Agrp_Analysis
Last updated: 2019-10-29
Checks: 6 1
Knit directory: fgf_alldata/
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| html | 9cf1e45 | Full Name | 2019-10-28 | Build site. |
library(Seurat)
library(tidyverse)
library(gProfileR)
library(ggraph)
library(future)
library(org.Mm.eg.db)
library(reactome.db)
library(ggraph)
library(igraph)
library(DESeq2)
library(here)
library(tidyverse)
library(ggrepel)
library(ggpubr)
library(wesanderson)
library(tidygraph)
library(ggforce)
library(reshape2)
library(ggbeeswarm)
library(ggsci)
library(cowplot)
library(gt)
plan(multiprocess, workers=16)
options(future.globals.maxSize = 4000 * 1024^2)Load prepped data
fgf.neur.sub<-readRDS(here("data/neuron/neurons_seur_filtered.RDS"))Specific Agrp analysis
agrp<-subset(fgf.neur.sub, ident="Agrp")
agrp %>% ScaleData(verbose=F) %>%
FindVariableFeatures(selection.method = "vst", nfeatures = 2000) %>%
RunPCA(ndims.print=1:10)->agrp
list_sub<-SplitObject(agrp, split.by="sample")
pb<-(lapply(list_sub, function(y) {
DefaultAssay(y) <- "SCT"
mat<-GetAssayData(y, slot="counts")
counts <- Matrix::rowSums(mat)
}) %>% do.call(rbind, .) %>% t() %>% as.data.frame())
trt<-ifelse(grepl("FGF", colnames(pb)), yes="F", no="P")
batch<-as.factor(sapply(strsplit(colnames(pb),"_"),"[",1))
day<-ifelse(as.numeric(as.character(batch))>10, yes="Day-5", no="Day-1")
group<-paste0(trt,"_",day)
meta<-data.frame(trt=trt, day=factor(day), group=group)
dds <- DESeqDataSetFromMatrix(countData = pb,
colData = meta,
design = ~ 0 + group)
keep <- rowSums(counts(dds) >= 5) > 5
dds <- dds[keep,]
dds<-DESeq(dds)
res_5<-results(dds, contrast = c("group","F_Day-5","P_Day-5"))
res_1<-results(dds, contrast = c("group","F_Day-1","P_Day-1"))
f_5_1<-results(dds, contrast = c("group","F_Day-5","F_Day-1"))
p_5_1<-results(dds, contrast = c("group","P_Day-5","P_Day-1"))Filter 24 hr results
res_1<-as.data.frame(res_1)
res_1<-res_1[complete.cases(res_1),]
res_1<-res_1[order(res_1$padj),]
res_1$gene<-rownames(res_1)
write_csv(res_1, path=here("output/neuron/agrp_24hr_dge.csv"))Volcano Plot of DE genes
res_1 %>% add_rownames("gene") %>%
mutate(siglog=ifelse(padj<0.05&abs(log2FoldChange)>1, yes=T, no=F)) %>%
mutate(onlysig=ifelse(padj<0.05&abs(log2FoldChange)<1, yes=T, no=F)) %>%
mutate(onlylog=ifelse(padj>0.05&abs(log2FoldChange)>1, yes=T, no=F)) %>%
mutate(col=ifelse(siglog==T, yes="1", no =
ifelse(onlysig==T, yes="2", no =
ifelse(onlylog==T, yes="3", no="4")))) %>%
mutate(label=ifelse(padj<0.01, yes=gene, no="")) %>%
dplyr::select(gene, log2FoldChange, padj, col, label) -> volcWarning: Deprecated, use tibble::rownames_to_column() instead.ggplot(volc, aes(y=(-log10(padj)), x=log2FoldChange, fill=factor(col), label=label)) +
xlab(expression(Log[2]*~Fold*~Change)) + ylab(expression(-Log[10]*~pvalue))+
geom_point(shape=21, size=3, alpha=0.75) + geom_hline(yintercept = -log10(0.05), linetype="dashed") +
geom_vline(xintercept = c(-1,1), linetype="dashed") + geom_text_repel() + theme_pubr() + labs_pubr() +
theme(legend.position = "none") +
scale_fill_manual(values = wes_palette("Royal1", 4, type="discrete"))
| Version | Author | Date |
|---|---|---|
| 9cf1e45 | Full Name | 2019-10-28 |
GO Term Analysis
resgo<-res_1[res_1$padj<0.1,]
resgo<-resgo[resgo$log2FoldChange>0,]
ego<-gprofiler(rownames(resgo), organism = "mmusculus", significant = T, custom_bg = rownames(dds),
src_filter = c("GO:BP","GO:MF","REAC","KEGG"),hier_filtering = "strong",
min_isect_size = 3,
sort_by_structure = T,exclude_iea = T,
min_set_size = 10, max_set_size = 300,correction_method = "fdr")
write_csv(ego, path=here("output/neuron/agrp_24hr_goterms.csv"))
ego %>% arrange(p.value) %>%
select(domain, term.name, p.value) %>%
head(10) %>%
gt()| domain | term.name | p.value |
|---|---|---|
| BP | regulation of membrane potential | 0.000773 |
| MF | voltage-gated cation channel activity | 0.001780 |
| BP | inorganic cation transmembrane transport | 0.002580 |
| BP | animal organ morphogenesis | 0.008260 |
| BP | multicellular organism growth | 0.011000 |
| BP | cellular response to nitrogen compound | 0.014400 |
| BP | regulation of long-term synaptic potentiation | 0.020600 |
| rea | Neuronal System | 0.023700 |
| BP | transmembrane receptor protein tyrosine kinase signaling pathway | 0.029900 |
| BP | synapse organization | 0.031300 |
GO similarity plot
mouse.GO <- as.data.frame(org.Mm.egGO2ALLEGS)[,c("gene_id","go_id")]
mouse.PATH <- as.data.frame(org.Mm.egPATH2EG)[,c("gene_id","path_id")]
mouse.PATH$path_id<-paste0("KEGG:",mouse.PATH$path_id)
mouse.REAC <- as.data.frame(reactomePATHID2EXTID)[,c("gene_id","DB_ID")]
colnames(mouse.REAC)[2]<-"path_id"
mouse.REAC$path_id<-paste0("REAC:",mouse.REAC$path_id)
colnames(mouse.GO)[2]<-"path_id"
allpaths<-rbind(mouse.GO, mouse.PATH)
jac<-allpaths[allpaths$path_id%in%ego$term.id,]
jac_list<-split(jac$gene_id, f = jac$path_id)
df<-stringdist::seq_distmatrix(jac_list,method="jw")
attributes(df)$Labels<-ego[match(attributes(df)$Labels, ego$term.id),"term.name"]
g<-graph.adjacency(
as.matrix(df),
mode="undirected",
weighted=TRUE,
diag=T)
g<-delete_edges(g,which(E(g)$weight>.6))
g<-as_tbl_graph(g)
g %>% activate(nodes) %>%
mutate(db = factor(toupper(ego[match(name, ego$term.name),"domain"]))) %>%
mutate(pval = -log10(ego[match(name, ego$term.name),"p.value"])) -> g
g %>% activate(nodes) %>%
mutate(community = as.factor(group_edge_betweenness())) %>% group_by(community) %>%
mutate(label=ifelse(pval==max(pval),name, NA)) -> g
set.seed("139")
ggraph(g, layout = "fr") +
geom_edge_link(color="black", aes(width = weight), alpha = 0.2, show.legend = F) +
scale_edge_width(range = c(0.2, 1)) +
geom_node_point(aes(size=pval, colour=db)) + scale_size(range = c(2,10)) + guides(colour = guide_legend(override.aes = list(size = 5))) +
geom_mark_hull(aes(x=x,y=y, fill=community), show.legend = F) +
geom_label_repel(aes(x=x,y=y,label=str_wrap(label,20)),fontface="bold", size=4, min.segment.length = .1, nudge_y = .5, alpha=0.5) +
labs(colour="Database", size=expression(log[10]*pvalue)) +
theme_graph()Warning: Removed 7 rows containing missing values (geom_label_repel).
| Version | Author | Date |
|---|---|---|
| 9cf1e45 | Full Name | 2019-10-28 |
ggsave(filename = here("output/neuron/agrp_go_graph.png"))Warning: Removed 7 rows containing missing values (geom_label_repel).D5 v D1
embed <- data.frame(Embeddings(agrp, reduction = "pca")[,1:10])
embed$group <- agrp$group
embed <- melt(embed, id.vars = "group")
ggplot(embed[embed$variable%in%c("PC_1","PC_2","PC_3","PC_4","PC_5","PC_6"),], aes(x = group, y=value)) +
geom_quasirandom(aes(fill=group), alpha=.5, shape=21) +
facet_wrap(.~variable, scales="free") +
scale_fill_jco() + theme_pubr() +
theme(legend.position = "none", axis.text.x = element_text(angle=45, hjust=1)) +
ylab("PC Embedding Value") + xlab(NULL) + labs_pubr()
| Version | Author | Date |
|---|---|---|
| 9cf1e45 | Full Name | 2019-10-28 |
ggsave(filename = here("output/neuron/agrp_pc_graph.png"))Calculate Variance Explained
mat <- Seurat::GetAssayData(agrp, assay = "SCT", slot = "scale.data")
pca <- agrp[["pca"]]
# Get the total variance:
total_variance <- sum(matrixStats::rowVars(mat))
eigValues = Stdev(object = agrp, reduction = "pca")^2
varExplained = eigValues / total_varianceExtract genes from top loading PCs
pc1<-rownames(agrp@reductions$pca[order(agrp@reductions$pca[,1]),])[1:100]
pc4<-rownames(agrp@reductions$pca[order(-agrp@reductions$pca[,4]),])[1:100]
pc6<-rownames(agrp@reductions$pca[order(-agrp@reductions$pca[,6]),])[1:100]
imp_pcs<-data.frame(pc1=rownames(agrp@reductions$pca[order(agrp@reductions$pca[,1]),]), pc4=rownames(agrp@reductions$pca[order(-agrp@reductions$pca[,4]),]),pc6=rownames(agrp@reductions$pca[order(-agrp@reductions$pca[,6]),]))
write_csv(imp_pcs, path=here("output/agrp_pcgenes.csv"))res_5<-as.data.frame(res_5)
res_5<-res_5[complete.cases(res_5),]
res_5[order(res_5$pvalue),] %>% add_rownames("gene") %>% filter(baseMean>100)Warning: Deprecated, use tibble::rownames_to_column() instead.# A tibble: 92 x 7
gene baseMean log2FoldChange lfcSE stat pvalue padj
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Cntn5 173. 1.74 0.487 3.57 0.000354 0.883
2 Mylip 233. -1.47 0.527 -2.79 0.00523 1.000
3 Pcdh15 147. 1.06 0.383 2.77 0.00563 1.000
4 Nlgn1 181. 0.850 0.349 2.44 0.0148 1.000
5 Pcdh7 194. 0.925 0.400 2.31 0.0208 1.000
6 Galntl6 142. 0.912 0.408 2.24 0.0254 1.000
7 Nrg1 157. 0.773 0.375 2.06 0.0392 1.000
8 Lrrtm4 167. 0.860 0.449 1.92 0.0554 1.000
9 Npy 1264. -1.33 0.716 -1.85 0.0643 1.000
10 Syt1 103. 0.761 0.421 1.81 0.0708 1.000
# … with 82 more rowsGO term enrichment for PC6 (highest value for FGF_D5)
pc6_go<-gprofiler(pc6, organism = "mmusculus", significant = T,
src_filter = c("GO:BP","GO:MF","REAC", "KEGG"),hier_filtering = "strong",
min_isect_size = 3,
sort_by_structure = T,exclude_iea = T,
min_set_size = 10, max_set_size = 500,correction_method = "fdr")
write_csv(pc6_go, path=here("output/neuron/agrp_PC6_goterms.csv"))
pc6_go %>% arrange(p.value) %>%
select(domain, term.name, p.value) %>%
head(10) %>%
gt()| domain | term.name | p.value |
|---|---|---|
| rea | Neuronal System | 2.59e-06 |
| BP | forebrain development | 1.14e-04 |
| BP | regulation of transmembrane transport | 1.02e-03 |
| BP | presynaptic membrane organization | 1.02e-03 |
| BP | cell-cell adhesion via plasma-membrane adhesion molecules | 2.07e-03 |
| BP | cellular potassium ion transport | 2.41e-03 |
| BP | positive regulation of neurogenesis | 2.41e-03 |
| BP | regulation of cell morphogenesis | 4.47e-03 |
| keg | Gap junction | 4.72e-03 |
| BP | negative regulation of STAT cascade | 4.78e-03 |
Plot top loading genes
imp_gene<-data.frame(t(agrp[["SCT"]]@scale.data[c("Agrp","Npy","Cntn5"),]))
imp_gene$group<-agrp$group
imp_gene$Sample<-agrp$sample
imp_gene<-melt(imp_gene, id.vars = c("group","Sample"))
ggplot(imp_gene[sample(nrow(imp_gene)),], aes(x=group, y=value)) +
geom_quasirandom(aes(fill=Sample),alpha=.85, shape=21) +
facet_wrap(.~variable, scales = "free", nrow = 1) + theme_pubr() +
theme(axis.text.x = element_text(angle=45, hjust=1), legend.position = "right") +
ylab("Normalized Expression") + xlab(NULL) + labs_pubr()
| Version | Author | Date |
|---|---|---|
| 9cf1e45 | Full Name | 2019-10-28 |
ggsave(filename = here("output/neuron/agrp_imp_gene.png"))
sessionInfo()R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Storage
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
locale:
[1] LC_CTYPE=en_DK.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_DK.UTF-8 LC_COLLATE=en_DK.UTF-8
[5] LC_MONETARY=en_DK.UTF-8 LC_MESSAGES=en_DK.UTF-8
[7] LC_PAPER=en_DK.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] gt_0.1.0 cowplot_1.0.0
[3] ggsci_2.9 ggbeeswarm_0.6.0
[5] reshape2_1.4.3 ggforce_0.3.0.9000
[7] tidygraph_1.1.2 wesanderson_0.3.6.9000
[9] ggpubr_0.2.1 magrittr_1.5
[11] ggrepel_0.8.1 here_0.1
[13] DESeq2_1.22.2 SummarizedExperiment_1.12.0
[15] DelayedArray_0.8.0 BiocParallel_1.16.6
[17] matrixStats_0.54.0 GenomicRanges_1.34.0
[19] GenomeInfoDb_1.18.2 igraph_1.2.4.1
[21] reactome.db_1.66.0 org.Mm.eg.db_3.7.0
[23] AnnotationDbi_1.44.0 IRanges_2.16.0
[25] S4Vectors_0.20.1 Biobase_2.42.0
[27] BiocGenerics_0.28.0 future_1.14.0
[29] ggraph_1.0.2 gProfileR_0.6.7
[31] forcats_0.4.0 stringr_1.4.0
[33] dplyr_0.8.3 purrr_0.3.2
[35] readr_1.3.1.9000 tidyr_0.8.3
[37] tibble_2.1.3 ggplot2_3.2.1
[39] tidyverse_1.2.1 Seurat_3.0.3.9036
loaded via a namespace (and not attached):
[1] utf8_1.1.4 reticulate_1.13 R.utils_2.9.0
[4] tidyselect_0.2.5 RSQLite_2.1.1 htmlwidgets_1.3
[7] grid_3.5.3 Rtsne_0.15 munsell_0.5.0
[10] codetools_0.2-16 ica_1.0-2 withr_2.1.2
[13] colorspace_1.4-1 highr_0.8 knitr_1.23
[16] rstudioapi_0.10 ROCR_1.0-7 ggsignif_0.5.0
[19] gbRd_0.4-11 listenv_0.7.0 labeling_0.3
[22] Rdpack_0.11-0 git2r_0.25.2 GenomeInfoDbData_1.2.0
[25] polyclip_1.10-0 bit64_0.9-7 farver_1.1.0
[28] rprojroot_1.3-2 vctrs_0.2.0 generics_0.0.2
[31] xfun_0.8 R6_2.4.0 rsvd_1.0.2
[34] locfit_1.5-9.1 concaveman_1.0.0 bitops_1.0-6
[37] assertthat_0.2.1 SDMTools_1.1-221.1 scales_1.0.0
[40] nnet_7.3-12 beeswarm_0.2.3 gtable_0.3.0
[43] npsurv_0.4-0 globals_0.12.4 workflowr_1.4.0
[46] rlang_0.4.0 genefilter_1.64.0 zeallot_0.1.0
[49] splines_3.5.3 lazyeval_0.2.2 acepack_1.4.1
[52] checkmate_1.9.4 broom_0.5.2 yaml_2.2.0
[55] modelr_0.1.4 backports_1.1.4 Hmisc_4.2-0
[58] tools_3.5.3 gplots_3.0.1.1 RColorBrewer_1.1-2
[61] ggridges_0.5.1 Rcpp_1.0.2 plyr_1.8.4
[64] base64enc_0.1-3 zlibbioc_1.28.0 RCurl_1.95-4.12
[67] rpart_4.1-15 pbapply_1.4-1 viridis_0.5.1
[70] zoo_1.8-6 haven_2.1.0 cluster_2.1.0
[73] fs_1.3.1 data.table_1.12.2 lmtest_0.9-37
[76] RANN_2.6.1 whisker_0.3-2 fitdistrplus_1.0-14
[79] xtable_1.8-4 hms_0.5.0 lsei_1.2-0
[82] evaluate_0.14 XML_3.98-1.20 readxl_1.3.1
[85] gridExtra_2.3 compiler_3.5.3 V8_2.3
[88] KernSmooth_2.23-15 crayon_1.3.4 R.oo_1.22.0
[91] htmltools_0.3.6 Formula_1.2-3 geneplotter_1.60.0
[94] RcppParallel_4.4.3 lubridate_1.7.4 DBI_1.0.0
[97] tweenr_1.0.1 MASS_7.3-51.4 Matrix_1.2-17
[100] cli_1.1.0 R.methodsS3_1.7.1 gdata_2.18.0
[103] metap_1.1 pkgconfig_2.0.2 foreign_0.8-71
[106] plotly_4.9.0 xml2_1.2.0 annotate_1.60.1
[109] vipor_0.4.5 stringdist_0.9.5.2 XVector_0.22.0
[112] bibtex_0.4.2 rvest_0.3.4 digest_0.6.20
[115] sctransform_0.2.0 RcppAnnoy_0.0.12 tsne_0.1-3
[118] rmarkdown_1.13 cellranger_1.1.0 leiden_0.3.1
[121] htmlTable_1.13.1 uwot_0.1.3 curl_4.0
[124] gtools_3.8.1 nlme_3.1-140 jsonlite_1.6
[127] fansi_0.4.0 viridisLite_0.3.0 pillar_1.4.2
[130] lattice_0.20-38 httr_1.4.1 survival_2.44-1.1
[133] glue_1.3.1 png_0.1-7 bit_1.1-14
[136] sass_0.1.2.1 stringi_1.4.3 blob_1.1.1
[139] latticeExtra_0.6-28 caTools_1.17.1.2 memoise_1.1.0
[142] irlba_2.3.3 future.apply_1.3.0 ape_5.3