Last updated: 2021-02-15
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Knit directory: Mouse_AAV_PGR_RNAseq_bulk/
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In the GEO submission 2 processed files were uploaded.
They have been uploaded in the /output folder and will be used below to generate different figures.
library(edgeR)
Loading required package: limma
library(limma)
library(Glimma)
library(gplots)
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
library(ComplexHeatmap)
Loading required package: grid
========================================
ComplexHeatmap version 2.0.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
genomic data. Bioinformatics 2016.
========================================
library(circlize)
========================================
circlize version 0.4.10
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/
If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
in R. Bioinformatics 2014.
This message can be suppressed by:
suppressPackageStartupMessages(library(circlize))
========================================
library(RColorBrewer)
library(mclust)
Package 'mclust' version 5.4.6
Type 'citation("mclust")' for citing this R package in publications.
rm1 <- read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Mouse_AAV_PGR_RNAseq_bulk/output/mrna_fulllen_pe_strrev_q30.mx.AAV_PR.fix_filt", row.names = 1)
colnames(rm1) = gsub("AAV.","",colnames(rm1))
info = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Mouse_AAV_PGR_RNAseq_bulk/output/2020021_AAV_PR_RNA_samplesheet.txt", header = TRUE, sep = "\t")
info$ID = gsub("_",".",info$ID)
names(rm1) = info$ID[match(names(rm1),info$ID)]
m = match(info$ID,names(rm1))
rm2 = rm1[,m]
rm1 = rm2
sampleinfo = info
levels(factor(sampleinfo$Group))
[1] "MCS" "PR"
levels(factor(sampleinfo$BinSex))
[1] "MCS_F" "MCS_M" "PR_F" "PR_M"
sampleinfo$colour = c("springgreen3","purple")[factor(sampleinfo$Group)]
table(colnames(rm2)==sampleinfo$ID)
TRUE
13
y <- DGEList(rm2)
par(mfrow=c(1,3))
plotMDS(y, pch=c(0,1)[factor(sampleinfo$Group)], col=sampleinfo$colour,dim.plot = c(1,3), cex = 2)
legend("top", legend = c("AAV_MCS","AAV_PR"), pch=c(1,0), col = c("purple", "springgreen3"), cex=1)
plotMDS(y, pch=c(0,15,1,16)[factor(sampleinfo$BinSex)], col=sampleinfo$colour, dim.plot = c(1,3),cex = 2)
legend("top", legend = c("AAV_MCS","AAV_PR"), pch=c(1,0), col = c("purple", "springgreen3"), cex=1)
legend("topleft", legend = c("F","M"), pch=c(1,16), col = c("grey"), cex=1)
plotMDS(y, cex = 0.8, dim.plot = c(1,3))
Version | Author | Date |
---|---|---|
e1cb83f | evangelynsim | 2021-02-15 |
rm3 = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Mouse_AAV_PGR_RNAseq_bulk/output/mrna_fulllen_pe_strrev_q30.mx.chr", header = TRUE, row.names = 1)
rm3 = merge(rm3, rm2, by = "row.names")
rm3 = rm3[!grepl("Y",rm3$Chr),]
rm3 = rm3[!grepl("X",rm3$Chr),]
row.names(rm3)= rm3$Row.names
rm3 = rm3[,c(7:ncol(rm3))]
rm2 = rm3
z <- DGEList(rm3)
par(mfrow=c(1,3))
plotMDS(z, pch=c(0,1)[factor(sampleinfo$Group)], col=sampleinfo$colour,dim.plot = c(1,3), cex = 2)
legend("left", legend = c("AAV_MCS","AAV_PR"), pch=c(1,0), col = c("purple", "springgreen3"), cex=1)
plotMDS(z, pch=c(0,15,1,16)[factor(sampleinfo$BinSex)], col=sampleinfo$colour, dim.plot = c(1,3),cex = 2)
legend("left", legend = c("AAV_MCS","AAV_PR"), pch=c(1,0), col = c("purple", "springgreen3"), cex=1)
legend("topleft", legend = c("F","M"), pch=c(1,16), col = c("grey"), cex=1)
plotMDS(z, cex = 0.8, dim.plot = c(1,3))
Version | Author | Date |
---|---|---|
e1cb83f | evangelynsim | 2021-02-15 |
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] mclust_5.4.6 RColorBrewer_1.1-2 circlize_0.4.10
[4] ComplexHeatmap_2.0.0 gplots_3.1.0 Glimma_1.12.0
[7] edgeR_3.26.8 limma_3.40.6 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 pillar_1.4.6 compiler_3.6.1
[4] later_1.1.0.1 git2r_0.27.1 highr_0.8
[7] bitops_1.0-6 tools_3.6.1 digest_0.6.27
[10] clue_0.3-57 jsonlite_1.7.0 evaluate_0.14
[13] lifecycle_0.2.0 tibble_3.0.3 lattice_0.20-41
[16] png_0.1-7 pkgconfig_2.0.3 rlang_0.4.7
[19] rstudioapi_0.11 parallel_3.6.1 yaml_2.2.1
[22] xfun_0.18 cluster_2.1.0 stringr_1.4.0
[25] knitr_1.30 GlobalOptions_0.1.2 caTools_1.18.0
[28] gtools_3.8.2 fs_1.5.0 vctrs_0.3.2
[31] locfit_1.5-9.4 rprojroot_1.3-2 glue_1.4.2
[34] R6_2.5.0 GetoptLong_1.0.2 rmarkdown_2.5
[37] magrittr_1.5 whisker_0.4 backports_1.1.10
[40] promises_1.1.1 ellipsis_0.3.1 htmltools_0.5.0
[43] shape_1.4.4 colorspace_1.4-1 httpuv_1.5.4
[46] KernSmooth_2.23-17 stringi_1.5.3 rjson_0.2.20
[49] crayon_1.3.4