammaIntercept | 0.001 | 0.000 | 0.001 | |
| fitfdist | 0.001 | 0.001 | 0.005 | |
| fitmixture | 0.025 | 0.005 | 0.032 | |
| genas | 0.135 | 0.020 | 0.168 | |
| geneSetTest | 0.002 | 0.000 | 0.002 | |
| getSpacing | 0.001 | 0.001 | 0.001 | |
| getlayout | 0.000 | 0.001 | 0.001 | |
| goana | 0.000 | 0.001 | 0.002 | |
| goanaTrend | 0.070 | 0.003 | 0.085 | |
| head | 0.007 | 0.002 | 0.010 | |
| heatdiagram | 0.000 | 0.000 | 0.001 | |
| helpMethods | 0 | 0 | 0 | |
| ids2indices | 0.000 | 0.001 | 0.000 | |
| imageplot | 0.046 | 0.009 | 0.060 | |
| intraspotCorrelation | 0 | 0 | 0 | |
| isfullrank | 0.001 | 0.001 | 0.002 | |
| isnumeric | 0.001 | 0.001 | 0.002 | |
| kooperberg | 0.000 | 0.001 | 0.001 | |
| limmaUsersGuide | 0.002 | 0.002 | 0.003 | |
| lm.series | 0.000 | 0.001 | 0.000 | |
| lmFit | 0.669 | 0.099 | 0.811 | |
| lmscFit | 0.000 | 0.000 | 0.002 | |
| loessfit | 0.014 | 0.005 | 0.022 | |
| logcosh | 0.001 | 0.001 | 0.002 | |
| logsumexp | 0.000 | 0.000 | 0.001 | |
| ma3x3 | 0.002 | 0.002 | 0.004 | |
| makeContrasts | 0.002 | 0.001 | 0.003 | |
| makeunique | 0.001 | 0.000 | 0.001 | |
| mdplot | 0.010 | 0.003 | 0.013 | |
| merge | 0.009 | 0.004 | 0.013 | |
| mergeScansRG | 0.000 | 0.000 | 0.001 | |
| modelMatrix | 0.003 | 0.002 | 0.005 | |
| modifyWeights | 0.000 | 0.001 | 0.001 | |
| nec | 0.001 | 0.001 | 0.001 | |
| normalizeMedianAbsValues | 0.001 | 0.001 | 0.002 | |
| normalizeRobustSpline | 0.080 | 0.010 | 0.099 | |
| normalizeVSN | 1.248 | 0.073 | 1.642 | |
| normalizebetweenarrays | 0.002 | 0.001 | 0.004 | |
| normalizeprintorder | 0.001 | 0.001 | 0.001 | |
| normexpfit | 0.003 | 0.001 | 0.005 | |
| normexpfitcontrol | 0 | 0 | 0 | |
| normexpfitdetectionp | 0.000 | 0.001 | 0.005 | |
| normexpsignal | 0.000 | 0.001 | 0.000 | |
| plotDensities | 0.000 | 0.001 | 0.000 | |
| plotExonJunc | 0.000 | 0.001 | 0.001 | |
| plotExons | 0.000 | 0.000 | 0.001 | |
| plotMD | 0.119 | 0.016 | 0.177 | |
| plotMDS | 0.024 | 0.003 | 0.034 | |
| plotRLDF | 0.013 | 0.002 | 0.019 | |
| plotSplice | 0.000 | 0.001 | 0.001 | |
| plotWithHighlights | 0.021 | 0.004 | 0.030 | |
| plotma | 0.141 | 0.015 | 0.175 | |
| poolvar | 0.001 | 0.000 | 0.002 | |
| predFCm | 0.023 | 0.002 | 0.045 | |
| printorder | 0.011 | 0.027 | 0.044 | |
| printtipWeights | 0.001 | 0.001 | 0.001 | |
| propTrueNull | 0.003 | 0.001 | 0.004 | |
| propexpr | 0.000 | 0.001 | 0.001 | |
| protectMetachar | 0.000 | 0.000 | 0.001 | |
| qqt | 0.011 | 0.001 | 0.014 | |
| qualwt | 0.000 | 0.001 | 0.000 | |
| rankSumTestwithCorrelation | 0.010 | 0.002 | 0.012 | |
| read.idat | 0.000 | 0.001 | 0.000 | |
| read.ilmn | 0.001 | 0.000 | 0.001 | |
| read.maimages | 0.000 | 0.001 | 0.000 | |
| readImaGeneHeader | 0.000 | 0.001 | 0.000 | |
| readgal | 0 | 0 | 0 | |
| removeBatchEffect | 0.019 | 0.002 | 0.023 | |
| removeext | 0.001 | 0.001 | 0.002 | |
| roast | 0.164 | 0.008 | 0.189 | |
| romer | 0.038 | 0.003 | 0.061 | |
| sampleInfoFromGEO | 0.000 | 0.001 | 0.003 | |
| selectmodel | 0.018 | 0.001 | 0.022 | |
| squeezeVar | 0.001 | 0.000 | 0.002 | |
| strsplit2 | 0.001 | 0.001 | 0.001 | |
| subsetting | 0.006 | 0.003 | 0.008 | |
| targetsA2C | 0.007 | 0.001 | 0.007 | |
| topGO | 0.001 | 0.000 | 0.000 | |
| topRomer | 0.001 | 0.000 | 0.000 | |
| topSplice | 0 | 0 | 0 | |
| toptable | 0 | 0 | 0 | |
| tricubeMovingAverage | 0.004 | 0.001 | 0.005 | |
| trigammainverse | 0.001 | 0.000 | 0.002 | |
| trimWhiteSpace | 0.000 | 0.000 | 0.001 | |
| uniquegenelist | 0.001 | 0.000 | 0.002 | |
| unwrapdups | 0.001 | 0.001 | 0.002 | |
| venn | 0.038 | 0.003 | 0.044 | |
| volcanoplot | 0 | 0 | 0 | |
| voom | 0.000 | 0.000 | 0.001 | |
| weightedLowess | 0.011 | 0.002 | 0.015 | |
| weightedmedian | 0.000 | 0.000 | 0.001 | |
| writefit | 0 | 0 | 0 | |
| zscore | 0.001 | 0.000 | 0.001 | |
| zscoreT | 0.001 | 0.000 | 0.002 | |