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T21-XIST/comparing_to_other_XIST_systems.R
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| ## --------------------------- | |
| ## | |
| ## Purpose of script: | |
| ## | |
| ## Author: Prakhar Bansal | |
| ## | |
| ## Date Created: 2021-09-09 | |
| ## | |
| ## Copyright (c) Prakhar Bansal, 2021 | |
| ## Email: pbansal@uchc.edu | |
| ## | |
| ## --------------------------- | |
| ## | |
| ## Notes: | |
| ## | |
| ## | |
| ## --------------------------- | |
| ## set working directory | |
| setwd(dirname(unlist(rstudioapi::getSourceEditorContext()['path']))) | |
| ## --------------------------- | |
| ## load up the packages we will need: (uncomment as required) | |
| require(tidyverse); theme_set(theme_classic() + theme(plot.title=element_text(hjust=0.5))) | |
| require(ggsci) | |
| ## --------------------------- | |
| # lawrence lab neurons | |
| lawrence_neural_counts_df <- read_csv("external_tables/GSE125837_monolayer_rawcounts.txt.gz", | |
| col_names = c( | |
| "index", "gene_name","diff10.day21.c5a.d14dox", | |
| "diff10.day21.c5a.d0dox","diff10.day21.c5a.nd", | |
| "diff10.day28.c5a.d14dox","diff10.day28.c5a.d21dox", | |
| "diff10.day28.c5a.d0dox","diff10.day28.c5a_nd", | |
| "diff10.day28.dis.nd","diff10.day28.par.d0dox", | |
| "diff10.day28.par.nd","diff6.day28.par.d0dox", | |
| "diff6.day28.par.nd","diff7.day21.c5a.d14dox", | |
| "diff7.day21.c5a.d0dox","diff7.day21.c5a.nd", | |
| "diff7.day28.c5a.d14dox","diff7.day28.c5a.d21dox", | |
| "diff7.day28.c5a.d0dox","diff7.day28.c5a.nd", | |
| "diff7.day28.dis.nd","diff8.day21.c5a.d14dox", | |
| "diff8.day21.c5a.d0dox","diff8.day21.c5a.nd", | |
| "diff8.day28.c5a.d14dox","diff8.day28.c5a.d21dox", | |
| "diff8.day28.c5a.d0dox","diff8.day28.c5a.nd", | |
| "diff8.day28.dis.nd","diff8.day28.par.d0dox", | |
| "diff8.day28.par.nd"), | |
| skip = 1) |> | |
| dplyr::select(-index) | |
| lawrence_neural_samples_df <- read_csv("external_tables/lawrence_neural_sample_info.csv") | |
| library(DESeq2) | |
| dds_all <- DESeqDataSetFromMatrix( | |
| as.data.frame(lawrence_neural_counts_df), | |
| colData = lawrence_neural_samples_df |> dplyr::relocate(sample_name, .before = 1), | |
| design = ~ dox_status + genotype, | |
| tidy = T | |
| ) | |
| dds_all <- estimateSizeFactors(dds_all) | |
| nc <- counts(dds_all, normalized=TRUE) | |
| # only keep genes with more than 5 counts in at least 10% of samples | |
| filter <- rowSums(nc >= 5) >= floor(ncol(nc)/10) | |
| dds_all <- dds_all[filter,] | |
| dds_all <- DESeq(dds_all) | |
| dds_all <- dds_all[which(mcols(dds_all)$betaConv),] | |
| lawrence_neural_vst_df <- assay(vst(dds_all)) |> | |
| as.data.frame() |> | |
| rownames_to_column(var="gene_name") | |
| write_csv(lawrence_neural_vst_df, "tables/lawrence_neural_vst_df.csv.gz") | |
| library(biomaRt) | |
| mart <- useEnsembl(biomart = "ensembl", | |
| GRCh = 37, | |
| dataset = "hsapiens_gene_ensembl") | |
| G_list <- getBM(filters= "hgnc_symbol", | |
| attributes= c("ensembl_gene_id_version", | |
| "hgnc_symbol", | |
| "chromosome_name", | |
| "start_position", | |
| "end_position", | |
| "strand", | |
| "description", | |
| "wikigene_name", | |
| "gene_biotype"), | |
| values=lawrence_neural_counts_df$gene_name, | |
| mart= mart) | |
| gene_info_df <- as.data.frame(G_list) |> | |
| group_by(hgnc_symbol) |> | |
| filter(row_number()==1) |> | |
| ungroup() | |
| chr21_rownorm_lawrence_neural_foldchange <- lawrence_neural_vst_df |> | |
| left_join(gene_info_df |> | |
| dplyr::select(hgnc_symbol, chromosome_name, start_position), by=c("gene_name"="hgnc_symbol")) |> | |
| pivot_longer(cols = -c("gene_name", "chromosome_name", "start_position"), | |
| names_to = "sample_name", | |
| values_to = "vst_count") |> | |
| mutate(sample_name=case_when(sample_name == "diff10.day28.c5a_nd" ~ "diff10.day28.c5a.nd", | |
| T ~ sample_name)) |> | |
| left_join(lawrence_neural_samples_df |> | |
| dplyr::select(sample_name, | |
| dox_status, | |
| genotype, | |
| effective_genotype), | |
| by=c("sample_name")) |> | |
| filter(chromosome_name == "21") |> | |
| group_by(gene_name) |> | |
| summarise(d21_mean = mean(vst_count[`genotype` == "D21"]), | |
| t21_mean = mean(vst_count[`genotype` == "T21"]), | |
| ct21_mean = mean(vst_count[`genotype` == "T21" | (`genotype` == "T21+XIST" & dox_status == "nd")]), | |
| dox_count = mean(vst_count[`genotype` == "T21+XIST" & dox_status != "nd"]), | |
| nodox_count = mean(vst_count[`genotype` == "T21+XIST" & dox_status == "nd"]), | |
| fold_change_xist = dox_count - ct21_mean, | |
| fold_change_d21=d21_mean - ct21_mean) |> | |
| ungroup() #|> | |
| #top_frac(.5, abs(fold_change_d21)) | |
| output_dir <- "outputs/2024-02-17 21-39-53.641348 timepoint+dox_status+effective_genotype" | |
| neural_output_dir <- (read_csv(paste0(output_dir, "/params.csv.gz")) |> | |
| filter(parameter == "neural_output_dir") |> | |
| pull(value))[1] | |
| sample_sheet_df <- read_csv(paste0(output_dir, "/tables/sample_sheet_df.csv.gz")) | |
| batch_corrected_vsd <- read_csv(paste0(output_dir, "/tables/vsd_salmon_df.csv.gz")) | |
| neural_sample_sheet_df <- read_csv(paste0(neural_output_dir, "/tables/sample_sheet_df.csv.gz")) | |
| neural_vsd_df <- read_csv(paste0(neural_output_dir, "/tables/vsd_salmon_df.csv.gz")) | |
| gene_info_df <- read_csv("tables/gene_info_df.csv.gz") | |
| our_chr21_rownorm_vsd <- batch_corrected_vsd |> | |
| pivot_longer(cols = -ensembl_id, names_to = "sample_id", values_to = "vst_count") |> | |
| left_join(gene_info_df, by=c("ensembl_id"="ensembl_gene_id_version")) |> | |
| left_join(sample_sheet_df, by=c("sample_id")) |> | |
| filter(chromosome_name == "21" & | |
| hgnc_symbol %in% chr21_rownorm_lawrence_neural_foldchange$gene_name) |> | |
| group_by(hgnc_symbol) |> | |
| summarise(dox_mean= mean(vst_count[(genotype == "T21XIST") & (dox_status == "yes")]), | |
| nodox_mean=mean(vst_count[(genotype == "T21XIST") &( dox_status == "no")]), | |
| withdrawal_mean=mean(vst_count[(genotype == "T21XIST") & (dox_status == "withdrawal")]), | |
| dwd_mean=mean(vst_count[(genotype == "T21XIST") & (dox_status != "no")]), | |
| d21_mean=mean(vst_count[(genotype == "D21")]), | |
| t21_mean=mean(vst_count[(genotype == "T21")]), | |
| ct21_mean=mean(vst_count[(genotype == "T21") | (genotype == "T21XIST" & dox_status == "no")]), | |
| fold_change_xist=dwd_mean-ct21_mean, | |
| fold_change_wd=withdrawal_mean-nodox_mean, | |
| fold_change_d21=d21_mean - ct21_mean) #|> | |
| # top_frac(.5, abs(fold_change_d21)) | |
| our_neural_chr21_rownorm_vsd <- neural_vsd_df |> | |
| pivot_longer(cols = -ensembl_id, names_to = "sample_id", values_to = "vst_count") |> | |
| left_join(gene_info_df, by=c("ensembl_id"="ensembl_gene_id_version")) |> | |
| left_join(neural_sample_sheet_df, by=c("sample_id")) |> | |
| filter(chromosome_name == "21" & | |
| hgnc_symbol %in% chr21_rownorm_lawrence_neural_foldchange$gene_name) |> | |
| group_by(hgnc_symbol) |> | |
| summarise(dwd_mean= mean(vst_count[(genotype == "T21XIST") & (dox_status != "no")]), | |
| # withdrawal_mean=mean(vst_count[(genotype == "T21XIST") & (dox_status == "wd")]), | |
| d21_mean=mean(vst_count[(genotype == "D21")]), | |
| t21_mean=mean(vst_count[(genotype == "T21")]), | |
| ct21_mean=mean(vst_count[(genotype == "T21") | (genotype == "T21XIST" & dox_status == "no")]), | |
| fold_change_xist=dwd_mean-ct21_mean, | |
| # fold_change_wd=withdrawal_mean-t21_mean, | |
| fold_change_d21=d21_mean - ct21_mean) #|> | |
| # top_frac(.5, abs(fold_change_d21)) | |
| comparison_df <- chr21_rownorm_lawrence_neural_foldchange |> | |
| dplyr::select(hgnc_symbol=gene_name, fold_change_xist, fold_change_d21) |> | |
| pivot_longer(cols = -hgnc_symbol, | |
| names_to = "comparison", | |
| values_to = "vst_diff") |> | |
| mutate(study="Czermiński et al. Neural") |> | |
| rbind( | |
| our_chr21_rownorm_vsd |> | |
| dplyr::select(hgnc_symbol, fold_change_xist, fold_change_d21) |> | |
| pivot_longer(cols = -hgnc_symbol, | |
| names_to = "comparison", | |
| values_to = "vst_diff") |> | |
| mutate(study="This Study iPSCs") | |
| ) |> | |
| rbind( | |
| our_neural_chr21_rownorm_vsd |> | |
| dplyr::select(hgnc_symbol, fold_change_xist, fold_change_d21) |> | |
| pivot_longer(cols = -hgnc_symbol, | |
| names_to = "comparison", | |
| values_to = "vst_diff") |> | |
| mutate(study="This Study Neural") | |
| ) | |
| ggplot(comparison_df, aes(x=vst_diff, color=comparison)) + | |
| geom_density() + | |
| scale_color_aaas(limits=c("fold_change_d21", "fold_change_xist"), labels=c("D21 vs. T21", "T21XIST vs T21")) + | |
| facet_grid(rows = vars(study), scales = "free") + | |
| geom_vline(xintercept = log2(2/3), linetype=2, size=.3) + | |
| geom_vline(xintercept = 0, size=.5) + | |
| coord_cartesian(xlim = c(-1,1)) + | |
| labs(title="Dosage Restoration Systems", | |
| x="VST Difference to T21", | |
| color="Comparison") + | |
| theme(legend.position = c(.75, .85), | |
| legend.background = element_rect(color="black")) | |
| ggsave("output_figs/2024-02-17 21-39-53.641348 timepoint+dox_status+effective_genotype/system_comparison_same_genes.pdf", width=3.5, height=3.1) |