load packages
library(RColorBrewer)
library(ggplot2)
library(clusterProfiler)
library(org.Mm.eg.db)
library(GeneOverlap)
library(ggVennDiagram)
library(VennDiagram)Differential binding sites from csaw (snakepipes) ## Heatmap of FOXG1 and ATAC scores over differential ATAC regions
knitr::include_graphics("C:/Users/admin.ia1014/Documents/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/SF5/Diff_ATAC_FOXG1_mean and LFC.png")
Supp Fig 6A: Differential accessbility changes upon reduced levels of FOXG1
# upload files
atac_diff_DEGs= read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/SF5/Galaxy735-[diff_atac_DEG_intersection_long_file].tabular",
sep="\t", quote="", fill=FALSE,)
atac_diff_DEG_df= as.data.frame(atac_diff_DEGs)
atac_diff_DEG_df[,'V8']<-factor(atac_diff_DEG_df[,'V8'])
atac_diff_DEG_df$V3<- as.numeric(atac_diff_DEG_df$V3)
#Filter
atac_diff_DEG_filt<- atac_diff_DEG_df[(abs(atac_diff_DEG_df$V3)>= 0.5 & (atac_diff_DEG_df$V6<= 0.01)),]
atac_diff_DEG_filt_df<- as.data.frame(atac_diff_DEG_filt)
atac_diff_DEG_filt_df[,'V8']<-factor(atac_diff_DEG_filt_df[,'V8'])
atac_diff_DEG_filt_df$V3<-as.numeric(atac_diff_DEG_filt_df$V3)
# plot DEG distribution in each cluster, LFC.cutoff= 0.5
my_palette_2 <- c("darkgreen", "darkred", "gray18")
p_atac_diff <- ggplot(atac_diff_DEG_df,
aes(x=V8, y=V3, fill=V8, color= V8, alpha=0.5, font=10))+
scale_color_manual(values = my_palette_2, aesthetics = "fill")+
scale_color_manual(values = my_palette_2, aesthetics = "colour")+
geom_violin()+
labs(x="cluster", y = "Log2FC")+ theme_light()+
stat_summary(fun=median, geom="point", size=2, color="black")+
theme(axis.text = element_text(size=10),
axis.title = element_text(size=10))
# Violin plot of the distribution of DEGs in gain/loss of accessibility clusters upon FOXG1 KD
violin_plot_atac<- p_atac_diff + geom_jitter( data= atac_diff_DEG_filt_df,
shape=16,
size=5,
position=position_jitter(width=0.2, height= 0.1))
violin_plot_atac
# export the violin plot to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/SF5/violin plot_diff_atac_DEGs_051022.pdf",
width=4,
height=4
)
print(violin_plot_atac)
dev.off()## png
## 2
# upload files
# DEGs
Foxg1_KD_DEGs<-read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 1/DE_genes_shrinked_apeglm_DIV11.tabular", sep="\t", header = TRUE, fill = FALSE,)
Foxg1_KD_DEGs_df<- as.data.frame(Foxg1_KD_DEGs)
Foxg1_KD_DEGs_df$log2FoldChange<-as.numeric(gsub(",", ".", Foxg1_KD_DEGs_df$log2FoldChange))
# Filter the DEGs
increased_DEG<-Foxg1_KD_DEGs_df[(Foxg1_KD_DEGs_df$log2FoldChange>=0.5 &
Foxg1_KD_DEGs_df$padj<=0.05),]
decreased_DEG<-Foxg1_KD_DEGs_df[(Foxg1_KD_DEGs_df$log2FoldChange<=(-0.5) &
Foxg1_KD_DEGs_df$padj<=0.05),]
static_DEG<-Foxg1_KD_DEGs_df[(abs(Foxg1_KD_DEGs_df$log2FoldChange)< 0.5) & (Foxg1_KD_DEGs_df$padj>0.05),]
# Put them together in a list
DEG_list<- list(increased_DEG= increased_DEG$X,
decreased_DEG=decreased_DEG$X,
static=static_DEG$X
)
# Differential ATAC peak files (gain-loss-random)
atac_diff_up<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/SF5/Galaxy673-[atac_up__Annotated_Peaks].tabular",
sep="\t", header = TRUE, quote="", fill=FALSE,)
atac_diff_down<-read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/SF5/Galaxy674-[atac_down__Annotated_Peaks].tabular",
sep="\t", header = TRUE, quote="", fill=FALSE,)
atac_random<-read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/SF5/atac_random__Annotated_Peaks.tabular",
sep="\t", header = TRUE,)
# Put them together in a clustered list
atac_diff_list<- list(gain_atac= atac_diff_up$geneId,
loss_atac= atac_diff_down$geneId,
random_atac= atac_random$geneId)
# ATAC clusters-DEGs over-representation matrix
GO_matrix_atac_diff<-newGOM(gsetA = atac_diff_list,
gsetB=DEG_list)
GO_matrix_atac_diff## A <3 x 3> GeneOverlapMatrix object
## Geneset A sizes:
## gain_atac loss_atac random_atac
## 322 347 257
## Geneset B sizes:
## increased_DEG decreased_DEG static
## 1012 2130 18772
# Oddsratio heatmap
heatmap_atac_diff<- drawHeatmap(GO_matrix_atac_diff,
what = c("odds.ratio"),
adj.p=TRUE,
cutoff=0.1,
ncolused = 5,
grid.col = "Purples",
note.col = "Black")
# Jaccard heatmap
heatmap_atac_diff<- drawHeatmap(GO_matrix_atac_diff,
what = c("Jaccard"),
adj.p=TRUE,
cutoff=0.1,
ncolused = 5,
grid.col = "Purples",
note.col = "Black")
# Symbol to ENTREZ ID annotation
atac_diff_up_id<-bitr(atac_diff_up$geneId, fromType = "ENSEMBL", toType = "ENTREZID", OrgDb ="org.Mm.eg.db", drop = TRUE)## 'select()' returned 1:1 mapping between keys and columns
## Warning in bitr(atac_diff_up$geneId, fromType = "ENSEMBL", toType =
## "ENTREZID", : 23.31% of input gene IDs are fail to map...
atac_diff_down_id<-bitr(atac_diff_down$geneId, fromType = "ENSEMBL", toType = "ENTREZID", OrgDb ="org.Mm.eg.db", drop = TRUE)## 'select()' returned 1:1 mapping between keys and columns
## Warning in bitr(atac_diff_down$geneId, fromType = "ENSEMBL", toType =
## "ENTREZID", : 16.78% of input gene IDs are fail to map...
# create geneCluster list
list_atac_diff<-list(atac_gain= atac_diff_up_id$ENTREZID,
atac_loss= atac_diff_down_id$ENTREZID)
# Differential Functional enrichment by comparecluster
atac_diff_GO <- compareCluster(geneClusters = list_atac_diff,
fun="enrichGO",
OrgDb = "org.Mm.eg.db",
ont = "BP",
pAdjustMethod = "BH",
qvalueCutoff = 0.05,
pvalueCutoff = 0.05,
readable = TRUE)
# Simplify the terms to avoid redundancy
atac_diff_GO_simp<-simplify(atac_diff_GO,
cutoff = 0.5,
by = "p.adjust",
select_fun = min,
measure = "Wang",
semData = NULL)
# Dotplot of GO term analysis
dp_diff_ATAC = dotplot(atac_diff_GO,
showCategory=10,
font.size=10
)
dp_diff_ATAC
# Dotplot of simplified terms
dp_diff_ATAC_simp = dotplot(atac_diff_GO_simp,
showCategory=10,
font.size=10
)
dp_diff_ATAC_simp
# export the dotplots to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/SF5/atac_diff_csaw_GOterms_dotplot_2006.pdf", width=6, height=5)
print(dp_diff_ATAC)
dev.off()## png
## 2
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/SF5/atac_diff_csaw_GOterms_dotplot_simp_2006.pdf", width=6, height=5)
print(dp_diff_ATAC_simp)
dev.off()## png
## 2
# create reference table for clustered differential GO terms and export to csv
atac_diff_GO_df = as.data.frame(atac_diff_GO)
write.table(atac_diff_GO_df, file="~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/SF5/atac_diff_csaw_GOterms.txt",
sep = "\t", quote = F,)# Venn diagrams of ATAC and DEGs
# load peak clustered ATAC at FOXG1 peaks
ATAC_c1<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 3/ATAC/Galaxy134-[ATAC_FOXG1_filtered_peaks_Cluster1].tabular",
header=TRUE, sep="\t", quote="", fill=FALSE,)
ATAC_c2<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 3/ATAC/Galaxy137-[ATAC_FOXG1_filtered_peaks_Cluster2].tabular",
header=TRUE, sep="\t", quote="", fill=FALSE,)
ATAC_c3<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 3/ATAC/Galaxy140-[ATAC_FOXG1_filtered_peaks_Cluster3].tabular",
header=TRUE, sep="\t", quote="", fill=FALSE,)
ATAC_c4<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 3/ATAC/Galaxy143-[ATAC_FOXG1_filtered_peaks_Cluster4].tabular",
header=TRUE, sep="\t", quote="", fill=FALSE,)
ATAC_c5<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 3/ATAC/Galaxy144-[ATAC_FOXG1_filtered_peaks_Cluster5].tabular",
header=TRUE, sep="\t", quote="", fill=FALSE,)
#Increased DEGs-Increased ATAC at FOXG1 peaks- Gain of ATAC
venn_list_ATAC_up<- list(ATAC_gain=ATAC_c4$geneId,
ATAC_diff_up= atac_diff_up$geneId,
Foxg1KD_increased=increased_DEG$X)
venn_ATAC_up<-ggVennDiagram(venn_list_ATAC_up)
venn_ATAC_up
# Intersected gene list
ATAC_up_intersect<- calculate.overlap(venn_list_ATAC_up)
ATAC_up_intersect_symbol<-bitr(ATAC_up_intersect$a5,
fromType = "ENSEMBL",
toType = "SYMBOL",
OrgDb = org.Mm.eg.db,
drop=TRUE)## 'select()' returned 1:1 mapping between keys and columns
ATAC_up_intersect_symbol## ENSEMBL SYMBOL
## 1 ENSMUSG00000024347 Psd2
## 2 ENSMUSG00000031026 Trim66
## 3 ENSMUSG00000032796 Lama1
## 4 ENSMUSG00000035580 Kcnh8
## 5 ENSMUSG00000055214 Pld5
## 6 ENSMUSG00000060579 Fhit
# Decreased DEGs-Increased ATAC at FOXG1 peaks- gain of ATAC
venn_list_ATAC_up_DEG_down<- list(ATAC_gain=ATAC_c4$geneId,
ATAC_diff_up= atac_diff_up$geneId,
Foxg1KD_decreased=decreased_DEG$X)
venn_ATAC_up_DEG_down<-ggVennDiagram(venn_list_ATAC_up_DEG_down,
n.sides = 3000)
venn_ATAC_up_DEG_down
# intersected gene list
ATAC_up_DEG_down_intersect<- calculate.overlap(venn_list_ATAC_up_DEG_down)
ATAC_up_DEG_down_intersect_symbol<-bitr(ATAC_up_DEG_down_intersect$a5,
fromType = "ENSEMBL",
toType = "SYMBOL",
OrgDb = org.Mm.eg.db,
drop=TRUE)## 'select()' returned 1:1 mapping between keys and columns
ATAC_up_DEG_down_intersect_symbol## ENSEMBL SYMBOL
## 1 ENSMUSG00000009734 Pou6f2
## 2 ENSMUSG00000021534 1700001L19Rik
## 3 ENSMUSG00000023800 Tiam2
## 4 ENSMUSG00000026156 B3gat2
## 5 ENSMUSG00000026640 Plxna2
## 6 ENSMUSG00000026726 Cubn
## 7 ENSMUSG00000029093 Sorcs2
## 8 ENSMUSG00000033569 Adgrb3
## 9 ENSMUSG00000039637 Coro7
## 10 ENSMUSG00000042659 Arrdc4
## 11 ENSMUSG00000047261 Gap43
## 12 ENSMUSG00000049556 Lingo1
## 13 ENSMUSG00000049871 Nlrc3
## 14 ENSMUSG00000050640 Tmem150c
## 15 ENSMUSG00000051331 Cacna1c
## 16 ENSMUSG00000052512 Nav2
## 17 ENSMUSG00000053024 Cntn2
## 18 ENSMUSG00000053477 Tcf4
## 19 ENSMUSG00000058589 Anks1b
## 20 ENSMUSG00000086006 Gm13293
# Decreased DEGs-Decreased ATAC at FOXG1 peaks- Loss of ATAC
venn_list_ATAC_down<- list(ATAC_loss=ATAC_c5$geneId,
ATAC_diff_down= atac_diff_down$geneId,
Foxg1KD_decreased=decreased_DEG$X)
venn_ATAC_down<-ggVennDiagram(venn_list_ATAC_down,
n.sides = 3000)
venn_ATAC_down
# Intersected gene list
ATAC_down_intersect<- calculate.overlap(venn_list_ATAC_down)
ATAC_down_intersect_symbol<-bitr(ATAC_down_intersect$a5,
fromType = "ENSEMBL",
toType = "SYMBOL",
OrgDb = org.Mm.eg.db,
drop=TRUE)## 'select()' returned 1:1 mapping between keys and columns
ATAC_down_intersect_symbol## ENSEMBL SYMBOL
## 1 ENSMUSG00000002020 Ltbp2
## 2 ENSMUSG00000003541 Ier3
## 3 ENSMUSG00000005087 Cd44
## 4 ENSMUSG00000014592 Camta1
## 5 ENSMUSG00000018008 Cyth4
## 6 ENSMUSG00000020644 Id2
## 7 ENSMUSG00000020658 Efr3b
## 8 ENSMUSG00000021070 Bdkrb2
## 9 ENSMUSG00000022861 Dgkg
## 10 ENSMUSG00000026879 Gsn
## 11 ENSMUSG00000027195 Hsd17b12
## 12 ENSMUSG00000027803 Wwtr1
## 13 ENSMUSG00000028249 Sdcbp
## 14 ENSMUSG00000028634 Hivep3
## 15 ENSMUSG00000029153 Ociad2
## 16 ENSMUSG00000029359 Tesc
## 17 ENSMUSG00000029641 Rasl11a
## 18 ENSMUSG00000030096 Slc6a6
## 19 ENSMUSG00000032017 Grik4
## 20 ENSMUSG00000032501 Trib1
## 21 ENSMUSG00000032724 Abtb2
## 22 ENSMUSG00000032860 P2ry2
## 23 ENSMUSG00000033066 Gas7
## 24 ENSMUSG00000033788 Dysf
## 25 ENSMUSG00000034593 Myo5a
## 26 ENSMUSG00000034751 Mast4
## 27 ENSMUSG00000044164 Rnf182
## 28 ENSMUSG00000046709 Mapk10
## 29 ENSMUSG00000047842 Diras2
## 30 ENSMUSG00000048001 Hes5
## 31 ENSMUSG00000052942 Glis3
## 32 ENSMUSG00000053519 Kcnip1
## 33 ENSMUSG00000053702 Nebl
## 34 ENSMUSG00000055652 Klhl25
## 35 ENSMUSG00000091387 Gcnt4
sessionInfo()## R version 4.2.0 (2022-04-22 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17763)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_Germany.1252 LC_CTYPE=English_Germany.1252
## [3] LC_MONETARY=English_Germany.1252 LC_NUMERIC=C
## [5] LC_TIME=English_Germany.1252
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] VennDiagram_1.7.3 futile.logger_1.4.3 ggVennDiagram_1.2.2
## [4] GeneOverlap_1.32.0 org.Mm.eg.db_3.15.0 AnnotationDbi_1.58.0
## [7] IRanges_2.30.1 S4Vectors_0.34.0 Biobase_2.56.0
## [10] BiocGenerics_0.42.0 clusterProfiler_4.4.4 ggplot2_3.4.0
## [13] RColorBrewer_1.1-3
##
## loaded via a namespace (and not attached):
## [1] fgsea_1.22.0 colorspace_2.0-3 ggtree_3.4.4
## [4] class_7.3-20 ellipsis_0.3.2 qvalue_2.28.0
## [7] XVector_0.36.0 aplot_0.1.9 proxy_0.4-27
## [10] rstudioapi_0.14 farver_2.1.1 graphlayouts_0.8.4
## [13] ggrepel_0.9.2 bit64_4.0.5 fansi_1.0.3
## [16] scatterpie_0.1.8 codetools_0.2-18 splines_4.2.0
## [19] cachem_1.0.6 GOSemSim_2.22.0 knitr_1.41
## [22] polyclip_1.10-4 jsonlite_1.8.3 GO.db_3.15.0
## [25] png_0.1-7 ggforce_0.4.1 compiler_4.2.0
## [28] httr_1.4.4 assertthat_0.2.1 Matrix_1.5-3
## [31] fastmap_1.1.0 lazyeval_0.2.2 cli_3.4.1
## [34] formatR_1.12 tweenr_2.0.2 htmltools_0.5.3
## [37] tools_4.2.0 igraph_1.3.5 gtable_0.3.1
## [40] glue_1.6.2 GenomeInfoDbData_1.2.8 reshape2_1.4.4
## [43] DO.db_2.9 dplyr_1.0.10 fastmatch_1.1-3
## [46] Rcpp_1.0.9 enrichplot_1.16.2 vctrs_0.5.1
## [49] Biostrings_2.64.1 ape_5.6-2 nlme_3.1-160
## [52] ggraph_2.1.0 xfun_0.35 stringr_1.4.1
## [55] lifecycle_1.0.3 gtools_3.9.3 DOSE_3.22.1
## [58] zlibbioc_1.42.0 MASS_7.3-58.1 scales_1.2.1
## [61] tidygraph_1.2.2 parallel_4.2.0 lambda.r_1.2.4
## [64] yaml_2.3.6 memoise_2.0.1 gridExtra_2.3
## [67] downloader_0.4 ggfun_0.0.9 yulab.utils_0.0.5
## [70] stringi_1.7.8 RSQLite_2.2.19 highr_0.9
## [73] e1071_1.7-12 tidytree_0.4.1 caTools_1.18.2
## [76] BiocParallel_1.30.4 GenomeInfoDb_1.32.4 rlang_1.0.6
## [79] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.18
## [82] lattice_0.20-45 sf_1.0-9 purrr_0.3.5
## [85] labeling_0.4.2 treeio_1.20.2 patchwork_1.1.2
## [88] shadowtext_0.1.2 bit_4.0.5 tidyselect_1.2.0
## [91] plyr_1.8.8 magrittr_2.0.3 R6_2.5.1
## [94] gplots_3.1.3 generics_0.1.3 DBI_1.1.3
## [97] pillar_1.8.1 withr_2.5.0 units_0.8-0
## [100] KEGGREST_1.36.3 RCurl_1.98-1.9 tibble_3.1.8
## [103] crayon_1.5.2 futile.options_1.0.1 KernSmooth_2.23-20
## [106] utf8_1.2.2 RVenn_1.1.0 rmarkdown_2.18
## [109] viridis_0.6.2 data.table_1.14.6 blob_1.2.3
## [112] classInt_0.4-8 digest_0.6.30 tidyr_1.2.1
## [115] gridGraphics_0.5-1 munsell_0.5.0 viridisLite_0.4.1
## [118] ggplotify_0.1.0