Introduction

Single-cell RNA sequencing has become a common approach to trace developmental processes of cells, however, using exogenous barcodes is more direct than predicting from expression profiles recently, based on that, as gene-editing technology matures, combining this technological method with exogenous barcodes can generate more complex dynamic information for single-cell. In this application note, we introduce an R package: LinTInd for reconstructing a tree from alleles generated by the genome-editing tool known as CRISPR for a moderate time period based on the order in which editing occurs, and for sc-RNA seq, ScarLin can also quantify the similarity between each cluster in three ways.

Installation

Via GitHub

devtools::install_github("mana-W/LinTInd")

Via Bioconductor

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("LinTInd")
library(LinTInd)

Import data

The input for LinTInd consists three required files:

and an optional file:

data<-paste0(system.file("extdata",package = 'LinTInd'),"/CB_UMI")
fafile<-paste0(system.file("extdata",package = 'LinTInd'),"/V3.fasta")
cutsite<-paste0(system.file("extdata",package = 'LinTInd'),"/V3.cutSites")
celltype<-paste0(system.file("extdata",package = 'LinTInd'),"/celltype.tsv")
data<-read.table(data,sep="\t",header=TRUE)
ref<-ReadFasta(fafile)
cutsite<-read.table(cutsite,col.names = c("indx","start","end"))
celltype<-read.table(celltype,header=TRUE,stringsAsFactors=FALSE)

For the sequence file, only the column contain reads’ strings is requeired, the cell barcodes and UMIs are both optional.

head(data,3)
##                                   Read.ID
## 1  @A01045:289:HM7K3DRXX:2:2101:9896:1031
## 2 @A01045:289:HM7K3DRXX:2:2101:13367:1031
## 3  @A01045:289:HM7K3DRXX:2:2101:9959:1047
##                                                                                                                                                                                                                                                     Read.Seq
## 1 GAACGCGTAGGATAACATGGCCATCATCAAGGAGTTCTCATGCGCTTCAAGGTGCACATGGTTTATTGGAGCCGTACATGAACTGAGGTTAAGGACAGGATGTCCCAGGCGTAGGTAATTGGCCCCCTGCCCTTCGCCTGGGTTATAAGCTTCGGGTTTAAACGGGCCCTGGGGGTGGCATCCCTGTGACCCCTCCCCAGTGCCTCTCCTGGCCCTGGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTC
## 2 GAACGCGTAGGATAACATGGCCATCATCAAGGAGTTCTCATGCGCTTCAAGGTGCACATGGTTTATTGGAGCCGTACATGAACTGAGGTTAAGGACAGGATGTCCCAGGCGTAGGTAATTGGCCCCCTGCCCTTCGCCTGGGTTATAAGCTTCGGGTTTAAACGGGCCCTGGGGGTGGCATCCCTGTGACCCCTCCCCAGTGCCTCTCCTGGCCCTGGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTC
## 3 GAACGCGTAGGATAACATGGCCATCATCAAGGAGTTCTCATGCGCTTCAAGGTGCACATGGTTTATTGGAGCCGTACATGAACTGAGGTTAAGGACAGGATGTCCCAGGCGTAGGTAATTGGCCCCCTGCCCTTCGCCTGGGTTATAAGCTTCGGGTTTAAACGGGCCCTGGGGGTGGCATCCCTGTGACCCCTCCCCAGTGCCTCTCCTGGCCCTGGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTC
##            Cell.BC        UMI
## 1 GAAGGGTAGCCTCAGC CTTCTCCCGA
## 2 ACCCTCACAAGACTGG TGTAATTTTT
## 3 GAAGGGTAGCCTCAGC CTTCTCCCGA
ref
## $scarfull
## 333-letter DNAString object
## seq: GAACGCGTAGGATAACATGGCCATCATCAAGGAGTT...GGAAGTTGCCACTCCAGTGCCCACCAGCCTTGTCCT
cutsite
##   indx start end
## 1    0    39 267
## 2    1     1  23
## 3    2    28  50
## 4    3    55  77
## 5    4    82 104
## 6    5   109 131
## 7    6   136 158
## 8    7   163 185
head(celltype,3)
##            Cell.BC Cell.type
## 1 AAGCGAGTCTTCTGTA         A
## 2 AATCGACTCGTAGTGT         A
## 3 ACATGCAGTCCACACG         A

Array identify and indel visualization

In the first step, we shold use FindIndel() to alignment and find indels, and the function IndelForm() will help us to generate an array-form string for each read.

scarinfo<-FindIndel(data=data,scarfull=ref,scar=cutsite,indel.coverage="All",type="test",cln=1)
scarinfo<-IndelForm(scarinfo,cln=1)

Then for single-cell sequencing, we shold define a final-version of array-form string for each cell use IndelIdents(), there are three method are provided :

For bulk sequencing, in this step, we will generate a “cell barcode” for each read.

cellsinfo<-IndelIdents(scarinfo,method.use="umi.num",cln=1)

After define the indels for each cell, we can use IndelPlot() to visualise them.

IndelPlot(cellsinfo = cellsinfo)

Indel extract and similarity calculate

We can use the function TagProcess() to extract indels for cells/reads. The parameter Cells is optional.

tag<-TagProcess(cellsinfo$info,Cells=celltype)

And if the annotation of each cells are provided, we can also use TagDist() to calculate the relationship between each group in three way:

The heatmap of this result will be saved as a pdf file.

tag_dist=TagDist(tag,method = "Jaccard")
## Using Cell.type as value column: use value.var to override.
## Aggregation function missing: defaulting to length
tag_dist
##           A         B         C         D         E
## A 1.0000000 0.4925373 0.2794118 0.2985075 0.2058824
## B 0.4925373 1.0000000 0.5588235 0.6060606 0.4117647
## C 0.2794118 0.5588235 1.0000000 0.9047619 0.7500000
## D 0.2985075 0.6060606 0.9047619 1.0000000 0.6666667
## E 0.2058824 0.4117647 0.7500000 0.6666667 1.0000000

Tree reconstruct

In the laste part, we can use BuildTree() to Generate an array generant tree.

treeinfo<-BuildTree(tag)
## Using Cell.num as value column: use value.var to override.

Finally, we can use the function PlotTree() to visualise the tree created before.

plotinfo<-PlotTree(treeinfo = treeinfo,data.extract = "TRUE",annotation = "TRUE")
## Using tags as id variables
plotinfo$p

Session Info

sessionInfo()
## R version 4.3.0 RC (2023-04-18 r84287)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
## [1] LinTInd_1.5.0       S4Vectors_0.39.0    BiocGenerics_0.47.0
## [4] ggplot2_3.4.2      
## 
## loaded via a namespace (and not attached):
##  [1] stringdist_0.9.10       gtable_0.3.3            xfun_0.39              
##  [4] bslib_0.4.2             htmlwidgets_1.6.2       rlist_0.4.6.2          
##  [7] lattice_0.21-8          vctrs_0.6.2             tools_4.3.0            
## [10] bitops_1.0-7            generics_0.1.3          yulab.utils_0.0.6      
## [13] tibble_3.2.1            fansi_1.0.4             highr_0.10             
## [16] pkgconfig_2.0.3         pheatmap_1.0.12         data.table_1.14.8      
## [19] ggnewscale_0.4.8        ggplotify_0.1.0         RColorBrewer_1.1-3     
## [22] lifecycle_1.0.3         GenomeInfoDbData_1.2.10 farver_2.1.1           
## [25] stringr_1.5.0           compiler_4.3.0          treeio_1.25.0          
## [28] Biostrings_2.69.0       munsell_0.5.0           data.tree_1.0.0        
## [31] ggtree_3.9.0            ggfun_0.0.9             GenomeInfoDb_1.37.0    
## [34] htmltools_0.5.5         sass_0.4.5              lazyeval_0.2.2         
## [37] RCurl_1.98-1.12         yaml_2.3.7              pillar_1.9.0           
## [40] crayon_1.5.2            jquerylib_0.1.4         tidyr_1.3.0            
## [43] ellipsis_0.3.2          cachem_1.0.7            nlme_3.1-162           
## [46] tidyselect_1.2.0        aplot_0.1.10            digest_0.6.31          
## [49] stringi_1.7.12          reshape2_1.4.4          dplyr_1.1.2            
## [52] purrr_1.0.1             labeling_0.4.2          cowplot_1.1.1          
## [55] fastmap_1.1.1           grid_4.3.0              colorspace_2.1-0       
## [58] cli_3.6.1               magrittr_2.0.3          patchwork_1.1.2        
## [61] utf8_1.2.3              ape_5.7-1               withr_2.5.0            
## [64] scales_1.2.1            rmarkdown_2.21          XVector_0.41.0         
## [67] networkD3_0.4           igraph_1.4.2            evaluate_0.20          
## [70] knitr_1.42              IRanges_2.35.0          gridGraphics_0.5-1     
## [73] rlang_1.1.0             Rcpp_1.0.10             glue_1.6.2             
## [76] tidytree_0.4.2          jsonlite_1.8.4          plyr_1.8.8             
## [79] R6_2.5.1                zlibbioc_1.47.0