Last updated: 2021-02-19

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Knit directory: Human_Development_ATACseq_bulk/

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/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_ATACseq_bulk/output/ATACseq_samplesheet.txt	output/ATACseq_samplesheet.txt

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    Untracked:  Development_noXY.jn.rnk
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Introduction

In the GEO submission, 4 processed files (peaks) were uploaded.

humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.all_unfiltered.csv
humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.all.fix_filt.csv
humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.hum.fix_filt.csv
humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.MvsF.fix_filt.csv

They have been uploaded in the /output folder and will be used below to generate different figures.

Used libraries and functions

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.

Read files

rm1 <- read.csv("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_ATACseq_bulk/output/humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.all.fix_filt.csv", row.names = 1)

info = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_ATACseq_bulk/output/ATACseq_samplesheet.txt", header = TRUE, sep = "\t")


m = match(info$ID,names(rm1))
rm2 = rm1[,m]

rm1 = rm2

sampleinfo = info
levels(factor(sampleinfo$Group))

[1] "Adult"   "Fetal"   "hiPSCCM" "Young"

sampleinfo$colour = c("violetred4","darkgoldenrod2","bisque2","tomato")[factor(sampleinfo$Group)]
table(colnames(rm2)==sampleinfo$ID)


TRUE 
  27

y <- DGEList(rm2)

Make plot

par(mfrow=c(1,3))
#par(mar=c(5,1,5,1))

plotMDS(y, pch=c(0,1,5,2)[factor(sampleinfo$Group)], col=sampleinfo$colour, cex = 2)
legend("right", legend = c("iPSCCM","Fetal","Young","Adult"), pch=c(5,1,2,0), col = c("bisque2","darkgoldenrod2","tomato","violetred4"), cex=1)

plotMDS(y, pch=c(0,15,1,16,5,18,2,17)[factor(sampleinfo$BinSex)], col=sampleinfo$colour, cex = 2)
legend("right", legend = c("iPSCCM","Fetal","Young","Adult"), pch=c(5,1,2,0), col = c("bisque2","darkgoldenrod2","tomato","violetred4"), cex=1)
legend("bottomright", legend = c("F","M"), pch=c(1,16), col = c("grey"), cex=1)

plotMDS(y, cex = 0.8)

Remove Chr X & Y genes

rm3=rm2
rm3$Chr= gsub(".*_|:.*$", "", rownames(rm3))
rm3 = rm3[!grepl("Y",rm3$Chr),]
rm3 = rm3[!grepl("X",rm3$Chr),]
rm3 = rm3[,c(1:ncol(rm3)-1)]

z <- DGEList(rm3)

Make MDS plot (removed Chr X & Y genes)

par(mfrow=c(1,3))

plotMDS(z, pch=c(0,1,5,2)[factor(sampleinfo$Group)], col=sampleinfo$colour, cex = 2)
legend("right", legend = c("iPSCCM","Fetal","Young","Adult"), pch=c(5,1,2,0), col = c("bisque2","darkgoldenrod2","tomato","violetred4"), cex=1)

plotMDS(z, pch=c(0,15,1,16,5,18,2,17)[factor(sampleinfo$BinSex)], col=sampleinfo$colour, cex = 2)
legend("right", legend = c("iPSCCM","Fetal","Young","Adult"), pch=c(5,1,2,0), col = c("bisque2","darkgoldenrod2","tomato","violetred4"), cex=1)
legend("bottomright", legend = c("F","M"), pch=c(1,16), col = c("grey"), cex=1)

plotMDS(z, cex = 0.8)

Make Heatmap (removed Chr X & Y genes)

mycpm = cpm(rm3, log = T)

corr1 = cor(mycpm, method = "pearson")

matrix = as.matrix(corr1)

set.seed(20)

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette) #colorramppa make colour gradient

ann <- data.frame(sampleinfo[,c(1,3)])
colnames(ann) <- c("Group","Sex")
colours <- list("Group"=c("Fetal"="darkgoldenrod2","Young"="tomato","Adult"="violetred4", "hiPSCCM"="bisque2" ),
                "Sex"=c("F"="deeppink1","M"="dodgerblue1"))
colAnn <- HeatmapAnnotation(df=ann, which="col", col=colours, annotation_width=unit(c(1, 4), "cm"), gap=unit(1, "mm"))
colAnn1 <- HeatmapAnnotation(df=ann, which="row", col=colours, annotation_width=unit(c(1, 4), "cm"), gap=unit(1, "mm"))

ht_list = 
  Heatmap(matrix, name = "Correlation",
          row_title = "",
          row_title_gp = gpar(fontsize = 10),
          col = rev(morecols(50)),
          width = unit(15, "cm"),
          height = unit(15, "cm"),
          top_annotation = colAnn,
          left_annotation = colAnn1,
          cluster_rows =T,
          show_row_names = T,
          row_names_side = "left",
          row_names_gp = gpar(fontsize = 15),
          cluster_columns =T,
          column_names_side = "top",
          column_names_gp = gpar(fontsize = 15),  
          column_title = "", 
          column_title_gp = gpar(fontsize = 15))
 
set.seed(20)

clus = draw(ht_list)

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

MDS plot & Heatmap (Peaks)

Evangelyn Sim

2/15/2021