2022-Mar-14_binned-interval-gendist-analysis-Fancm-ms
Ruqian Lyu
3/14/2022
Last updated: 2022-03-14
Checks: 5 1
Knit directory: yeln_2019_spermtyping/
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scCNV versus BC1F1 bulk sequencing samples
savePNG <- function(plot_p, saveToFile, dpi=300, fig.width = 14,
fig.height =10,
bothPngPDF= FALSE){
png(saveToFile, width = fig.width,
height = fig.height, units = "in",
pointsize = 12, res = dpi)
print(plot_p)
dev.off()
}scCNV <- readRDS(file = "~/Projects/rejy_2020_single-sperm-co-calling/output/outputR/analysisRDS/countsAll-settings4.3-scCNV-CO-counts_07-mar-2022.rds")scCNV by Fancm genotype
x <- c("mutant","mutant","wildtype","mutant",
"wildtype","wildtype")
xx <- c("Fancm-/-","Fancm-/-","Fancm+/+","Fancm-/-",
"Fancm+/+","Fancm+/+")
scCNV$sampleType <- plyr::mapvalues(scCNV$sampleGroup,from = c("WC_522",
"WC_526",
"WC_CNV_42",
"WC_CNV_43",
"WC_CNV_44",
"WC_CNV_53"),
to =xx)
scCNV_dist_type <- calGeneticDist(scCNV,group_by = "sampleType")
colSums(as.matrix(rowData(scCNV_dist_type)$kosambi))Fancm-/- Fancm+/+
1387.336 1227.412 Bulk bc1f1
bc1f1_samples <- readRDS(file = "output/outputR/analysisRDS/all_rse_count_07-20.rds")bc1f1_samples_dist <- calGeneticDist(bc1f1_samples,group_by = "sampleGroup" )
bc1f1_samples_dist_male <- calGeneticDist(bc1f1_samples[,bc1f1_samples$sampleGroup %in%
c("Male_HET","Male_WT","Male_KO")],
group_by = "sampleGroup" )
bc1f1_samples_dist_female <- calGeneticDist(bc1f1_samples[,bc1f1_samples$sampleGroup %in%
c("Female_HET","Female_WT","Female_KO")],group_by = "sampleGroup")scCNV_dist_bin_dist <- calGeneticDist(scCNV,group_by = "sampleType",bin_size = 1e7)
bc1f1_samples_dist_male_bin_dist <- calGeneticDist(bc1f1_samples[,bc1f1_samples$sampleGroup %in%
c("Male_HET","Male_WT","Male_KO")],
bin_size = 1e7,
group_by = "sampleGroup")
bc1f1_samples_dist_bin_dist <- calGeneticDist(bc1f1_samples,
bin_size = 1e7,
group_by = "sampleGroup")
combined_bin_dist <- scCNV_dist_bin_dist
mcols(combined_bin_dist ) <- cbind(mcols(scCNV_dist_bin_dist),
apply(mcols(bc1f1_samples_dist_male_bin_dist),
2,function(x) (-1)*x))plotGeneticDist(combined_bin_dist,cumulative = F,chr = "chr8")+
scale_color_manual("sampleType",
labels = c("mutant"= "single sperm KO", "wildtype"="single sperm WT",
"Male_KO" = "male KO" , "Male_WT" = "male WT" ),
values = c("mutant" = "#2b2d42",
"wildtype" = "#d90429",
"Male_KO" = "#8d99ae",
"Male_WT" = "#e75466",
"Male_HET" = "#76797a"))Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.
# values = c("mutant" = "#2b2d42",
# "wildtype" = "#d90429",
# "Male_KO" = "#8d99ae",
# "Male_WT" = "#e75466",
# "Male_HET" = "#76797a")
chr_cums <- list()
for(chr in paste0("chr",c(1:19)) ){
suppressMessages(
chr_cums[[chr]] <-
plotGeneticDist(combined_bin_dist,cumulative = F,chr =chr)+
scale_color_manual("sampleType",
labels = c("Fancm-/-"= "single sperm KO",
"Fancm+/+"="single sperm WT",
"Male_KO" = "male KO" ,
"Male_WT" = "male WT" ),
values = c("Fancm-/-" = "cornflowerblue",
"Fancm+/+" = "tan1",
"Male_KO" = "cornflowerblue",
"Male_WT" = "tan1",
"Male_HET" = "grey50"))+geom_hline(yintercept = 0,
linetype = "dashed",alpha=0.4)+
scale_y_continuous(breaks = c(-15,-10,-5,0,5,10,15,20),
labels = c("15","10","5","0","5","10","15","20"))+
scale_x_continuous(labels = scales::unit_format(unit = "M",
scale = 1e-06),limits = c(0,196e6),
breaks= c(0,50e6,100e6,150e6,195e6))+
guides(color="none")+xlab("")+ylab("")+
theme(panel.grid.minor = element_line(colour = "grey", size = 0.2),
panel.grid.major = element_blank(),
plot.margin = margin(t=10,r=15),
strip.text.x = element_text(size =22)))
}scCNV versus BC1F1 bulk sequencing samples across all chromosomes
mChrThresPlotsscCNV_bc1f1male <- marrangeGrob(chr_cums, nrow=7, ncol=3,
layout_matrix = matrix(c(1:19,NA,NA),
nrow=7,byrow = T),
top = textGrob(" "),
bottom = textGrob("Chromosome positions",gp = gpar(fontsize = 25)), left = textGrob("CentiMorgans per 10 megabase",
gp = gpar(fontsize = 25),
rot = 90))
mChrThresPlotsscCNV_bc1f1male
savePNG
scCNV versus BC1F1 bulk sequencing samples 4 chromosomes
Up: scCNV
Down: BC1F1 bulk sequencing
plotGeneticDistCustmise <- function (gr, bin = TRUE, chr = NULL, cumulative = FALSE,
line_size = 2)
{
col_to_plot <- colnames(GenomicRanges::mcols(gr))
sample_group_colors <- RColorBrewer::brewer.pal(ifelse(length(col_to_plot) >
2, length(col_to_plot), 3), name = "Set1")
names(sample_group_colors)[seq_along(col_to_plot)] <- col_to_plot
if (cumulative) {
GenomicRanges::mcols(gr) <- apply(mcols(gr), 2, function(x,
seq = as.character(seqnames(gr))) {
temp_df <- data.frame(x = x, seq = seq) %>% dplyr::group_by(seq) %>%
dplyr::mutate(cum = cumsum(x))
temp_df$cum
})
}
plot_df <- data.frame(gr)
colnames(plot_df)[(ncol(plot_df) - length(col_to_plot) +
1):ncol(plot_df)] <- col_to_plot
plot_df <- plot_df %>% dplyr::mutate(x_tick = 0.5 * (.data$start +
.data$end))
plot_df <- plot_df %>% tidyr::pivot_longer(cols = col_to_plot,
names_to = "SampleGroup", values_to = "bin_dist")
x_tick <- bin_dist <- end <- SampleGroup <- NULL
if (is.null(chr)) {
p <- plot_df %>% ggplot() + geom_step(mapping = aes(x = x_tick,
y = bin_dist, color = SampleGroup), size = line_size)
}
else {
p <- plot_df %>% dplyr::filter(seqnames %in% chr) %>%
ggplot() + geom_step(mapping = aes(x = end, y = bin_dist,
color = SampleGroup), size = line_size)
}
p <- p + scale_x_continuous(labels = scales::unit_format(unit = "M",
scale = 1e-06)) + facet_wrap(. ~ seqnames, ncol = 1,
scales = "free") + theme_classic(base_size = 18) + xlab("Chromosome positions") +
scale_color_manual(values = sample_group_colors)
if (cumulative) {
p + ylab("cumulative centiMorgans")
}
else {
p + ylab("centiMorgans")
}
}Previously selected chromosomes 8, 9, 11, 18
chr_cums <- list()
for(chr in paste0("chr",c(8,9,11,18))){
suppressMessages(
chr_cums[[chr]] <-
plotGeneticDistCustmise(combined_bin_dist,cumulative = F,chr =chr, line_size = 2)+
scale_color_manual("sampleType",
labels = c("Fancm-/-"= "single sperm KO",
"Fancm+/+"="single sperm WT",
"Male_KO" = "male KO" ,
"Male_WT" = "male WT" ),
values = c("Fancm-/-" = "cornflowerblue",
"Fancm+/+" = "tan1",
"Male_KO" = "cornflowerblue",
"Male_WT" = "tan1",
"Male_HET" = "grey50"))+geom_hline(yintercept = 0,
linetype = "dashed",alpha=0.4)+
scale_y_continuous(breaks = c(-15,-10,-5,0,5,10,15,20),
labels = c("15","10","5","0","5","10","15","20"))+
scale_x_continuous(labels = scales::unit_format(unit = "M",
scale = 1e-06),
limits = c(0,196e6),
breaks= c(0,50e6,100e6,150e6,195e6))+
guides(color="none")+xlab("")+ylab("")+
theme(panel.grid.minor = element_line(colour = "grey", size = 0.2),
panel.grid.major = element_blank(),
strip.text.x = element_text(size = 22),
axis.text.y = element_text(size = 25),
axis.text.x = element_text(size = 23),
axis.title = element_text(size = 25)))
}# arg_mchrs <- arrangeGrob(chr_cums$chr8+theme(strip.text = element_text(size = 20),
# axis.text = element_text(size = 20),
# axis.title.x = element_blank(),
# plot.margin = margin(t=10,r=15)),
# chr_cums$chr9+theme(strip.text = element_text(size = 22),
# axis.text = element_text(size = 20),
# axis.title.x = element_blank(),
# plot.margin = margin(t=10,r=15)),
# chr_cums$chr11+theme(strip.text = element_text(size = 22),
# axis.text = element_text(size = 20),
# plot.margin = margin(t=10,r=15)),
# chr_cums$chr18+theme(strip.text = element_text(size = 22),
# axis.text = element_text(size = 25),plot.margin = margin(t=10,r=15)))
#
arg_mchrs <- arrangeGrob(chr_cums$chr8+theme(axis.title.x = element_blank(),
plot.margin = margin(t=10,r=8)),
chr_cums$chr9+theme(axis.title.x = element_blank(),
plot.margin = margin(t=10,r=8)),
chr_cums$chr11+theme(plot.margin = margin(t=10,r=8)),
chr_cums$chr18+theme(plot.margin = margin(t=10,r=8)))
# grid.arrange(arg_mchrs1,left = textGrob("Cumulative centiMorgans",rot = 90,gp = gpar(fontsize=25)),
# bottom = textGrob("Chromosome positions",rot = 0,gp = gpar(fontsize=25)),nrow=1)
#
# grid.arrange(arg_mchrs2,left = textGrob("Cumulative centiMorgans",rot = 90,gp = gpar(fontsize=25)),
# bottom = textGrob("Chromosome positions",rot = 0,gp = gpar(fontsize=25)),nrow =1,ncol=1)
grid.arrange(arg_mchrs,left = textGrob("CentiMorgans per 10 megabase",rot = 90,
gp = gpar(fontsize=25)),
bottom = textGrob("Chromosome positions",rot = 0,
gp = gpar(fontsize=25)))
The current selected chromosomes
chr_cums <- list()
for(chr in paste0("chr",c(1,4,8,11))){
suppressMessages(
chr_cums[[chr]] <-
plotGeneticDistCustmise(combined_bin_dist,cumulative = F,chr =chr, line_size = 2)+
scale_color_manual("sampleType",
labels = c("Fancm-/-"= "single sperm KO",
"Fancm+/+"="single sperm WT",
"Male_KO" = "male KO" ,
"Male_WT" = "male WT" ),
values = c("Fancm-/-" = "cornflowerblue",
"Fancm+/+" = "tan1",
"Male_KO" = "cornflowerblue",
"Male_WT" = "tan1",
"Male_HET" = "grey50"))+geom_hline(yintercept = 0,
linetype = "dashed",alpha=0.4)+
scale_y_continuous(breaks = c(-15,-10,-5,0,5,10,15,20),
labels = c("15","10","5","0","5","10","15","20"))+
scale_x_continuous(labels = scales::unit_format(unit = "M",
scale = 1e-06),limits = c(0,196e6),
breaks= c(0,50e6,100e6,150e6,195e6))+
guides(color="none")+xlab("")+ylab("")+
theme(panel.grid.minor = element_line(colour = "grey", size = 0.2),
panel.grid.major = element_blank(),
strip.text.x = element_text(size = 22),
axis.text.y = element_text(size = 25),
axis.text.x = element_text(size = 23),
axis.title = element_text(size = 25)))
}arg_mchrs <- arrangeGrob(chr_cums$chr1+theme(axis.title.x = element_blank(),
plot.margin = margin(t=10,r=15)),
chr_cums$chr4+theme(axis.title.x = element_blank(),
plot.margin = margin(t=10,r=15)),
chr_cums$chr8+theme(plot.margin = margin(t=10,r=15)),
chr_cums$chr11+theme(plot.margin = margin(t=10,r=15)))
grid.arrange(arg_mchrs,left = textGrob("CentiMorgans per 10 megabase",rot = 90,
gp = gpar(fontsize=25)),
bottom = textGrob("Chromosome positions",rot = 0,
gp = gpar(fontsize=25)))
BC1F1 female versus male
Down: female
Up: male
i = 1
mcols(bc1f1_samples_dist_bin_dist)[,c(2:4)] <- apply(mcols(bc1f1_samples_dist_bin_dist)[,c(2:4)],2,function(x){
(-1)*x
})
plotGeneticDist(bc1f1_samples_dist_bin_dist,cumulative = F,chr = "chr8")+
scale_color_manual("sampleType",
values = c("Female_WT" = "tan1",
"Female_KO" = "cornflowerblue",
"Female_HET" = "grey50",
"Male_KO" = "cornflowerblue",
"Male_WT" = "tan1",
"Male_HET" = "grey50"))+
scale_y_continuous(breaks = c(-15,-10,-5,0,5,10,15,20),
labels = c("15","10","5","0","5","10","15","20"))+
scale_x_continuous(labels = scales::unit_format(unit = "M",
scale = 1e-06),limits = c(0,196e6),
breaks= c(0,50e6,100e6,150e6,195e6))+
guides(color="none")+xlab("")+ylab("")+theme(panel.grid.minor =
element_line(colour = "grey", size = 0.2),
panel.grid.major = element_blank())Scale for 'colour' is already present. Adding another scale for 'colour',
which will replace the existing scale.Scale for 'x' is already present. Adding another scale for 'x', which will
replace the existing scale.
chr_cums <- list()
for(chr in paste0("chr",c(1:19)) ){
suppressMessages(
chr_cums[[chr]] <-
plotGeneticDist(bc1f1_samples_dist_bin_dist,cumulative = F,chr = chr)+
scale_color_manual("sampleType",
values = c("Female_WT" = "tan1",
"Female_KO" = "cornflowerblue",
"Female_HET" = "grey50",
"Male_KO" = "cornflowerblue",
"Male_WT" = "tan1",
"Male_HET" = "grey50"))+geom_hline(yintercept = 0,
linetype = "dashed",alpha=0.4)+
scale_y_continuous(breaks = c(-15,-10,-5,0,5,10,15,20),
labels = c("15","10","5","0","5","10","15","20"))+
scale_x_continuous(labels = scales::unit_format(unit = "M",
scale = 1e-06),limits = c(0,196e6),
breaks= c(0,50e6,100e6,150e6,195e6))+
guides(color="none")+xlab("")+ylab("")+theme(panel.grid.minor =
element_line(colour = "grey", size = 0.2),axis.text.x = element_text(size=rel(0.8)),
panel.grid.major = element_blank(),
plot.margin = margin(t=10,r=15)))
}
mChrThresPlots_female_male <- marrangeGrob(chr_cums, ncol=3,
layout_matrix = matrix(c(1:19,NA,NA),nrow=7,byrow = T),
top = textGrob(" "),
bottom = textGrob("Chromosome positions",gp = gpar(fontsize = 25)), left = textGrob("CentiMorgans per 10 megabase",
gp = gpar(fontsize = 25),
rot = 90))
mChrThresPlots_female_male
savePNG
Permutation for testing differences in genetic distances in each chromsome bin
Permutation is performed by permuting the sample type labels among the single cells or bulk samples and caculate the differences in genetic distances between sample groups.
register(BPPARAM = MulticoreParam(workers = 10))
permuteSampleType <- function(co_count, B = 1000, bin_size = 1e7,
permuteCol = "sampleType"){
len_1 <- table(colData(co_count)[,permuteCol])[1]
bbl <- bplapply(1:B, function(x){
permutedCoCount <- co_count
type1Idx <- sample(seq(ncol(permutedCoCount)),len_1)
type2Idx <- setdiff(seq(ncol(permutedCoCount)),type1Idx )
permutedCoCount$sampleType[type1Idx] <- names(table(permutedCoCount$sampleType))[1]
permutedCoCount$sampleType[type2Idx] <- names(table(permutedCoCount$sampleType))[2]
permutedCoCount_dist_bin_dist <- calGeneticDist(permutedCoCount,group_by = permuteCol,
bin_size = bin_size)
mcols(permutedCoCount_dist_bin_dist)
})
observed_dist_bin_dist <- calGeneticDist(co_count,group_by = permuteCol,
bin_size = bin_size)
observed_dist_bin_diff <- mcols(observed_dist_bin_dist)[,1] - mcols(observed_dist_bin_dist)[,2]
mt_scnv_permute <- sapply(bbl,function(x){x[,2]})
wt_scnv_permute <- sapply(bbl,function(x){x[,1]})
permute_statistic <- mt_scnv_permute - wt_scnv_permute
permute_statistic <- rowSums(permute_statistic >= observed_dist_bin_diff)
permute_pvals <- permp(permute_statistic,nperm = B,n1 =len_1,n2 = ncol(co_count)-len_1)
temp_gr <- observed_dist_bin_dist
mcols(temp_gr) <- permute_pvals
temp_gr
}scCNV_pval_bins <- permuteSampleType(co_count = scCNV)hist(scCNV_pval_bins$X)
After multiple testing correction:
scCNV_pval_bins_adj <- p.adjust(mcols(scCNV_pval_bins)[,1],"fdr")
mcols(scCNV_pval_bins) <- data.frame(scCNV_p.adj = scCNV_pval_bins_adj)There is no bins that was detected as having significant differences in genetic distances in single sperm dataset.
any(scCNV_pval_bins$scCNV_p.adj<0.05)[1] FALSEbc1f1_samples_dist_male_2groups <- bc1f1_samples_dist_male
bc1f1_samples_dist_female_2groups <- bc1f1_samples_dist_female
bc1f1_samples_dist_male_2groups$sampleType <- plyr::mapvalues(bc1f1_samples_dist_male_2groups$sampleGroup,
from = c("Male_HET","Male_WT","Male_KO"),
to = c("Fancm+/*","Fancm+/*","Fancm-/-") )
bc1f1_samples_dist_female_2groups$sampleType <- plyr::mapvalues(bc1f1_samples_dist_female_2groups$sampleGroup,
from = c("Female_HET","Female_WT","Female_KO"),
to = c("Fancm+/*","Fancm+/*","Fancm-/-") )
bc1f1_samples_dist_female_2groups_pval_bins <- permuteSampleType(co_count = bc1f1_samples_dist_female_2groups)
bc1f1_male_samples_2groups_pval_bins <- permuteSampleType(co_count = bc1f1_samples_dist_male_2groups)
hist(bc1f1_male_samples_2groups_pval_bins$X)
hist(bc1f1_samples_dist_female_2groups_pval_bins$X)
bc1f1_male_samples_2groups_pval_bins_adj <- p.adjust(mcols(bc1f1_male_samples_2groups_pval_bins)[,1],"fdr")
mcols(bc1f1_male_samples_2groups_pval_bins) <- data.frame(bulkBC1F1Male_p.adj = bc1f1_male_samples_2groups_pval_bins_adj)
bc1f1_female_samples_2groups_pval_bins_adj <- p.adjust(mcols(bc1f1_samples_dist_female_2groups_pval_bins)[,1],"fdr")
mcols(bc1f1_samples_dist_female_2groups_pval_bins) <- data.frame(bulkBC1F1Female_p.adj = bc1f1_female_samples_2groups_pval_bins_adj)Bins that show significant difference between Fancm -/- and Fancm +/+ in scCNV:
hist(scCNV_pval_bins_adj)
any(scCNV_pval_bins_adj<0.05)[1] FALSEBins that show significant differences between Fancm -/- and Fancm +/* in bc1f1 males:
hist(bc1f1_male_samples_2groups_pval_bins_adj)
any(bc1f1_male_samples_2groups_pval_bins_adj<0.05)[1] FALSEBins that show significant differences between Fancm -/- and Fancm +/* in bc1f1 males:
hist(bc1f1_female_samples_2groups_pval_bins_adj)
any(bc1f1_female_samples_2groups_pval_bins_adj<0.05)[1] FALSEHighlight bins with sig. differences between sample groups
From above histogram plot, there is no significant differences in genetic distances in each chromosome bin between two sample groups in scCNV or Bulk samples.
Sessioninfo
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Rocky Linux 8.5 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.12.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] statmod_1.4.36 BiocParallel_1.28.3
[3] gridExtra_2.3 SummarizedExperiment_1.24.0
[5] Biobase_2.54.0 GenomicRanges_1.46.1
[7] GenomeInfoDb_1.30.1 IRanges_2.28.0
[9] S4Vectors_0.32.3 BiocGenerics_0.40.0
[11] MatrixGenerics_1.6.0 matrixStats_0.61.0
[13] dplyr_1.0.7 ggplot2_3.3.5
[15] comapr_0.99.43 readxl_1.3.1
loaded via a namespace (and not attached):
[1] backports_1.4.1 circlize_0.4.13 Hmisc_4.6-0
[4] workflowr_1.7.0 BiocFileCache_2.2.1 plyr_1.8.6
[7] lazyeval_0.2.2 splines_4.1.2 digest_0.6.29
[10] foreach_1.5.2 ensembldb_2.18.3 htmltools_0.5.2
[13] fansi_1.0.2 magrittr_2.0.2 checkmate_2.0.0
[16] memoise_2.0.1 BSgenome_1.62.0 cluster_2.1.2
[19] Biostrings_2.62.0 prettyunits_1.1.1 jpeg_0.1-9
[22] colorspace_2.0-2 blob_1.2.2 rappdirs_0.3.3
[25] xfun_0.29 crayon_1.4.2 RCurl_1.98-1.5
[28] jsonlite_1.7.3 survival_3.2-13 VariantAnnotation_1.40.0
[31] iterators_1.0.14 glue_1.6.1 gtable_0.3.0
[34] zlibbioc_1.40.0 XVector_0.34.0 DelayedArray_0.20.0
[37] shape_1.4.6 scales_1.1.1 DBI_1.1.2
[40] Rcpp_1.0.8 viridisLite_0.4.0 progress_1.2.2
[43] htmlTable_2.4.0 foreign_0.8-81 bit_4.0.4
[46] Formula_1.2-4 htmlwidgets_1.5.4 httr_1.4.2
[49] RColorBrewer_1.1-2 ellipsis_0.3.2 farver_2.1.0
[52] pkgconfig_2.0.3 XML_3.99-0.8 Gviz_1.38.3
[55] nnet_7.3-16 dbplyr_2.1.1 utf8_1.2.2
[58] tidyselect_1.1.1 labeling_0.4.2 rlang_1.0.0
[61] reshape2_1.4.4 later_1.3.0 AnnotationDbi_1.56.2
[64] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
[67] cachem_1.0.6 cli_3.1.1 generics_0.1.1
[70] RSQLite_2.2.9 evaluate_0.14 stringr_1.4.0
[73] fastmap_1.1.0 yaml_2.2.2 knitr_1.37
[76] bit64_4.0.5 fs_1.5.2 purrr_0.3.4
[79] KEGGREST_1.34.0 AnnotationFilter_1.18.0 xml2_1.3.3
[82] biomaRt_2.50.3 compiler_4.1.2 rstudioapi_0.13
[85] plotly_4.10.0 filelock_1.0.2 curl_4.3.2
[88] png_0.1-7 tibble_3.1.6 stringi_1.7.6
[91] highr_0.9 GenomicFeatures_1.46.4 lattice_0.20-45
[94] ProtGenerics_1.26.0 Matrix_1.4-0 vctrs_0.3.8
[97] pillar_1.6.5 lifecycle_1.0.1 jquerylib_0.1.4
[100] GlobalOptions_0.1.2 data.table_1.14.2 bitops_1.0-7
[103] httpuv_1.6.5 rtracklayer_1.54.0 R6_2.5.1
[106] BiocIO_1.4.0 latticeExtra_0.6-29 promises_1.2.0.1
[109] codetools_0.2-18 dichromat_2.0-0 assertthat_0.2.1
[112] rprojroot_2.0.2 rjson_0.2.21 withr_2.4.3
[115] GenomicAlignments_1.30.0 Rsamtools_2.10.0 GenomeInfoDbData_1.2.7
[118] parallel_4.1.2 hms_1.1.1 rpart_4.1-15
[121] tidyr_1.2.0 rmarkdown_2.11 git2r_0.29.0
[124] biovizBase_1.42.0 base64enc_0.1-3 restfulr_0.0.13
devtools::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.1.2 (2021-11-01)
os Rocky Linux 8.5 (Green Obsidian)
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Melbourne
date 2022-03-14
pandoc 2.11.4 @ /usr/lib/rstudio-server/bin/pandoc/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
AnnotationDbi 1.56.2 2021-11-09 [1] Bioconductor
AnnotationFilter 1.18.0 2021-10-26 [1] Bioconductor
assertthat 0.2.1 2019-03-21 [1] CRAN (R 4.1.2)
backports 1.4.1 2021-12-13 [1] CRAN (R 4.1.2)
base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.1.2)
Biobase * 2.54.0 2021-10-26 [1] Bioconductor
BiocFileCache 2.2.1 2022-01-23 [1] Bioconductor
BiocGenerics * 0.40.0 2021-10-26 [1] Bioconductor
BiocIO 1.4.0 2021-10-26 [1] Bioconductor
BiocParallel * 1.28.3 2021-12-09 [1] Bioconductor
biomaRt 2.50.3 2022-02-03 [1] Bioconductor
Biostrings 2.62.0 2021-10-26 [1] Bioconductor
biovizBase 1.42.0 2021-10-26 [1] Bioconductor
bit 4.0.4 2020-08-04 [1] CRAN (R 4.1.2)
bit64 4.0.5 2020-08-30 [1] CRAN (R 4.1.2)
bitops 1.0-7 2021-04-24 [1] CRAN (R 4.1.2)
blob 1.2.2 2021-07-23 [1] CRAN (R 4.1.2)
brio 1.1.3 2021-11-30 [1] CRAN (R 4.1.0)
BSgenome 1.62.0 2021-10-26 [1] Bioconductor
cachem 1.0.6 2021-08-19 [1] CRAN (R 4.1.0)
callr 3.7.0 2021-04-20 [1] CRAN (R 4.1.2)
cellranger 1.1.0 2016-07-27 [1] CRAN (R 4.1.2)
checkmate 2.0.0 2020-02-06 [1] CRAN (R 4.1.0)
circlize 0.4.13 2021-06-09 [1] CRAN (R 4.1.0)
cli 3.1.1 2022-01-20 [1] CRAN (R 4.1.2)
cluster 2.1.2 2021-04-17 [2] CRAN (R 4.1.2)
codetools 0.2-18 2020-11-04 [2] CRAN (R 4.1.2)
colorspace 2.0-2 2021-06-24 [1] CRAN (R 4.1.2)
comapr * 0.99.43 2022-03-09 [1] Github (ruqianl/comapr@915d97c)
crayon 1.4.2 2021-10-29 [1] CRAN (R 4.1.2)
curl 4.3.2 2021-06-23 [1] CRAN (R 4.1.2)
data.table 1.14.2 2021-09-27 [1] CRAN (R 4.1.2)
DBI 1.1.2 2021-12-20 [1] CRAN (R 4.1.2)
dbplyr 2.1.1 2021-04-06 [1] CRAN (R 4.1.2)
DelayedArray 0.20.0 2021-10-26 [1] Bioconductor
desc 1.4.0 2021-09-28 [1] CRAN (R 4.1.0)
devtools 2.4.3 2021-11-30 [1] CRAN (R 4.1.0)
dichromat 2.0-0 2013-01-24 [1] CRAN (R 4.1.0)
digest 0.6.29 2021-12-01 [1] CRAN (R 4.1.2)
dplyr * 1.0.7 2021-06-18 [1] CRAN (R 4.1.2)
ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.1.2)
ensembldb 2.18.3 2022-01-13 [1] Bioconductor
evaluate 0.14 2019-05-28 [1] CRAN (R 4.1.2)
fansi 1.0.2 2022-01-14 [1] CRAN (R 4.1.2)
farver 2.1.0 2021-02-28 [1] CRAN (R 4.1.2)
fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.1.2)
filelock 1.0.2 2018-10-05 [1] CRAN (R 4.1.0)
foreach 1.5.2 2022-02-02 [1] CRAN (R 4.1.0)
foreign 0.8-81 2020-12-22 [2] CRAN (R 4.1.2)
Formula 1.2-4 2020-10-16 [1] CRAN (R 4.1.0)
fs 1.5.2 2021-12-08 [1] CRAN (R 4.1.2)
generics 0.1.1 2021-10-25 [1] CRAN (R 4.1.2)
GenomeInfoDb * 1.30.1 2022-01-30 [1] Bioconductor
GenomeInfoDbData 1.2.7 2022-01-28 [1] Bioconductor
GenomicAlignments 1.30.0 2021-10-26 [1] Bioconductor
GenomicFeatures 1.46.4 2022-01-20 [1] Bioconductor
GenomicRanges * 1.46.1 2021-11-18 [1] Bioconductor
ggplot2 * 3.3.5 2021-06-25 [1] CRAN (R 4.1.2)
git2r 0.29.0 2021-11-22 [1] CRAN (R 4.1.2)
GlobalOptions 0.1.2 2020-06-10 [1] CRAN (R 4.1.0)
glue 1.6.1 2022-01-22 [1] CRAN (R 4.1.2)
gridExtra * 2.3 2017-09-09 [1] CRAN (R 4.1.0)
gtable 0.3.0 2019-03-25 [1] CRAN (R 4.1.2)
Gviz 1.38.3 2022-01-23 [1] Bioconductor
highr 0.9 2021-04-16 [1] CRAN (R 4.1.2)
Hmisc 4.6-0 2021-10-07 [1] CRAN (R 4.1.0)
hms 1.1.1 2021-09-26 [1] CRAN (R 4.1.2)
htmlTable 2.4.0 2022-01-04 [1] CRAN (R 4.1.0)
htmltools 0.5.2 2021-08-25 [1] CRAN (R 4.1.2)
htmlwidgets 1.5.4 2021-09-08 [1] CRAN (R 4.1.0)
httpuv 1.6.5 2022-01-05 [1] CRAN (R 4.1.2)
httr 1.4.2 2020-07-20 [1] CRAN (R 4.1.2)
IRanges * 2.28.0 2021-10-26 [1] Bioconductor
iterators 1.0.14 2022-02-05 [1] CRAN (R 4.1.0)
jpeg 0.1-9 2021-07-24 [1] CRAN (R 4.1.0)
jquerylib 0.1.4 2021-04-26 [1] CRAN (R 4.1.2)
jsonlite 1.7.3 2022-01-17 [1] CRAN (R 4.1.2)
KEGGREST 1.34.0 2021-10-26 [1] Bioconductor
knitr 1.37 2021-12-16 [1] CRAN (R 4.1.0)
labeling 0.4.2 2020-10-20 [1] CRAN (R 4.1.2)
later 1.3.0 2021-08-18 [1] CRAN (R 4.1.0)
lattice 0.20-45 2021-09-22 [2] CRAN (R 4.1.2)
latticeExtra 0.6-29 2019-12-19 [1] CRAN (R 4.1.0)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 4.1.0)
lifecycle 1.0.1 2021-09-24 [1] CRAN (R 4.1.2)
magrittr 2.0.2 2022-01-26 [1] CRAN (R 4.1.2)
Matrix 1.4-0 2021-12-08 [1] CRAN (R 4.1.2)
MatrixGenerics * 1.6.0 2021-10-26 [1] Bioconductor
matrixStats * 0.61.0 2021-09-17 [1] CRAN (R 4.1.2)
memoise 2.0.1 2021-11-26 [1] CRAN (R 4.1.0)
munsell 0.5.0 2018-06-12 [1] CRAN (R 4.1.2)
nnet 7.3-16 2021-05-03 [2] CRAN (R 4.1.2)
pillar 1.6.5 2022-01-25 [1] CRAN (R 4.1.2)
pkgbuild 1.3.1 2021-12-20 [1] CRAN (R 4.1.0)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.1.2)
pkgload 1.2.4 2021-11-30 [1] CRAN (R 4.1.0)
plotly 4.10.0 2021-10-09 [1] CRAN (R 4.1.0)
plyr 1.8.6 2020-03-03 [1] CRAN (R 4.1.0)
png 0.1-7 2013-12-03 [1] CRAN (R 4.1.0)
prettyunits 1.1.1 2020-01-24 [1] CRAN (R 4.1.2)
processx 3.5.2 2021-04-30 [1] CRAN (R 4.1.2)
progress 1.2.2 2019-05-16 [1] CRAN (R 4.1.2)
promises 1.2.0.1 2021-02-11 [1] CRAN (R 4.1.0)
ProtGenerics 1.26.0 2021-10-26 [1] Bioconductor
ps 1.6.0 2021-02-28 [1] CRAN (R 4.1.2)
purrr 0.3.4 2020-04-17 [1] CRAN (R 4.1.2)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.1.2)
rappdirs 0.3.3 2021-01-31 [1] CRAN (R 4.1.2)
RColorBrewer 1.1-2 2014-12-07 [1] CRAN (R 4.1.2)
Rcpp 1.0.8 2022-01-13 [1] CRAN (R 4.1.2)
RCurl 1.98-1.5 2021-09-17 [1] CRAN (R 4.1.0)
readxl * 1.3.1 2019-03-13 [1] CRAN (R 4.1.2)
remotes 2.4.2 2021-11-30 [1] CRAN (R 4.1.0)
reshape2 1.4.4 2020-04-09 [1] CRAN (R 4.1.0)
restfulr 0.0.13 2017-08-06 [1] CRAN (R 4.1.0)
rjson 0.2.21 2022-01-09 [1] CRAN (R 4.1.0)
rlang 1.0.0 2022-01-26 [1] CRAN (R 4.1.2)
rmarkdown 2.11 2021-09-14 [1] CRAN (R 4.1.2)
rpart 4.1-15 2019-04-12 [2] CRAN (R 4.1.2)
rprojroot 2.0.2 2020-11-15 [1] CRAN (R 4.1.0)
Rsamtools 2.10.0 2021-10-26 [1] Bioconductor
RSQLite 2.2.9 2021-12-06 [1] CRAN (R 4.1.0)
rstudioapi 0.13 2020-11-12 [1] CRAN (R 4.1.2)
rtracklayer 1.54.0 2021-10-26 [1] Bioconductor
S4Vectors * 0.32.3 2021-11-21 [1] Bioconductor
scales 1.1.1 2020-05-11 [1] CRAN (R 4.1.2)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.1.0)
shape 1.4.6 2021-05-19 [1] CRAN (R 4.1.0)
statmod * 1.4.36 2021-05-10 [1] CRAN (R 4.1.2)
stringi 1.7.6 2021-11-29 [1] CRAN (R 4.1.0)
stringr 1.4.0 2019-02-10 [1] CRAN (R 4.1.0)
SummarizedExperiment * 1.24.0 2021-10-26 [1] Bioconductor
survival 3.2-13 2021-08-24 [2] CRAN (R 4.1.2)
testthat 3.1.2 2022-01-20 [1] CRAN (R 4.1.0)
tibble 3.1.6 2021-11-07 [1] CRAN (R 4.1.2)
tidyr 1.2.0 2022-02-01 [1] CRAN (R 4.1.0)
tidyselect 1.1.1 2021-04-30 [1] CRAN (R 4.1.2)
usethis 2.1.5 2021-12-09 [1] CRAN (R 4.1.0)
utf8 1.2.2 2021-07-24 [1] CRAN (R 4.1.2)
VariantAnnotation 1.40.0 2021-10-26 [1] Bioconductor
vctrs 0.3.8 2021-04-29 [1] CRAN (R 4.1.2)
viridisLite 0.4.0 2021-04-13 [1] CRAN (R 4.1.2)
withr 2.4.3 2021-11-30 [1] CRAN (R 4.1.2)
workflowr 1.7.0 2021-12-21 [1] CRAN (R 4.1.2)
xfun 0.29 2021-12-14 [1] CRAN (R 4.1.2)
XML 3.99-0.8 2021-09-17 [1] CRAN (R 4.1.0)
xml2 1.3.3 2021-11-30 [1] CRAN (R 4.1.0)
XVector 0.34.0 2021-10-26 [1] Bioconductor
yaml 2.2.2 2022-01-25 [1] CRAN (R 4.1.2)
zlibbioc 1.40.0 2021-10-26 [1] Bioconductor
[1] /mnt/beegfs/mccarthy/backed_up/general/rlyu/Software/Rlibs/4.1
[2] /opt/R/4.1.2/lib/R/library
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