from 18.5 embryos *Datasets:
- 2 replicates for WT NEUROD1-ChIP + 2 Inputs
- Mapping: Bowtie2, using snakepipes defaults (FF)
*** ## Quality Control (FF) 1. Deeptools QC
Plot Read Coverage
Multibamsummary correlation, spearman
PCA plot
- Filtered .bam Files (from snakepipes)
- Filtering rules: -F 1024 -q 3
- meaning: remove optical PCR duplicates (-F 1024), remove reads with MAPQ<3 (-q 3)
- BamCoverage (from snakepipes) Depth normalisation of samples. These were not used in downstream analyses (IA).
- BamCompare (from snakepipes) Input (control) and depth normalisation of ChIP-seq samples. 2 different calculations:
- log2Ratio of Sample over Input
- Subtraction of Input fr~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 5/NEUROD1_ChIP-seq_deepTools_qc/logs/ These were preferred for downstream analyses and visualisations (IA).
-
MACS2- Peak Calling (from snakepipes) Narrow peaks and summits were called for each replicate separately. Models are in:
These peak files were used for downstream analyses.
# Downstream Analyses ## Genomic Distribution of NEUROD1 peaks *From usegalaxy.eu, Tool: ChIPSeeker (v 1.28.3)
Genomic Distribution of NEUROD1 peaks, WT in vitro
-
From usegalaxy.eu
-
Tools: computeMatrix, plotHeatmap w/ K-means clustering k=5, ChIPSeeker for region annotation
NEUROD1 peaks at TSS, in vitro
-
read in NEUROD1 peaks file
NEUROD1_peaks <- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 5/Galaxy172-[ChIPseeker_neurod1_intersect_annotated_peaks].tabular", header=TRUE, sep="\t", quote="", fill=FALSE,)- annotate ensembl ID to ENTREZID
NEUROD1_id<-bitr(NEUROD1_peaks$geneName, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(NEUROD1_peaks$geneName, fromType = "ENSEMBL", toType =
## "ENTREZID", : 24.38% of input gene IDs are fail to map...
- Functional enrichment of NEUROD1 peaks
NEUROD1_GO<-enrichGO(gene = NEUROD1_id$ENTREZID,
keyType = "ENTREZID",
ont = "BP",
OrgDb = "org.Mm.eg.db",
pvalueCutoff = 0.01,
qvalueCutoff = 0.01,
pAdjustMethod = "BH",
readable = TRUE)- create reference table for enriched GO terms and export to csv
NEUROD1_GO_df<- as.data.frame(NEUROD1_GO)
write.csv(NEUROD1_GO_df, file="~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/NEUROD1_GOterms_reference_2206.csv")- simplify the terms to avoid redundancy
NEUROD1_GO_simp<-simplify(NEUROD1_GO,
cutoff = 0.7,
by = "p.adjust",
select_fun = min,
measure = "Wang",
semData = NULL)- embed dotplot of simplified terms
dp_neurod1 = dotplot(NEUROD1_GO_simp,
showCategory=10,
font.size=11,
orderBy="GeneRatio"
)
plot(dp_neurod1)
* export the dotplot to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/NEUROD1_peaks_GOterms_dotplot_simp_2206.pdf", width=4.5, height=3.5)
print(dp_neurod1)
dev.off()## png
## 2
- Overlapping and unique FOXG1 and NEUROD1 peaks were analysed using bedtools in usegalaxy.eu (at least 1 bp overlap)
- read in files from Galaxy
FOXG1_NEUROD1_clustered_DEG<- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 5/Galaxy280-[FOXG1-NEUROD1-shared_peak-DEG-intersection_long_file].tabular", header=TRUE, sep="\t", quote="", fill=FALSE,)
FOXG1_NEUROD1_clustered_DEG_df= as.data.frame(FOXG1_NEUROD1_clustered_DEG)
FOXG1_NEUROD1_clustered_DEG_df[,'FOXG1_unique']<-factor(FOXG1_NEUROD1_clustered_DEG_df[,'FOXG1_unique'])
FOXG1_NEUROD1_clustered_DEG_df$log2FoldChange<-as.numeric(gsub(",", ".", FOXG1_NEUROD1_clustered_DEG_df$log2FoldChange))
FOXG1_NEUROD1_clustered_DEG_df$log2FoldChange<-as.numeric(as.character(FOXG1_NEUROD1_clustered_DEG_df$log2FoldChange))- Filter according to LFC and p.adjusted
FOXG1_NEUROD1_clustered_DEG_LFC<-FOXG1_NEUROD1_clustered_DEG_df[
(abs(FOXG1_NEUROD1_clustered_DEG_df$log2FoldChange)>= 0.58 &
FOXG1_NEUROD1_clustered_DEG_df$padj<=0.01),]
FOXG1_NEUROD1_clustered_DEG_LFC_df<- as.data.frame(FOXG1_NEUROD1_clustered_DEG_LFC)
FOXG1_NEUROD1_clustered_DEG_LFC_df[,'FOXG1_unique']<-factor(FOXG1_NEUROD1_clustered_DEG_LFC_df[,'FOXG1_unique'])
FOXG1_NEUROD1_clustered_DEG_LFC_df$log2FoldChange<-as.numeric(FOXG1_NEUROD1_clustered_DEG_LFC_df$log2FoldChange)my_palette <- brewer.pal(name="Dark2",n=6)[2:6]
violin_plot_vitro <- ggplot(FOXG1_NEUROD1_clustered_DEG_df, aes(x=FOXG1_unique, y=log2FoldChange, fill=FOXG1_unique, color=FOXG1_unique, alpha=0.8, font=12))+
scale_color_manual(values = c("springgreen4","mediumpurple4", "steelblue4"), aesthetics = "fill")+
scale_color_manual(values = c("springgreen4","mediumpurple4", "steelblue4"), aesthetics = "colour")+
geom_violin()+
labs(x="clusters", y = "Log2FC", font=12)+ theme_light()+
stat_summary(fun=median,
geom="point",
size=2.5,
color="black")
violin_plot_vitro_dots<-violin_plot_vitro +
geom_jitter(data= FOXG1_NEUROD1_clustered_DEG_LFC_df, shape=16,
size=2.5,
position=position_jitter(width=0.2, height= 0.1))- embed the vioin plot
plot(violin_plot_vitro_dots)
* export to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/violin plot_NEUROD1_FOXG1_DEG_clusters_2206_2.pdf",
width=4.5,
height=4.5)
print(violin_plot_vitro_dots)
dev.off()## png
## 2
#upload separate files for each cluster
FOXG1_unique_DEGS <- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 5/Galaxy266-[FOXG1_unique_DEG_intersection].tabular", header=TRUE, sep="\t", quote="", fill=FALSE,)
NEUROD1_unique_DEGS <- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 5/Galaxy267-[NEUROD1_unique_DEG_intersection].tabular", header=TRUE, sep="\t", quote="", fill=FALSE,)
shared_DEGS <- read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 5/Galaxy265-[FOXG1-NEUROD1-shared_DEG_intersection].tabular", header=TRUE, sep="\t", quote="", fill=FALSE,)
FOXG1_unique_DEGS$log2FoldChange<-as.numeric(FOXG1_unique_DEGS$log2FoldChange)
NEUROD1_unique_DEGS$log2FoldChange<-as.numeric(NEUROD1_unique_DEGS$log2FoldChange)
shared_DEGS$log2FoldChange<-as.numeric(shared_DEGS$log2FoldChange)
FOXG1_unique_p<-FOXG1_unique_DEGS[FOXG1_unique_DEGS$padj<=0.01,]
NEUROD1_unique_p<-NEUROD1_unique_DEGS[NEUROD1_unique_DEGS$padj<=0.01,]
shared_p<-shared_DEGS[shared_DEGS$padj<=0.01,]- read in clustered annotated peaks
FOXG1_unique_peaks<-read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 5/Galaxy13-[FOXG1_unique_peaks].tabular", header=FALSE, sep="\t", quote="", fill=FALSE,)
NEUROD1_unique_peaks<-read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 5/Galaxy15-[neurod1_unique_peaks].tabular", header=FALSE, sep="\t", quote="", fill=FALSE,)
shared_peaks<-read.table("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Input Files/Figure 5/Galaxy14-[shared_peaks].tabular", header=FALSE, sep="\t", quote="", fill=FALSE,)- annotate peaks belonging to different clusters to entrezid
FOXG1_peaks_id<-bitr(FOXG1_unique_peaks$V13, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(FOXG1_unique_peaks$V13, fromType = "ENSEMBL", toType =
## "ENTREZID", : 20.82% of input gene IDs are fail to map...
NEUROD1_peaks_id<-bitr(NEUROD1_unique_peaks$V13, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(NEUROD1_unique_peaks$V13, fromType = "ENSEMBL", toType =
## "ENTREZID", : 25.56% of input gene IDs are fail to map...
shared_peaks_id<-bitr(shared_peaks$V13, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(shared_peaks$V13, fromType = "ENSEMBL", toType = "ENTREZID", :
## 24.43% of input gene IDs are fail to map...
- annotate DEGs belonging to different clusters to entrezid
FOXG1_unique_id<-bitr(FOXG1_unique_DEGS$geneName, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(FOXG1_unique_DEGS$geneName, fromType = "ENSEMBL", toType =
## "ENTREZID", : 6.83% of input gene IDs are fail to map...
NEUROD1_unique_id<-bitr(NEUROD1_unique_DEGS$geneName, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(NEUROD1_unique_DEGS$geneName, fromType = "ENSEMBL", toType =
## "ENTREZID", : 7.93% of input gene IDs are fail to map...
shared_id<-bitr(shared_DEGS$geneName, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(shared_DEGS$geneName, fromType = "ENSEMBL", toType =
## "ENTREZID", : 8.07% of input gene IDs are fail to map...
- annotate DEGs with p<0.01 cutoff to entrezid
FOXG1_unique_id_p<-bitr(FOXG1_unique_p$geneName, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(FOXG1_unique_p$geneName, fromType = "ENSEMBL", toType =
## "ENTREZID", : 14.73% of input gene IDs are fail to map...
NEUROD1_unique_id_p<-bitr(NEUROD1_unique_p$geneName, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(NEUROD1_unique_p$geneName, fromType = "ENSEMBL", toType =
## "ENTREZID", : 16.3% of input gene IDs are fail to map...
shared_id_p<-bitr(shared_p$geneName, fromType = "ENSEMBL",
toType = "ENTREZID",
OrgDb = org.Mm.eg.db,
drop = TRUE)## 'select()' returned 1:many mapping between keys and columns
## Warning in bitr(shared_p$geneName, fromType = "ENSEMBL", toType = "ENTREZID", :
## 16.38% of input gene IDs are fail to map...
list_FOXG1_NEUROD1_DEG<-list(FOXG1_unique=FOXG1_unique_id$ENTREZID,
NEUROD1_unique=NEUROD1_unique_id$ENTREZID,
shared=shared_id$ENTREZID
)
FOXG1_NEUROD1_DEG_cc<-compareCluster(geneClusters= list_FOXG1_NEUROD1_DEG,
fun = "enrichGO",
OrgDb = "org.Mm.eg.db",
ont = "BP",
pAdjustMethod = "BH",
qvalueCutoff = 0.01,
pvalueCutoff = 0.01,
readable = TRUE)- create a reference table for clustered GO terms and export to csv
FOXG1_NEUROD1_DEG_cc_df<- as.data.frame(FOXG1_NEUROD1_DEG_cc)
write.csv(FOXG1_NEUROD1_DEG_cc_df, file="~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/FOXG1_NEUROD1_DEG_clustered_GOterms_220622.csv")- simplify the terms to avoid redundancy
FOXG1_NEUROD1_DEG_cc_simp<-simplify(FOXG1_NEUROD1_DEG_cc,
cutoff = 0.7,
by = "p.adjust",
select_fun = min,
measure = "Wang",
semData = NULL)#embed dotplot of simplified terms
dp = dotplot(FOXG1_NEUROD1_DEG_cc_simp,
showCategory=10,
font.size=11)
dp
* embed dotplot of simplified terms only 5 categories
dp_5_nd1 = dotplot(FOXG1_NEUROD1_DEG_cc_simp,
showCategory=5,
font.size=11,
includeAll=FALSE
)
dp_5_nd1
* export the dotplot_5 categories to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/FOXG1_NEUROD1_clustered_DEGs_GOterms_dotplot_simp_2206.pdf", width=5, height=5.5)
print(dp)
dev.off()## png
## 2
list_FOXG1_NEUROD1_DEG_p<-list(
FOXG1_unique=FOXG1_unique_id_p$ENTREZID,
NEUROD1_unique=NEUROD1_unique_id_p$ENTREZID,
shared=shared_id_p$ENTREZID)
FOXG1_NEUROD1_DEG_cc_p<-compareCluster(
geneClusters= list_FOXG1_NEUROD1_DEG_p,
fun = "enrichGO",
OrgDb = "org.Mm.eg.db",
ont = "BP",
pAdjustMethod = "BH",
qvalueCutoff = 0.01,
pvalueCutoff = 0.01,
readable = TRUE)- create reference table for clustered GO terms (DEGs p<0.01) and export to csv
FOXG1_NEUROD1_DEG_cc_p_df<- as.data.frame(FOXG1_NEUROD1_DEG_cc)
write.csv(FOXG1_NEUROD1_DEG_cc_p_df, file="~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/FOXG1_NEUROD1_clustered_DEGs_p001_GOterms.csv")- simplify the terms to avoid redundancy
FOXG1_NEUROD1_DEG_cc_p_simp<-simplify(FOXG1_NEUROD1_DEG_cc_p,
cutoff = 0.7,
by = "p.adjust",
select_fun = min,
measure = "Wang",
semData = NULL)- embed dotplot of simplified terms
dp_DEG_p = dotplot(FOXG1_NEUROD1_DEG_cc_p_simp,
showCategory=10,
font.size=11,
includeAll=FALSE
)
dp_DEG_p
* embed dotplot of simplified terms only 5 categories
dp_5_nd1_p = dotplot(FOXG1_NEUROD1_DEG_cc_p_simp,
showCategory=5,
font.size=11,
)
dp_5_nd1_p
* export the dotplot categories to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/FOXG1_NEUROD1_clustered_DEGs_p001_GOterms_excl_dotplot_2206.pdf", width=5, height=5.5)
print(dp_DEG_p)
dev.off()## png
## 2
list_FOXG1_NEUROD1_peaks<-list(FOXG1_unique=FOXG1_peaks_id$ENTREZID,
NEUROD1_unique=NEUROD1_peaks_id$ENTREZID,
shared=shared_peaks_id$ENTREZID
)
FOXG1_NEUROD1_peak_cc<-compareCluster(geneClusters= list_FOXG1_NEUROD1_peaks,
fun = "enrichGO",
OrgDb = "org.Mm.eg.db",
ont = "BP",
pAdjustMethod = "BH",
qvalueCutoff = 0.001,
pvalueCutoff = 0.001,
readable = TRUE)- create reference table for GO terms and export to csv
FOXG1_NEUROD1_peaks_cc_df<- as.data.frame(FOXG1_NEUROD1_peak_cc)
write.csv(FOXG1_NEUROD1_peaks_cc_df, file="~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/FOXG1_NEUROD1_clustered_peaks_GOterms.csv")- simplify the terms to avoid redundancy
FOXG1_NEUROD1_peaks_cc_simp<-simplify(FOXG1_NEUROD1_peak_cc,
cutoff = 0.7,
by = "p.adjust",
select_fun = min,
measure = "Wang",
semData = NULL)- embed dotplot of simplified terms
dp_peaks = dotplot(FOXG1_NEUROD1_peaks_cc_simp,
showCategory=10,
font.size=11,
includeAll=FALSE
)
dp_peaks
* export the dotplot to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/FOXG1_NEUROD1_clustered_peaks_GOterms_dotplot_excl_simp_2206.pdf", width=5, height=5.5)
print(dp_peaks)
dev.off()## png
## 2
- embed dotplot of simplified terms only 5 categories
dp_5_nd1_peak = dotplot(FOXG1_NEUROD1_peaks_cc_simp,
showCategory=5,
font.size=18,
)
dp_5_nd1_peak
* export the dotplot_5 categories to pdf
pdf("~/Integrative-multi-omics-analyses-of-FOXG1-functions/Output/Figure 5/FOXG1_NEUROD1_clustered_peaks_GOterms_dotplot_simp_5categories", width=10, height=5)
print(dp_5_nd1_peak)
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] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggplot2_3.4.0 RColorBrewer_1.1-3 GeneOverlap_1.32.0
## [4] org.Mm.eg.db_3.15.0 AnnotationDbi_1.58.0 IRanges_2.30.1
## [7] S4Vectors_0.34.0 Biobase_2.56.0 BiocGenerics_0.42.0
## [10] clusterProfiler_4.4.4
##
## 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 tweenr_2.0.2 htmltools_0.5.3
## [34] tools_4.2.0 igraph_1.3.5 gtable_0.3.1
## [37] glue_1.6.2 GenomeInfoDbData_1.2.8 reshape2_1.4.4
## [40] DO.db_2.9 dplyr_1.0.10 fastmatch_1.1-3
## [43] Rcpp_1.0.9 enrichplot_1.16.2 vctrs_0.5.1
## [46] Biostrings_2.64.1 ape_5.6-2 nlme_3.1-160
## [49] ggraph_2.1.0 xfun_0.35 stringr_1.4.1
## [52] lifecycle_1.0.3 gtools_3.9.3 DOSE_3.22.1
## [55] zlibbioc_1.42.0 MASS_7.3-58.1 scales_1.2.1
## [58] tidygraph_1.2.2 parallel_4.2.0 yaml_2.3.6
## [61] memoise_2.0.1 gridExtra_2.3 downloader_0.4
## [64] ggfun_0.0.9 yulab.utils_0.0.5 stringi_1.7.8
## [67] RSQLite_2.2.19 highr_0.9 tidytree_0.4.1
## [70] caTools_1.18.2 BiocParallel_1.30.4 GenomeInfoDb_1.32.4
## [73] rlang_1.0.6 pkgconfig_2.0.3 bitops_1.0-7
## [76] evaluate_0.18 lattice_0.20-45 purrr_0.3.5
## [79] labeling_0.4.2 treeio_1.20.2 patchwork_1.1.2
## [82] shadowtext_0.1.2 bit_4.0.5 tidyselect_1.2.0
## [85] plyr_1.8.8 magrittr_2.0.3 R6_2.5.1
## [88] gplots_3.1.3 generics_0.1.3 DBI_1.1.3
## [91] pillar_1.8.1 withr_2.5.0 KEGGREST_1.36.3
## [94] RCurl_1.98-1.9 tibble_3.1.8 crayon_1.5.2
## [97] KernSmooth_2.23-20 utf8_1.2.2 rmarkdown_2.18
## [100] viridis_0.6.2 grid_4.2.0 data.table_1.14.6
## [103] blob_1.2.3 digest_0.6.30 tidyr_1.2.1
## [106] gridGraphics_0.5-1 munsell_0.5.0 viridisLite_0.4.1
## [109] ggplotify_0.1.0