We illustrate the GenomicOZone package with an example. The Escherichia coli transcriptome data set (Gault and Rodrigue 2016) contains transcriptome profiles of E. coli K-12 under nickel stress, with three samples under exposure to nickel and three normal samples.
To run this example, two other packages GEOquery (Davis and Meltzer 2007) for data download and readxl for Excel file reading are required and should have been installed, in addition to GenomicOZone.
The following code chunk reads a data transcriptome set, prepares genome annotation, and creates parameters before outstanding zone analysis. Specifically, an Escherichia coli transcriptome data set of series number GSE76167 was downloaded from GEO database (Edgar, Domrachev, and Lash 2002) and saved in the package. We accessed the data and extracted the RPKM values from six samples including three stressed samples and three wild-type samples. The genome annotation is prepared as a GRanges (Lawrence et al. 2013) object.
# Obtain data matrix from GSE76167 supplementary file
# invisible(getGEOSuppFiles("GSE76167"))
# data <- read_excel(".//GSE76167_GeneFPKM_AllSamples.xlsx")
file <- system.file("extdata", "GSE76167_GeneFPKM_AllSamples.xlsx", package = "GenomicOZone", mustWork = TRUE)
data <- read_excel(file)
# Adjust the input data
data.info <- data[,1:5]
data <- data[,-c(1:5)]
data <- data[,substr(colnames(data), 1, 4) == "FPKM"]
data <- data.matrix(data[,c(1,5,6,3,4,2)])
colnames(data) <- c(paste(rep("WT",3), "_", c(1,2,3), sep=""),
paste(rep("Ni",3), "_", c(1,2,3), sep=""))
rownames(data) <- data.info$tracking_id
# Obtain genes
data.genes <- data.info$gene_short_name
data.genes[data.genes == "-"] <- data.info$tracking_id[data.genes == "-"]
# Create colData
colData <- data.frame(Sample_name = colnames(data),
Condition = factor(rep(c("WT", "Ni"), each = 3),
levels = c("WT", "Ni")))
# Create design
design <- ~ Condition
# Create rowData.GRanges
pattern <- "(.[^\\:]*)\\:([0-9]+)\\-([0-9]+)"
matched <- regexec(pattern, as.character(data.info$locus))
values <- regmatches(as.character(data.info$locus), matched)
data.gene.coor <- data.frame(chr = as.character(sapply(values, function(x){x[[2]]})),
start = as.numeric(sapply(values, function(x){x[[3]]})),
end = as.numeric(sapply(values, function(x){x[[4]]})))
rownames(data.gene.coor) <- as.character(data.info$tracking_id)
rowData.GRanges <- GRanges(seqnames = data.gene.coor$chr,
IRanges(start = data.gene.coor$start,
end = data.gene.coor$end),
Gene.name = data.genes)
names(rowData.GRanges) <- data.info$tracking_id
chr.size <- 4646332
names(chr.size) <- "NC_007779"
seqlevels(rowData.GRanges) <- names(chr.size)
seqlengths(rowData.GRanges) <- chr.sizeWith the formatted data, parameters, and annotation, we run the outstanding zone analysis as below:
# Create an input object also checking for data format, consistency, and completeness
GOZ.ds <- GOZDataSet(data = data,
colData = colData,
design = design,
rowData.GRanges = rowData.GRanges)
# Run the outstanding zone analysis
GOZ.ds <- GenomicOZone(GOZ.ds)The following four auxiliary functions extract gene annotation, zone annotation, outstanding zone annotation, and zone expression matrix, respectively:
# Extract gene/zone GRanges object
Gene.GRanges <- extract_genes(GOZ.ds)
head(Gene.GRanges)
#> GRanges object with 6 ranges and 2 metadata columns:
#> seqnames ranges strand | Gene.name zone
#> <Rle> <IRanges> <Rle> | <character> <factor>
#> gene0 NC_007779 189-255 * | thrL NC_007779_1
#> gene1 NC_007779 336-2799 * | thrA NC_007779_1
#> gene2 NC_007779 2800-3733 * | thrB NC_007779_1
#> gene3 NC_007779 3733-5020 * | thrC NC_007779_1
#> gene4 NC_007779 5233-5530 * | yaaX NC_007779_1
#> gene5 NC_007779 5682-6459 * | yaaA NC_007779_1
#> -------
#> seqinfo: 1 sequence from an unspecified genome
Zone.GRanges <- extract_zones(GOZ.ds)
head(Zone.GRanges)
#> GRanges object with 6 ranges and 3 metadata columns:
#> seqnames ranges strand | zone p.value.adj
#> <Rle> <IRanges> <Rle> | <factor> <numeric>
#> NC_007779_1 NC_007779 1-9926 * | NC_007779_1 9.80875e-03
#> NC_007779_2 NC_007779 9927-20813 * | NC_007779_2 9.46349e-07
#> NC_007779_3 NC_007779 20814-42401 * | NC_007779_3 2.77431e-01
#> NC_007779_4 NC_007779 42402-72227 * | NC_007779_4 1.92004e-01
#> NC_007779_5 NC_007779 72228-88026 * | NC_007779_5 2.36520e-04
#> NC_007779_6 NC_007779 88027-99642 * | NC_007779_6 4.36277e-01
#> effect.size
#> <numeric>
#> NC_007779_1 0.1440329
#> NC_007779_2 0.3000733
#> NC_007779_3 0.0142269
#> NC_007779_4 0.0146438
#> NC_007779_5 0.1749271
#> NC_007779_6 0.0137143
#> -------
#> seqinfo: 1 sequence from an unspecified genome
# min.effect.size = 0.36 is chosen from the
# minumum of top 5% effect size values
OZone.GRanges <- extract_outstanding_zones(
GOZ.ds,
alpha = 0.05,
min.effect.size = 0.36)
head(OZone.GRanges)
#> GRanges object with 6 ranges and 3 metadata columns:
#> seqnames ranges strand | zone p.value.adj
#> <Rle> <IRanges> <Rle> | <factor> <numeric>
#> NC_007779_13 NC_007779 194902-211875 * | NC_007779_13 2.43544e-08
#> NC_007779_28 NC_007779 452812-467605 * | NC_007779_28 2.43544e-08
#> NC_007779_29 NC_007779 467606-478589 * | NC_007779_29 1.93940e-11
#> NC_007779_32 NC_007779 505826-535808 * | NC_007779_32 5.59306e-18
#> NC_007779_36 NC_007779 590163-605172 * | NC_007779_36 2.67853e-08
#> NC_007779_65 NC_007779 1059677-1072591 * | NC_007779_65 3.27949e-15
#> effect.size
#> <numeric>
#> NC_007779_13 0.369738
#> NC_007779_28 0.369738
#> NC_007779_29 0.512169
#> NC_007779_32 0.400091
#> NC_007779_36 0.419441
#> NC_007779_65 0.566764
#> -------
#> seqinfo: 1 sequence from an unspecified genome
Zone.exp.mat <- extract_zone_expression(GOZ.ds)
head(Zone.exp.mat)
#> WT_1 WT_2 WT_3 Ni_1 Ni_2 Ni_3
#> NC_007779_1 62214.043 83959.120 73790.007 47984.066 55259.507 49647.909
#> NC_007779_2 6247.521 21137.231 24602.949 28791.985 28981.303 33428.900
#> NC_007779_3 7900.263 9331.010 9789.358 2364.148 2515.822 2508.266
#> NC_007779_4 3943.298 4319.107 4270.070 5037.490 6642.837 6111.983
#> NC_007779_5 9095.239 8746.648 9593.191 10926.196 12249.913 14521.159
#> NC_007779_6 2108.727 2016.687 1815.983 2510.306 2378.745 2172.734Three types of plot can be generated from the returned object by GenomicOZone(), including genome-wide overview, within-chromosome heatmap, and within-zone expression. The plots are generated using R package ggplot2 (Wickham 2016) and ggbio (Yin, Cook, and Lawrence 2012). The value of min.effect.size = 0.36 is chosen from the minumum of top 5% effect size values. The effect size for ANOVA is calculated by R package sjstats (Lüdecke 2019).
# Genome-wide overview
plot_genome(GOZ.ds, plot.file = "E_coli_genome.pdf",
plot.width = 15, plot.height = 4,
alpha = 0.05, min.effect.size = 0.36)
#> Warning in (function (mapping = NULL, data = NULL, stat = "identity", position
#> = "identity", : Ignoring unknown parameters: `fill`
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.
#> NULL
knitr::include_graphics("E_coli_genome.pdf")# Within-chromosome heatmap
plot_chromosomes(GOZ.ds, plot.file = "E_coli_chromosome.pdf",
plot.width = 20, plot.height = 4,
alpha = 0.05, min.effect.size = 0.36)
#> png
#> 2
knitr::include_graphics("E_coli_chromosome.pdf")# Within-zone expression
plot_zones(GOZ.ds, plot.file = "E_coli_zone.pdf",
plot.all.zones = FALSE,
alpha = 0.05, min.effect.size = 0.36)
#> Warning: The `fun.y` argument of `stat_summary()` is deprecated as of ggplot2 3.3.0.
#> ℹ Please use the `fun` argument instead.
#> ℹ The deprecated feature was likely used in the GenomicOZone package.
#> Please report the issue to the authors.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> png
#> 2
knitr::include_graphics("E_coli_zone.pdf")Davis, Sean, and Paul S Meltzer. 2007. “GEOquery: A Bridge Between the Gene Expression Omnibus (Geo) and Bioconductor.” Bioinformatics 23 (14): 1846–7.
Edgar, Ron, Michael Domrachev, and Alex E Lash. 2002. “Gene Expression Omnibus: NCBI Gene Expression and Hybridization Array Data Repository.” Nucleic Acids Research 30 (1): 207–10.
Gault, Manon, and Agnès Rodrigue. 2016. “Data Set for Transcriptome Analysis of Escherichia Coli Exposed to Nickel.” Data in Brief 9: 314–17.
Lawrence, Michael, Wolfgang Huber, Hervé Pages, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T Morgan, and Vincent J Carey. 2013. “Software for Computing and Annotating Genomic Ranges.” PLoS Computational Biology 9 (8): e1003118.
Lüdecke, Daniel. 2019. Sjstats: Statistical Functions for Regression Models (Version 0.17.5). https://doi.org/10.5281/zenodo.1284472.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
Yin, Tengfei, Dianne Cook, and Michael Lawrence. 2012. “Ggbio: An R Package for Extending the Grammar of Graphics for Genomic Data.” Genome Biology 13 (8): R77.