load packages
library(RColorBrewer)
library(ggplot2)
library(clusterProfiler)
library(org.Mm.eg.db)
library(GeneOverlap)
library(ggVennDiagram)
library(VennDiagram)paired-end * shFoxg1: 2 replicates for ChIP: hippoN_FOxg1KD_H3K4me3_rep1/ _rep2
- 2 inputs: Foxg1KD_Input_rep1, FOxg1KD_Input_rep2
* shControl: 2 replicates for ChIP: hippoN_Ctr_H3K4me3_rep1/ _rep2 - 2 inputs: Control_Input_rep1, Control_Input_rep2 * Mapping: Bowtie2,
using snakepipes defaults
*** ## Quality Control (snakepipes) 1. Deeptools QC
Plot Read Coverage
Multibamsummary correlation, spearman
PCA plot
- Filtered Bam Files (snkaepipes)
- Filtering rules: -q 3
- meaning: remove reads with MAPQ<3 (-q 3)
- BamCoverage (snakepipes) Depth normalisation of samples. These were not used in downstream analyses (IA).
- BamCompare (snakepipes) Input (control) and depth normalisation of ChIP-seq samples. 2 different calculations:
- log2Ratio of Sample over Input
- Subtraction of Input from ChIP-sample
These were preferred for downstream analyses and visualisations (IA).
- MACS2- Peak Calling (snakepipes) Narrow peaks and summits were
called for each replicate separately. These peak files were used
for downstream analyses.
# Changes in H3K4me3 at FOXG1 peaks upon Foxg1KD
-
From Galaxy
-
Tools: computeMatrix, plotHeatmap w/ K-means clustering, ChIPSeeker for region annotation
-
Regions plotted: FOXG1 peaks (N1 filtered)
-
Score files: mean Log2FoldRatio of FOXG1/Input, Control_H3K4me3/Input, Foxg1KD_H3K4me3/Input, H3K4me3_Foxg1KD/Control
H3K4me3 alterations upon Foxg1KD
# upload files
k4me3_DEGs= read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 3/H3K4me3/Galaxy170-[K4me3_at_FOXG1_peaks_clustered_DEGs].tabular", sep="\t", quote="", fill=FALSE, header=FALSE,)
# this dataset is an intersection of K-means clustered K27ac enrichment at FOXG1 peaks (above heatmap) and DEGs upon FOXG1 KD.
k4me3_DEG_df= as.data.frame(k4me3_DEGs)
k4me3_DEG_df[,'V14']<-factor(k4me3_DEG_df[,'V14'])
k4me3_DEG_df$V25<-as.numeric(k4me3_DEG_df$V25)## Warning: NAs introduced by coercion
# filter by LFC and padj
k4me3_DEG_filt<- k4me3_DEG_df[(abs(k4me3_DEG_df$V25)>0.5 & k4me3_DEG_df$V28<=0.01),]
k4me3_DEG_filt_df<- as.data.frame(k4me3_DEG_filt)
k4me3_DEG_filt_df[,'V14']<-factor(k4me3_DEG_filt_df[,'V14'])
k4me3_DEG_filt_df$V25<-as.numeric(k4me3_DEG_filt_df$V25)
## violin plot of DEG distribution in each cluster: LFC.cutoff=0.5
#colour key
my_palette <- brewer.pal(name="Reds",n=8)[4:8]
p_k4me3 <- ggplot(k4me3_DEG_df,
aes(x=V14, y=V25, fill=V14, color= V14, alpha=0.5, font=12))+
scale_color_manual(values = my_palette, aesthetics = "fill")+
scale_color_manual(values = my_palette, 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=12),
axis.title = element_text(size=12))
violin_plot_k4me3<- p_k4me3 + geom_jitter( data= k4me3_DEG_filt_df,
shape=16,
size=2.5,
position=position_jitter(width=0.2, height= 0.1)
)
# plot the violin plot and export to pdf (Figure 3J)
# Differentially expressed genes corresponding to clustered H3K4me3 at FOXG1 binding sites
violin_plot_k4me3## Warning: Removed 3 rows containing non-finite values (`stat_ydensity()`).
## Warning: Removed 3 rows containing non-finite values (`stat_summary()`).
# export the plot to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 3/violin plot_H3K4me3_at_FOXG1peaks_filtered_051022.pdf",
width=4.5,
height=4)
print(violin_plot_k4me3)## Warning: Removed 3 rows containing non-finite values (`stat_ydensity()`).
## Removed 3 rows containing non-finite values (`stat_summary()`).
dev.off()## png
## 2
# Upload clustered and annotated peak files
k4me3_c1<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 3/H3K4me3/Galaxy135-[k4me3_FOXG1_filtered_peaks_Cluster1].tabular",
header=TRUE, sep="\t", quote="", fill=FALSE,)
k4me3_c2<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 3/H3K4me3/Galaxy138-[K4me3_FOXG1_filtered_peaks_Cluster2].tabular",
header=TRUE, sep="\t", quote="", fill=FALSE,)
k4me3_c3<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 3/H3K4me3/Galaxy141-[K4me3_FOXG1_filtered_peaks_Cluster3].tabular",
header=TRUE, sep="\t", quote="", fill=FALSE,)
# make a list of clustered h3k4me3 peaks (gain, loss, static)
k4me3_list<- list(static_k4me3= k4me3_c1$geneId,
gain_k4me3= k4me3_c2$geneId,
loss_k4me3= k4me3_c3$geneId)
# Upload DEG table (FOXG1KD/Control in DIV11 hippocampal neurons)
Foxg1_KD_DEGs<-read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 1/DE_genes_shrinked_apeglm_DIV11.tabular",
sep="\t", header = TRUE, fill = FALSE,)
Foxg1_KD_DEGs_df<- as.data.frame(Foxg1_KD_DEGs)
Foxg1_KD_DEGs_df$log2FoldChange<-as.numeric(gsub(",", ".", Foxg1_KD_DEGs_df$log2FoldChange))
# filter according to increased and decreased gene expression
increased_DEG<-Foxg1_KD_DEGs_df[(Foxg1_KD_DEGs_df$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$padj>0.05),]
# combine filtered DEGs in a list
DEG_list<- list(increased_DEG= increased_DEG$X,
decreased_DEG=decreased_DEG$X,
static=static_DEG$X
)
# Prepare Geneoverlap matrix for enrichment test- clustered H3K4me3-DEG overlap
GO_matrix_k4me3<-newGOM(k4me3_list, DEG_list, genome.size =nrow(Foxg1_KD_DEGs_df))
GO_matrix_k4me3## A <3 x 3> GeneOverlapMatrix object
## Geneset A sizes:
## static_k4me3 gain_k4me3 loss_k4me3
## 15916 45238 46771
## Geneset B sizes:
## increased_DEG decreased_DEG static
## 1012 2130 18772
# oddsratio heatmap for strength of association between lists
heatmap_k4me3<- drawHeatmap(GO_matrix_k4me3,
what = c("odds.ratio"),
adj.p=TRUE,
cutoff=0.1,
ncolused = 5,
grid.col = "Reds",
note.col = "Black")
# Jaccard heatmap for similarity between two lists
heatmap_k4me3<- drawHeatmap(GO_matrix_k4me3,
what = c("Jaccard"),
adj.p=TRUE,
cutoff=0.1,
ncolused = 5,
grid.col = "Oranges",
note.col = "Black")
# annotate ENSEMBL ID to ENTREZID
c1_k4me3<-bitr(k4me3_c1$geneId, fromType="ENSEMBL",
toType=c("ENTREZID","ENSEMBL"),
OrgDb="org.Mm.eg.db",
drop = TRUE)
c2_k4me3<-bitr(k4me3_c2$geneId,
fromType="ENSEMBL",
toType=c("ENTREZID","ENSEMBL"),
OrgDb="org.Mm.eg.db",
drop = TRUE)
c3_k4me3<-bitr(k4me3_c3$geneId,
fromType="ENSEMBL",
toType=c("ENTREZID","ENSEMBL"),
OrgDb="org.Mm.eg.db",
drop = TRUE)
### create geneCluster list from ENTREZIDs
list_k4me3<-list(cluster1= c1_k4me3$ENTREZID,
cluster2= c2_k4me3$ENTREZID,
cluster3= c3_k4me3$ENTREZID)
### comparecluster analysis for H3K4me3 peaks
k4me3_GO <- compareCluster(geneClusters = list_k4me3,
fun="enrichGO",
OrgDb = "org.Mm.eg.db",
ont = "BP",
pAdjustMethod = "BH",
qvalueCutoff = 0.01,
pvalueCutoff = 0.01,
readable = TRUE)
# simplify the terms to avoid redundancy
k4me3_GO_simp <- clusterProfiler::simplify(k4me3_GO,
cutoff = 0.7,
by = "p.adjust",
select_fun = min,
measure = "Wang",
semData = NULL)
# Dotplot of the comparecluster result
dotplot_k4me3<-dotplot(k4me3_GO,
showCategory=8,
font.size=12,
includeAll=FALSE
)
dotplot_k4me3
# Dotplot of the SIMPLIFIED comparecluster result
dp_k4me3_simp = dotplot(k4me3_GO_simp,
showCategory=8,
font.size=10,
includeAll=FALSE,
)
dp_k4me3_simp
# create dataframe for GO terms from the analysis
df_k4me3_GO = as.data.frame(k4me3_GO)
write.table(df_k4me3_GO,
file="~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 3/H3K4me3_atFOXG1_clustered_GO terms_filtered_051022.txt",
sep = "\t",
quote = FALSE,)
# export the dotplots to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 3/K4me3_atFOXG1_clustered_GO terms_dotplot_filtered_051022.pdf",
width=5.5, height=5.7)
print(dotplot_k4me3)
dev.off()## png
## 2
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/SF4/k4me3_atFOXG1_clustered_GO terms_dotplot_simp_filtered_051022.pdf",
width=4, height=5)
print(dp_k4me3_simp)
dev.off()## png
## 2
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] ellipsis_0.3.2 qvalue_2.28.0 XVector_0.36.0
## [7] aplot_0.1.9 rstudioapi_0.14 farver_2.1.1
## [10] graphlayouts_0.8.4 ggrepel_0.9.2 bit64_4.0.5
## [13] fansi_1.0.3 scatterpie_0.1.8 codetools_0.2-18
## [16] splines_4.2.0 cachem_1.0.6 GOSemSim_2.22.0
## [19] knitr_1.41 polyclip_1.10-4 jsonlite_1.8.3
## [22] GO.db_3.15.0 png_0.1-7 ggforce_0.4.1
## [25] compiler_4.2.0 httr_1.4.4 assertthat_0.2.1
## [28] Matrix_1.5-3 fastmap_1.1.0 lazyeval_0.2.2
## [31] cli_3.4.1 formatR_1.12 tweenr_2.0.2
## [34] htmltools_0.5.3 tools_4.2.0 igraph_1.3.5
## [37] gtable_0.3.1 glue_1.6.2 GenomeInfoDbData_1.2.8
## [40] reshape2_1.4.4 DO.db_2.9 dplyr_1.0.10
## [43] fastmatch_1.1-3 Rcpp_1.0.9 enrichplot_1.16.2
## [46] vctrs_0.5.1 Biostrings_2.64.1 ape_5.6-2
## [49] nlme_3.1-160 ggraph_2.1.0 xfun_0.35
## [52] stringr_1.4.1 lifecycle_1.0.3 gtools_3.9.3
## [55] DOSE_3.22.1 zlibbioc_1.42.0 MASS_7.3-58.1
## [58] scales_1.2.1 tidygraph_1.2.2 parallel_4.2.0
## [61] lambda.r_1.2.4 yaml_2.3.6 memoise_2.0.1
## [64] gridExtra_2.3 downloader_0.4 ggfun_0.0.9
## [67] yulab.utils_0.0.5 stringi_1.7.8 RSQLite_2.2.19
## [70] highr_0.9 tidytree_0.4.1 caTools_1.18.2
## [73] BiocParallel_1.30.4 GenomeInfoDb_1.32.4 rlang_1.0.6
## [76] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.18
## [79] lattice_0.20-45 purrr_0.3.5 labeling_0.4.2
## [82] treeio_1.20.2 patchwork_1.1.2 shadowtext_0.1.2
## [85] bit_4.0.5 tidyselect_1.2.0 plyr_1.8.8
## [88] magrittr_2.0.3 R6_2.5.1 gplots_3.1.3
## [91] generics_0.1.3 DBI_1.1.3 pillar_1.8.1
## [94] withr_2.5.0 KEGGREST_1.36.3 RCurl_1.98-1.9
## [97] tibble_3.1.8 crayon_1.5.2 futile.options_1.0.1
## [100] KernSmooth_2.23-20 utf8_1.2.2 RVenn_1.1.0
## [103] rmarkdown_2.18 viridis_0.6.2 data.table_1.14.6
## [106] blob_1.2.3 digest_0.6.30 tidyr_1.2.1
## [109] gridGraphics_0.5-1 munsell_0.5.0 viridisLite_0.4.1
## [112] ggplotify_0.1.0