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Velmeshev_rds.R
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library(Seurat)
library(SeuratObject)
library(data.table)
library(stringr)
library(stringi)
library(tidyr)
library(ggplot2)
library(matrixStats)
library(dplyr)
`%!in%` <- Negate(`%in%`)
rds_path <- "UCSC/Seurat_UCSC/"
main_deg <- "UCSC/DEGs/"
main_scenic <- "UCSC/SCENIC/"
main <- "UCSC/outputs/Velmeshev/"
####################################################################################################
#
# ADULT
#
####################################################################################################
meta <- read.csv("UCSC/UCSC_downloads/new_meta_Velmeshev_2022.csv", header=T, sep=",", as.is=T, row.names=1)
rownames(meta) <- meta$cell
#meta$samples <- gsub(".*-","",meta$cell)
#meta$samples <- str_replace_all(meta$samples, "_", "-")
#write.csv(meta, "UCSC/UCSC_downloads/new_meta_Velmeshev_2022.csv")
meta_ad <- subset(meta, age=="Adult")
mat_ad <- fread("UCSC/UCSC_downloads/Velmeshev_outs/Adult.tsv.gz")
genes <- mat_ad[,1][[1]]
genes <- gsub(".+[|]", "", genes)
mat_ad <- data.frame(mat_ad[,-1], row.names=genes)
colnames(mat_ad) <- rownames(meta_ad)
velm_ad <- CreateSeuratObject(counts = mat_ad, project = "Velmeshev_2022_Adult", meta.data=meta_ad, assay = "RNA")
# Seurat tutorial https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
velm_ad[["percent.mt"]] <- PercentageFeatureSet(velm_ad, pattern = "^MT-")
# Visualize QC metrics as a violin plot
VlnPlot(velm_ad, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(velm_ad, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(velm_ad, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
velm_ad <- subset(velm_ad, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
velm_ad <- NormalizeData(velm_ad)
velm_ad <- FindVariableFeatures(velm_ad, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(velm_ad), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(velm_ad)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2
all.genes <- rownames(velm_ad)
velm_ad <- ScaleData(velm_ad, features = all.genes)
velm_ad <- RunPCA(velm_ad, features = VariableFeatures(object = velm_ad))
# Examine and visualize PCA results a few different ways
print(velm_ad[["pca"]], dims = 1:5, nfeatures = 5)
VizDimLoadings(velm_ad, dims = 1:2, reduction = "pca")
DimPlot(velm_ad, reduction = "pca")
DimHeatmap(velm_ad, dims = 1, cells = 500, balanced = TRUE)
# NOTE: This process can take a long time for big datasets, comment out for expediency. More
# approximate techniques such as those implemented in ElbowPlot() can be used to reduce
# computation time
velm_ad <- JackStraw(velm_ad, num.replicate = 100)
velm_ad <- ScoreJackStraw(velm_ad, dims = 1:20)
JackStrawPlot(velm_ad, dims = 1:15)
ElbowPlot(velm_ad)
# based on rprevious plots, decide the number of dimensions
velm_ad <- FindNeighbors(velm_ad, dims = 1:12)
velm_ad <- FindClusters(velm_ad, resolution = 0.5)
# Look at cluster IDs of the first 5 cells
#head(Idents(velm_ad), 5)
# If you haven't installed UMAP, you can do so via reticulate::py_install(packages =
# 'umap-learn')
velm_ad <- RunUMAP(velm_ad, dims = 1:12)
DimPlot(velm_ad, reduction = "umap")
velm_ad@meta.data$sex_age <- paste(velm_ad@meta.data$sex, velm_ad@meta.data$age, sep="_")
velm_ad@meta.data$proj <- rep(velm_ad@project.name, nrow(velm_ad@meta.data))
velm_ad@meta.data$id_sex_age <- paste(velm_ad@meta.data$samples, velm_ad@meta.data$sex, velm_ad@meta.data$age, sep="_")
VlnPlot(velm_ad, features = "XIST", group.by = "id_sex_age") + NoLegend()
ggsave(paste0(main, velm_ad@project.name, "_XIST.pdf"))
saveRDS(velm_ad, paste0(rds_path, velm_ad@project.name, ".rds"))
velm_num_cells <- as.data.frame(table(velm_ad$id_sex_age))
velm_num_cells <- separate(velm_num_cells, Var1, into=c("id", "sex", "age"), sep="_")
velm_num_cells <- cbind("proj" = rep(velm_ad@project.name, nrow(velm_num_cells)), velm_num_cells)
write.csv(velm_num_cells, "UCSC/outputs/Velmeshev_num_cells_per_age.csv")
################
velm_ad <- readRDS(paste0(rds_path, "Velmeshev_2022_Adult.rds"))
velm_ad@meta.data$cluster_final <- rep("no_data", nrow(velm_ad@meta.data))
present_clusters <- ann_df[which(ann_df$og_clusters %in% velm_ad@meta.data$cluster),]
for (ct in unique(present_clusters$cts)) {
print(ct)
for (og_cl in present_clusters[which(present_clusters$cts==ct), "og_clusters"]) {
velm_ad@meta.data[which(velm_ad@meta.data$cluster==og_cl), "cluster_final"] <- ct
}
}
DimPlot(velm_ad, reduction = "umap", group.by = "cluster_final")
pdf(paste0(main, velm_ad@project.name, "_cluster_final.pdf"))
print(DimPlot(velm_ad, reduction = "umap", group.by = "cluster_final"))
dev.off()
saveRDS(velm_ad, paste0(rds_path, velm_ad@project.name, ".rds"))
################
velm_ad <- readRDS(paste0(rds_path, "Velmeshev_2022_Adult.rds"))
velm_ad@meta.data$sex_ct <- paste(velm_ad@meta.data$sex, velm_ad@meta.data$cluster_final, sep="_")
velm_num_sex_ct <- as.data.frame(table(velm_ad$sex_ct))
velm_num_sex_ct <- separate(velm_num_sex_ct, Var1, into=c("sex", "ct"), sep="_")
velm_num_sex_ct <- cbind("proj" = rep(velm_ad@project.name, nrow(velm_num_sex_ct)), velm_num_sex_ct)
write.csv(velm_num_sex_ct, paste0(main, "Velmeshev_num_sex_ct_per_age.csv"))
saveRDS(velm_ad, paste0(rds_path, velm_ad@project.name, ".rds"))
################ FOR DEGs ANALYSIS
source("scripts/UCSC/RDS_preparation/prep_DEG_files.R")
velm_ad <- readRDS(paste0(rds_path, "Velmeshev_2022_Adult.rds"))
num_sex_ct <- NumSexCt(velm_ad)
min_num_cells <- c(10,50,100)
velm_ad_deg <- paste0(main_deg, velm_ad@project.name, "/outputs/")
dir.create(velm_ad_deg, recursive = T, showWarnings = F)
PlotFiltDf(min_num_cells, num_sex_ct, velm_ad_deg)
saveRDS(velm_ad, paste0(rds_path, velm_ad@project.name, ".rds"))
############ For 02C_Conservation
velm_ad <- readRDS(paste0(rds_path, "Velmeshev_2022_Adult.rds"))
cell_info <- CreateCellInfo(velm_ad, main_deg)
expr_mat_all <- GetAssayData(velm_ad[["RNA"]], slot="data")
expr_mat_all <- as.data.frame(as.matrix(expr_mat_all))
df_list <- list()
df_list_n <- vector()
for (df_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==df_id), "cell_id"])
df_og_df <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
df_list <- append(df_list, list(df_og_df))
df_list_n <- c(df_list_n, df_id)
}
names(df_list) <- df_list_n
GenerateTotGenesDf(df_list, main_deg, velm_ad)
############ For SCENIC - HIGHEST VARIABLE GENES
velm_ad <- readRDS(paste0(rds_path, "Velmeshev_2022_Adult.rds"))
velm_ad@meta.data$sample_id <- rownames(velm_ad@meta.data)
velm_ad@meta.data$sex_ct_sample <- paste(velm_ad@meta.data$sex_ct, velm_ad@meta.data$sample_id, sep="_")
Idents(velm_ad) <- "sex_ct_sample"
velm_ad_scenic <- paste0(main_scenic, velm_ad@project.name, "/")
dir.create(velm_ad_scenic, recursive = T, showWarnings = F)
Top2000ExprMtx(expr_mat_all, velm_ad_scenic, velm_ad)
##### Map the samples back to the groups they belong to
expr_mat_all <- readRDS(paste0(velm_ad_scenic, "top_2000_SD_expr_matrix_", velm_ad@project.name, ".rds"))
group_list <- list()
group_list_n <- vector()
for (group_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==group_id), "cell_id"])
df_og_group <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
group_list <- append(group_list, list(df_og_group))
group_list_n <- c(group_list_n, group_id)
}
names(group_list) <- group_list_n
PlotGroupNumbers(group_list, 100, velm_ad_scenic)
PlotGroupNumbers(group_list, 500, velm_ad_scenic)
###### Create Randomly sampled dfs
expr_mat_all <- readRDS(paste0(velm_ad_scenic, "top_2000_SD_expr_matrix_", velm_ad@project.name, ".rds"))
cell_info <- read.csv(paste0(main_deg, velm_ad@project.name, "/cell_info_", velm_ad@project.name, ".csv"))
cell_info$X <- NULL
df_list100 <- group_list
df_list100 <- RemoveDfs(df_list100, 100)
CheckDfs(df_list100)
RandomSampling(df_list100, 3, 100, velm_ad_scenic)
####################################################################################################
#
# 3RD TRIMESTER
#
####################################################################################################
meta <- read.csv("UCSC/UCSC_downloads/new_meta_Velmeshev_2022.csv", header=T, sep=",", as.is=T, row.names=1)
rownames(meta) <- meta$cell
meta_3rd_trim <- subset(meta, age=="3rd trimester")
mat_3rd_trim_1 <- fread("UCSC/UCSC_downloads/Velmeshev_outs/3rd_trimester_1.tsv.gz")
mat_3rd_trim_2 <- fread("UCSC/UCSC_downloads/Velmeshev_outs/3rd_trimester_2.tsv.gz")
mat_3rd_trim_2[,1] <- NULL
colnames(mat_3rd_trim_1) <- c("gene", (seq(1,(ncol(mat_3rd_trim_1)-1))))
colnames(mat_3rd_trim_2) <- c("gene", (seq(ncol(mat_3rd_trim_1), (ncol(mat_3rd_trim_1) + ncol(mat_3rd_trim_2) - 2))))
mat_3rd_trim <- merge(mat_3rd_trim_1, mat_3rd_trim_2, by="gene")
rm(mat_3rd_trim_1, mat_3rd_trim_2)
genes <- mat_3rd_trim[,1][[1]]
genes <- gsub(".+[|]", "", genes)
mat_3rd_trim <- data.frame(mat_3rd_trim[,-1], row.names=genes)
colnames(mat_3rd_trim) <- rownames(meta_3rd_trim)
velm_3rd_trim <- CreateSeuratObject(counts = mat_3rd_trim, project = "Velmeshev_2022_3rd_trimester", meta.data=meta_3rd_trim, assay = "RNA")
saveRDS(velm_3rd_trim, paste0(rds_path, velm_3rd_trim@project.name, ".rds"))
rm(meta_3rd_trim, mat_3rd_trim)
# Seurat tutorial https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
velm_3rd_trim[["percent.mt"]] <- PercentageFeatureSet(velm_3rd_trim, pattern = "^MT-")
# Visualize QC metrics as a violin plot
VlnPlot(velm_3rd_trim, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(velm_3rd_trim, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(velm_3rd_trim, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
velm_3rd_trim <- subset(velm_3rd_trim, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
velm_3rd_trim <- NormalizeData(velm_3rd_trim)
velm_3rd_trim <- FindVariableFeatures(velm_3rd_trim, selection.method = "vst", nfeatures = 2000)
saveRDS(velm_3rd_trim, paste0(rds_path, velm_3rd_trim@project.name, ".rds"))
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(velm_3rd_trim), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(velm_3rd_trim)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2
all.genes <- rownames(velm_3rd_trim)
velm_3rd_trim <- ScaleData(velm_3rd_trim, features = all.genes)
velm_3rd_trim <- RunPCA(velm_3rd_trim, features = VariableFeatures(object = velm_3rd_trim))
# Examine and visualize PCA results a few different ways
print(velm_3rd_trim[["pca"]], dims = 1:5, nfeatures = 5)
VizDimLoadings(velm_3rd_trim, dims = 1:2, reduction = "pca")
DimPlot(velm_3rd_trim, reduction = "pca")
DimHeatmap(velm_3rd_trim, dims = 1, cells = 500, balanced = TRUE)
# NOTE: This process can take a long time for big datasets, comment out for expediency. More
# approximate techniques such as those implemented in ElbowPlot() can be used to reduce
# computation time
velm_3rd_trim <- JackStraw(velm_3rd_trim, num.replicate = 100)
velm_3rd_trim <- ScoreJackStraw(velm_3rd_trim, dims = 1:20)
JackStrawPlot(velm_3rd_trim, dims = 1:15)
ElbowPlot(velm_3rd_trim)
#saveRDS(velm_3rd_trim, paste0("UCSC/Seurat_UCSC/", velm_3rd_trim@project.name, ".rds"))
# based on rprevious plots, decide the number of dimensions
velm_3rd_trim <- FindNeighbors(velm_3rd_trim, dims = 1:12)
velm_3rd_trim <- FindClusters(velm_3rd_trim, resolution = 0.5)
# Look at cluster IDs of the first 5 cells
#head(Idents(velm_3rd_trim), 5)
# If you haven't installed UMAP, you can do so via reticulate::py_install(packages =
# 'umap-learn')
velm_3rd_trim <- RunUMAP(velm_3rd_trim, dims = 1:12)
DimPlot(velm_3rd_trim, reduction = "umap")
velm_3rd_trim@meta.data$sex_age <- paste(velm_3rd_trim@meta.data$sex, velm_3rd_trim@meta.data$age, sep="_")
velm_3rd_trim@meta.data$proj <- rep(velm_3rd_trim@project.name, nrow(velm_3rd_trim@meta.data))
velm_3rd_trim@meta.data$id_sex_age <- paste(velm_3rd_trim@meta.data$samples, velm_3rd_trim@meta.data$sex, velm_3rd_trim@meta.data$age, sep="_")
VlnPlot(velm_3rd_trim, features = "XIST", group.by = "id_sex_age")
ggsave(paste0(main, velm_3rd_trim@project.name, "_XIST.pdf"))
saveRDS(velm_3rd_trim, paste0(rds_path, velm_3rd_trim@project.name, ".rds"))
num_cells <- read.csv("UCSC/outputs/Velmeshev_num_cells_per_age.csv")
num_cells[,1] <- NULL
velm_num_cells <- as.data.frame(table(velm_3rd_trim$id_sex_age))
velm_num_cells <- separate(velm_num_cells, Var1, into=c("id", "sex", "age"), sep="_")
velm_num_cells <- cbind("proj" = rep(velm_3rd_trim@project.name, nrow(velm_num_cells)), velm_num_cells)
num_cells <- rbind(num_cells, velm_num_cells)
write.csv(num_cells, "UCSC/outputs/Velmeshev_num_cells_per_age.csv")
################
velm_3rd_trim <- readRDS(paste0(rds_path, "Velmeshev_2022_3rd_trimester.rds"))
velm_3rd_trim@meta.data$cluster_final <- rep("no_data", nrow(velm_3rd_trim@meta.data))
present_clusters <- ann_df[which(ann_df$og_clusters %in% velm_3rd_trim@meta.data$cluster),]
for (ct in unique(present_clusters$cts)) {
print(ct)
for (og_cl in present_clusters[which(present_clusters$cts==ct), "og_clusters"]) {
velm_3rd_trim@meta.data[which(velm_3rd_trim@meta.data$cluster==og_cl), "cluster_final"] <- ct
}
}
DimPlot(velm_3rd_trim, reduction = "umap", group.by = "cluster_final")
pdf(paste0(main, velm_3rd_trim@project.name, "_cluster_final.pdf"))
print(DimPlot(velm_3rd_trim, reduction = "umap", group.by = "cluster_final"))
dev.off()
saveRDS(velm_3rd_trim, paste0(rds_path, velm_3rd_trim@project.name, ".rds"))
################
velm_3rd_trim <- readRDS(paste0(rds_path, "Velmeshev_2022_3rd_trimester.rds"))
velm_3rd_trim@meta.data$sex_ct <- paste(velm_3rd_trim@meta.data$sex, velm_3rd_trim@meta.data$cluster_final, sep="_")
velm_num_sex_ct <- as.data.frame(table(velm_3rd_trim$sex_ct))
velm_num_sex_ct <- separate(velm_num_sex_ct, Var1, into=c("sex", "ct"), sep="_")
velm_num_sex_ct <- cbind("proj" = rep(velm_3rd_trim@project.name, nrow(velm_num_sex_ct)), velm_num_sex_ct)
velm_num_sex_ct_all <- read.csv("UCSC/outputs/Velmeshev/Velmeshev_num_sex_ct_per_age.csv")
velm_num_sex_ct_all[,1] <- NULL
velm_num_sex_ct_all <- rbind(velm_num_sex_ct_all, velm_num_sex_ct)
write.csv(velm_num_sex_ct_all, paste0(main, "Velmeshev_num_sex_ct_per_age.csv"))
####################################################################################################
#
# 0-1 YEARS
#
####################################################################################################
#meta <- read.csv("UCSC/UCSC_downloads/new_meta_Velmeshev_2022.csv", header=T, sep=",", as.is=T, row.names=1)
#rownames(meta) <- meta$cell
meta_1st_year <- subset(meta, age=="0-1 years")
mat_1st_year_1 <- fread("UCSC/UCSC_downloads/Velmeshev_outs/0_1_years_1.tsv.gz")
mat_1st_year_2 <- fread("UCSC/UCSC_downloads/Velmeshev_outs/0_1_years_2.tsv.gz")
mat_1st_year_1[,1] <- NULL
colnames(mat_1st_year_1) <- c("gene", (seq(1,(ncol(mat_1st_year_1)-1))))
colnames(mat_1st_year_2) <- c("gene", (seq(ncol(mat_1st_year_1), (ncol(mat_1st_year_1) + ncol(mat_1st_year_2) - 2))))
mat_1st_year <- merge(mat_1st_year_1, mat_1st_year_2, by="gene")
rm(mat_1st_year_1, mat_1st_year_2)
genes <- mat_1st_year[,1][[1]]
genes <- gsub(".+[|]", "", genes)
mat_1st_year <- data.frame(mat_1st_year[,-1], row.names=genes)
colnames(mat_1st_year) <- rownames(meta_1st_year)
velm_1st_year <- CreateSeuratObject(counts = mat_1st_year, project = "Velmeshev_2022_0_1_years", meta.data=meta_1st_year, assay = "RNA")
rm(meta_1st_year, mat_1st_year)
saveRDS(velm_1st_year, paste0("UCSC/Seurat_UCSC/", velm_1st_year@project.name, ".rds"))
# Seurat tutorial https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
velm_1st_year[["percent.mt"]] <- PercentageFeatureSet(velm_1st_year, pattern = "^MT-")
# Visualize QC metrics as a violin plot
VlnPlot(velm_1st_year, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(velm_1st_year, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(velm_1st_year, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
velm_1st_year <- subset(velm_1st_year, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
velm_1st_year <- NormalizeData(velm_1st_year)
velm_1st_year <- FindVariableFeatures(velm_1st_year, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(velm_1st_year), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(velm_1st_year)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2
all.genes <- rownames(velm_1st_year)
velm_1st_year <- ScaleData(velm_1st_year, features = all.genes)
velm_1st_year <- RunPCA(velm_1st_year, features = VariableFeatures(object = velm_1st_year))
# Examine and visualize PCA results a few different ways
print(velm_1st_year[["pca"]], dims = 1:5, nfeatures = 5)
VizDimLoadings(velm_1st_year, dims = 1:2, reduction = "pca")
DimPlot(velm_1st_year, reduction = "pca")
DimHeatmap(velm_1st_year, dims = 1, cells = 500, balanced = TRUE)
# NOTE: This process can take a long time for big datasets, comment out for expediency. More
# approximate techniques such as those implemented in ElbowPlot() can be used to reduce
# computation time
velm_1st_year <- JackStraw(velm_1st_year, num.replicate = 100)
velm_1st_year <- ScoreJackStraw(velm_1st_year, dims = 1:20)
JackStrawPlot(velm_1st_year, dims = 1:15)
ElbowPlot(velm_1st_year)
# based on rprevious plots, decide the number of dimensions
velm_1st_year <- FindNeighbors(velm_1st_year, dims = 1:13)
velm_1st_year <- FindClusters(velm_1st_year, resolution = 0.5)
# Look at cluster IDs of the first 5 cells
#head(Idents(velm_1st_year), 5)
# If you haven't installed UMAP, you can do so via reticulate::py_install(packages =
# 'umap-learn')
velm_1st_year <- RunUMAP(velm_1st_year, dims = 1:12)
DimPlot(velm_1st_year, reduction = "umap")
velm_1st_year@meta.data$sex_age <- paste(velm_1st_year@meta.data$sex, velm_1st_year@meta.data$age, sep="_")
velm_1st_year@meta.data$proj <- rep(velm_1st_year@project.name, nrow(velm_1st_year@meta.data))
velm_1st_year@meta.data$id_sex_age <- paste(velm_1st_year@meta.data$samples, velm_1st_year@meta.data$sex, velm_1st_year@meta.data$age, sep="_")
VlnPlot(velm_1st_year, features = "XIST", group.by = "id_sex_age") + NoLegend()
ggsave(paste0(main, velm_1st_year@project.name, "_XIST.pdf"))
saveRDS(velm_1st_year, paste0("UCSC/Seurat_UCSC/", velm_1st_year@project.name, ".rds"))
num_cells <- read.csv("UCSC/outputs/Velmeshev_num_cells_per_age.csv")
num_cells[,1] <- NULL
velm_num_cells <- as.data.frame(table(velm_1st_year$id_sex_age))
velm_num_cells <- separate(velm_num_cells, Var1, into=c("id", "sex", "age"), sep="_")
velm_num_cells <- cbind("proj" = rep(velm_1st_year@project.name, nrow(velm_num_cells)), velm_num_cells)
num_cells <- rbind(num_cells, velm_num_cells)
write.csv(num_cells, "UCSC/outputs/Velmeshev_num_cells_per_age.csv")
################
velm_1st_year <- readRDS("UCSC/Seurat_UCSC/Velmeshev_2022_0_1_years.rds")
velm_1st_year@meta.data$cluster_final <- rep("no_data", nrow(velm_1st_year@meta.data))
present_clusters <- ann_df[which(ann_df$og_clusters %in% velm_1st_year@meta.data$cluster),]
for (ct in unique(present_clusters$cts)) {
print(ct)
for (og_cl in present_clusters[which(present_clusters$cts==ct), "og_clusters"]) {
velm_1st_year@meta.data[which(velm_1st_year@meta.data$cluster==og_cl), "cluster_final"] <- ct
}
}
DimPlot(velm_1st_year, reduction = "umap", group.by = "cluster_final")
pdf(paste0(main, velm_1st_year@project.name, "_cluster_final.pdf"))
print(DimPlot(velm_1st_year, reduction = "umap", group.by = "cluster_final"))
dev.off()
saveRDS(velm_1st_year, paste0("UCSC/Seurat_UCSC/", velm_1st_year@project.name, ".rds"))
################
velm_1st_year <- readRDS("UCSC/Seurat_UCSC/Velmeshev_2022_0_1_years.rds")
velm_1st_year@meta.data$sex_ct <- paste(velm_1st_year@meta.data$sex, velm_1st_year@meta.data$cluster_final, sep="_")
velm_num_sex_ct <- as.data.frame(table(velm_1st_year$sex_ct))
velm_num_sex_ct <- separate(velm_num_sex_ct, Var1, into=c("sex", "ct"), sep="_")
velm_num_sex_ct <- cbind("proj" = rep(velm_1st_year@project.name, nrow(velm_num_sex_ct)), velm_num_sex_ct)
velm_num_sex_ct_all <- read.csv("UCSC/outputs/Velmeshev/Velmeshev_num_sex_ct_per_age.csv")
velm_num_sex_ct_all[,1] <- NULL
velm_num_sex_ct_all <- rbind(velm_num_sex_ct_all, velm_num_sex_ct)
write.csv(velm_num_sex_ct_all, paste0(main, "Velmeshev_num_sex_ct_per_age.csv"))
################ FOR DEGs ANALYSIS
source("scripts/UCSC/RDS_preparation/prep_DEG_files.R")
velm_1st_year <- readRDS(paste0(rds_path, "Velmeshev_2022_0_1_years.rds"))
velm_1st_year@meta.data$sex_ct <- paste(velm_1st_year@meta.data$sex, velm_1st_year@meta.data$cluster_final, sep="_")
num_sex_ct <- NumSexCt(velm_1st_year)
min_num_cells <- c(10,50,100)
velm_1st_year_deg <- paste0(main_deg, velm_1st_year@project.name, "/outputs/")
dir.create(velm_1st_year_deg, recursive = T, showWarnings = F)
PlotFiltDf(min_num_cells, num_sex_ct, velm_1st_year_deg)
############ For 02C_Conservation
cell_info <- CreateCellInfo(velm_1st_year, main_deg)
expr_mat_all <- GetAssayData(velm_1st_year[["RNA"]], slot="data")
expr_mat_all <- as.data.frame(as.matrix(expr_mat_all))
df_list <- list()
df_list_n <- vector()
for (df_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==df_id), "cell_id"])
df_og_df <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
df_list <- append(df_list, list(df_og_df))
df_list_n <- c(df_list_n, df_id)
}
names(df_list) <- df_list_n
GenerateTotGenesDf(df_list, main_deg, velm_1st_year)
############ For SCENIC - HIGHEST VARIABLE GENES
velm_1st_year@meta.data$sample_id <- rownames(velm_1st_year@meta.data)
velm_1st_year@meta.data$sex_ct_sample <- paste(velm_1st_year@meta.data$sex_ct, velm_1st_year@meta.data$sample_id, sep="_")
Idents(velm_1st_year) <- "sex_ct_sample"
velm_1st_year_scenic <- paste0(main_scenic, velm_1st_year@project.name, "/")
dir.create(velm_1st_year_scenic, recursive = T, showWarnings = F)
Top2000ExprMtx(expr_mat_all, velm_1st_year_scenic, velm_1st_year)
##### Map the samples back to the groups they belong to
group_list <- list()
group_list_n <- vector()
for (group_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==group_id), "cell_id"])
df_og_group <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
group_list <- append(group_list, list(df_og_group))
group_list_n <- c(group_list_n, group_id)
}
names(group_list) <- group_list_n
PlotGroupNumbers(group_list, 100, velm_1st_year_scenic)
PlotGroupNumbers(group_list, 500, velm_1st_year_scenic)
###### Create Randomly sampled dfs
expr_mat_all <- readRDS(paste0(velm_1st_year_scenic, "top_2000_SD_expr_matrix_", velm_1st_year@project.name, ".rds"))
cell_info <- read.csv(paste0(main_deg, velm_1st_year@project.name, "/cell_info_", velm_1st_year@project.name, ".csv"))
cell_info$X <- NULL
df_list100 <- group_list
df_list100 <- RemoveDfs(df_list100, 100)
CheckDfs(df_list100)
RandomSampling(df_list100, 3, 100, velm_1st_year_scenic)
saveRDS(velm_1st_year, paste0(rds_path, velm_1st_year@project.name, ".rds"))
####################################################################################################
#
# 1-2- YEARS
#
####################################################################################################
#meta <- read.csv("UCSC/UCSC_downloads/new_meta_Velmeshev_2022.csv", header=T, sep=",", as.is=T, row.names=1)
#rownames(meta) <- meta$cell
meta_2nd_year <- subset(meta, age=="1-2 years")
mat_2nd_year <- fread("UCSC/UCSC_downloads/Velmeshev_outs/1_2_years.tsv.gz")
genes <- mat_2nd_year[,1][[1]]
genes <- gsub(".+[|]", "", genes)
mat_2nd_year <- data.frame(mat_2nd_year[,-1], row.names=genes)
colnames(mat_2nd_year) <- rownames(meta_2nd_year)
velm_2nd_year <- CreateSeuratObject(counts = mat_2nd_year, project = "Velmeshev_2022_1_2_years", meta.data=meta_2nd_year, assay = "RNA")
rm(meta_2nd_year, mat_2nd_year)
saveRDS(velm_2nd_year, paste0("UCSC/Seurat_UCSC/", velm_2nd_year@project.name, ".rds"))
# Seurat tutorial https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
velm_2nd_year[["percent.mt"]] <- PercentageFeatureSet(velm_2nd_year, pattern = "^MT-")
# Visualize QC metrics as a violin plot
VlnPlot(velm_2nd_year, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(velm_2nd_year, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(velm_2nd_year, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
velm_2nd_year <- subset(velm_2nd_year, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
velm_2nd_year <- NormalizeData(velm_2nd_year)
velm_2nd_year <- FindVariableFeatures(velm_2nd_year, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(velm_2nd_year), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(velm_2nd_year)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2
all.genes <- rownames(velm_2nd_year)
velm_2nd_year <- ScaleData(velm_2nd_year, features = all.genes)
velm_2nd_year <- RunPCA(velm_2nd_year, features = VariableFeatures(object = velm_2nd_year))
# Examine and visualize PCA results a few different ways
print(velm_2nd_year[["pca"]], dims = 1:5, nfeatures = 5)
VizDimLoadings(velm_2nd_year, dims = 1:2, reduction = "pca")
DimPlot(velm_2nd_year, reduction = "pca")
DimHeatmap(velm_2nd_year, dims = 1, cells = 500, balanced = TRUE)
# NOTE: This process can take a long time for big datasets, comment out for expediency. More
# approximate techniques such as those implemented in ElbowPlot() can be used to reduce
# computation time
velm_2nd_year <- JackStraw(velm_2nd_year, num.replicate = 100)
velm_2nd_year <- ScoreJackStraw(velm_2nd_year, dims = 1:20)
JackStrawPlot(velm_2nd_year, dims = 1:15)
ElbowPlot(velm_2nd_year)
# based on rprevious plots, decide the number of dimensions
velm_2nd_year <- FindNeighbors(velm_2nd_year, dims = 1:12)
velm_2nd_year <- FindClusters(velm_2nd_year, resolution = 0.5)
# Look at cluster IDs of the first 5 cells
#head(Idents(velm_2nd_year), 5)
# If you haven't installed UMAP, you can do so via reticulate::py_install(packages =
# 'umap-learn')
velm_2nd_year <- RunUMAP(velm_2nd_year, dims = 1:12)
DimPlot(velm_2nd_year, reduction = "umap")
velm_2nd_year@meta.data$sex_age <- paste(velm_2nd_year@meta.data$sex, velm_2nd_year@meta.data$age, sep="_")
velm_2nd_year@meta.data$proj <- rep(velm_2nd_year@project.name, nrow(velm_2nd_year@meta.data))
velm_2nd_year@meta.data$id_sex_age <- paste(velm_2nd_year@meta.data$samples, velm_2nd_year@meta.data$sex, velm_2nd_year@meta.data$age, sep="_")
VlnPlot(velm_2nd_year, features = "XIST", group.by = "id_sex_age") + NoLegend()
ggsave(paste0(main, velm_2nd_year@project.name, "_XIST.pdf"))
saveRDS(velm_2nd_year, paste0("UCSC/Seurat_UCSC/", velm_2nd_year@project.name, ".rds"))
num_cells <- read.csv("UCSC/outputs/Velmeshev_num_cells_per_age.csv")
num_cells[,1] <- NULL
velm_num_cells <- as.data.frame(table(velm_2nd_year$id_sex_age))
velm_num_cells <- separate(velm_num_cells, Var1, into=c("id", "sex", "age"), sep="_")
velm_num_cells <- cbind("proj" = rep(velm_2nd_year@project.name, nrow(velm_num_cells)), velm_num_cells)
num_cells <- rbind(num_cells, velm_num_cells)
write.csv(num_cells, "UCSC/outputs/Velmeshev_num_cells_per_age.csv")
################
velm_2nd_year <- readRDS("UCSC/Seurat_UCSC/Velmeshev_2022_1_2_years.rds")
velm_2nd_year@meta.data$cluster_final <- rep("no_data", nrow(velm_2nd_year@meta.data))
present_clusters <- ann_df[which(ann_df$og_clusters %in% velm_2nd_year@meta.data$cluster),]
for (ct in unique(present_clusters$cts)) {
print(ct)
for (og_cl in present_clusters[which(present_clusters$cts==ct), "og_clusters"]) {
velm_2nd_year@meta.data[which(velm_2nd_year@meta.data$cluster==og_cl), "cluster_final"] <- ct
}
}
DimPlot(velm_2nd_year, reduction = "umap", group.by = "cluster_final")
pdf(paste0(main, velm_2nd_year@project.name, "_cluster_final.pdf"))
print(DimPlot(velm_2nd_year, reduction = "umap", group.by = "cluster_final"))
dev.off()
saveRDS(velm_2nd_year, paste0("UCSC/Seurat_UCSC/", velm_2nd_year@project.name, ".rds"))
################
velm_2nd_year <- readRDS("UCSC/Seurat_UCSC/Velmeshev_2022_1_2_years.rds")
velm_2nd_year@meta.data$sex_ct <- paste(velm_2nd_year@meta.data$sex, velm_2nd_year@meta.data$cluster_final, sep="_")
velm_num_sex_ct <- as.data.frame(table(velm_2nd_year$sex_ct))
velm_num_sex_ct <- separate(velm_num_sex_ct, Var1, into=c("sex", "ct"), sep="_")
velm_num_sex_ct <- cbind("proj" = rep(velm_2nd_year@project.name, nrow(velm_num_sex_ct)), velm_num_sex_ct)
velm_num_sex_ct_all <- read.csv("UCSC/outputs/Velmeshev/Velmeshev_num_sex_ct_per_age.csv")
velm_num_sex_ct_all[,1] <- NULL
velm_num_sex_ct_all <- rbind(velm_num_sex_ct_all, velm_num_sex_ct)
write.csv(velm_num_sex_ct_all, paste0(main, "Velmeshev_num_sex_ct_per_age.csv"))
################ FOR DEGs ANALYSIS
source("scripts/UCSC/RDS_preparation/prep_DEG_files.R")
velm_2nd_year <- readRDS(paste0(rds_path, "Velmeshev_2022_1_2_years.rds"))
velm_2nd_year@meta.data$sex_ct <- paste(velm_2nd_year@meta.data$sex, velm_2nd_year@meta.data$cluster_final, sep="_")
num_sex_ct <- NumSexCt(velm_2nd_year)
min_num_cells <- c(10,50,100)
velm_2nd_year_deg <- paste0(main_deg, velm_2nd_year@project.name, "/outputs/")
dir.create(velm_2nd_year_deg, recursive = T, showWarnings = F)
PlotFiltDf(min_num_cells, num_sex_ct, velm_2nd_year_deg)
############ For 02C_Conservation
cell_info <- CreateCellInfo(velm_2nd_year, main_deg)
expr_mat_all <- GetAssayData(velm_2nd_year[["RNA"]], slot="data")
expr_mat_all <- as.data.frame(as.matrix(expr_mat_all))
df_list <- list()
df_list_n <- vector()
for (df_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==df_id), "cell_id"])
df_og_df <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
df_list <- append(df_list, list(df_og_df))
df_list_n <- c(df_list_n, df_id)
}
names(df_list) <- df_list_n
GenerateTotGenesDf(df_list, main_deg, velm_2nd_year)
############ For SCENIC - HIGHEST VARIABLE GENES
velm_2nd_year@meta.data$sample_id <- rownames(velm_2nd_year@meta.data)
velm_2nd_year@meta.data$sex_ct_sample <- paste(velm_2nd_year@meta.data$sex_ct, velm_2nd_year@meta.data$sample_id, sep="_")
Idents(velm_2nd_year) <- "sex_ct_sample"
velm_2nd_year_scenic <- paste0(main_scenic, velm_2nd_year@project.name, "/")
dir.create(velm_2nd_year_scenic, recursive = T, showWarnings = F)
Top2000ExprMtx(expr_mat_all, velm_2nd_year_scenic, velm_2nd_year)
##### Map the samples back to the groups they belong to
group_list <- list()
group_list_n <- vector()
for (group_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==group_id), "cell_id"])
df_og_group <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
group_list <- append(group_list, list(df_og_group))
group_list_n <- c(group_list_n, group_id)
}
names(group_list) <- group_list_n
PlotGroupNumbers(group_list, 100, velm_2nd_year_scenic)
PlotGroupNumbers(group_list, 500, velm_2nd_year_scenic)
###### Create Randomly sampled dfs
expr_mat_all <- readRDS(paste0(velm_2nd_year_scenic, "top_2000_SD_expr_matrix_", velm_2nd_year@project.name, ".rds"))
cell_info <- read.csv(paste0(main_deg, velm_2nd_year@project.name, "/cell_info_", velm_2nd_year@project.name, ".csv"))
cell_info$X <- NULL
df_list100 <- group_list
df_list100 <- RemoveDfs(df_list100, 100)
CheckDfs(df_list100)
RandomSampling(df_list100, 3, 100, velm_2nd_year_scenic)
saveRDS(velm_2nd_year, paste0(rds_path, velm_2nd_year@project.name, ".rds"))
####################################################################################################
#
# 2-4 YEARS
#
####################################################################################################
meta <- read.csv("UCSC/UCSC_downloads/new_meta_Velmeshev_2022.csv", header=T, sep=",", as.is=T, row.names=1)
rownames(meta) <- meta$cell
meta_2_4_years <- subset(meta, age=="2-4 years")
mat_2_4_years <- fread("UCSC/UCSC_downloads/Velmeshev_outs/2_4_years.tsv.gz")
genes <- mat_2_4_years[,1][[1]]
genes <- gsub(".+[|]", "", genes)
mat_2_4_years <- data.frame(mat_2_4_years[,-1], row.names=genes)
colnames(mat_2_4_years) <- rownames(meta_2_4_years)
velm_2_4_years <- CreateSeuratObject(counts = mat_2_4_years, project = "Velmeshev_2022_2_4_years", meta.data=meta_2_4_years, assay = "RNA")
rm(meta_2_4_years, mat_2_4_years, genes)
#saveRDS(velm_2_4_years, paste0("UCSC/Seurat_UCSC/", velm_2_4_years@project.name, ".rds"))
# Seurat tutorial https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
velm_2_4_years[["percent.mt"]] <- PercentageFeatureSet(velm_2_4_years, pattern = "^MT-")
# Visualize QC metrics as a violin plot
VlnPlot(velm_2_4_years, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(velm_2_4_years, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(velm_2_4_years, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
velm_2_4_years <- subset(velm_2_4_years, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
velm_2_4_years <- NormalizeData(velm_2_4_years)
velm_2_4_years <- FindVariableFeatures(velm_2_4_years, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(velm_2_4_years), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(velm_2_4_years)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)
plot1 + plot2
all.genes <- rownames(velm_2_4_years)
velm_2_4_years <- ScaleData(velm_2_4_years, features = all.genes)
velm_2_4_years <- RunPCA(velm_2_4_years, features = VariableFeatures(object = velm_2_4_years))
# Examine and visualize PCA results a few different ways
print(velm_2_4_years[["pca"]], dims = 1:5, nfeatures = 5)
VizDimLoadings(velm_2_4_years, dims = 1:2, reduction = "pca")
DimPlot(velm_2_4_years, reduction = "pca")
DimHeatmap(velm_2_4_years, dims = 1, cells = 500, balanced = TRUE)
# NOTE: This process can take a long time for big datasets, comment out for expediency. More
# approximate techniques such as those implemented in ElbowPlot() can be used to reduce
# computation time
velm_2_4_years <- JackStraw(velm_2_4_years, num.replicate = 100)
velm_2_4_years <- ScoreJackStraw(velm_2_4_years, dims = 1:20)
JackStrawPlot(velm_2_4_years, dims = 1:15)
ElbowPlot(velm_2_4_years)
# based on rprevious plots, decide the number of dimensions
velm_2_4_years <- FindNeighbors(velm_2_4_years, dims = 1:13)
velm_2_4_years <- FindClusters(velm_2_4_years, resolution = 0.5)
# Look at cluster IDs of the first 5 cells
#head(Idents(velm_2_4_years), 5)
# If you haven't installed UMAP, you can do so via reticulate::py_install(packages =
# 'umap-learn')
velm_2_4_years <- RunUMAP(velm_2_4_years, dims = 1:12)
DimPlot(velm_2_4_years, reduction = "umap")
velm_2_4_years@meta.data$sex_age <- paste(velm_2_4_years@meta.data$sex, velm_2_4_years@meta.data$age, sep="_")
velm_2_4_years@meta.data$proj <- rep(velm_2_4_years@project.name, nrow(velm_2_4_years@meta.data))
velm_2_4_years@meta.data$id_sex_age <- paste(velm_2_4_years@meta.data$samples, velm_2_4_years@meta.data$sex, velm_2_4_years@meta.data$age, sep="_")
VlnPlot(velm_2_4_years, features = "XIST", group.by = "id_sex_age") + NoLegend()
ggsave(paste0(main, velm_2_4_years@project.name, "_XIST.pdf"))
saveRDS(velm_2_4_years, paste0("UCSC/Seurat_UCSC/", velm_2_4_years@project.name, ".rds"))
num_cells <- read.csv("UCSC/outputs/Velmeshev_num_cells_per_age.csv")
num_cells[,1] <- NULL
velm_num_cells <- as.data.frame(table(velm_2_4_years$id_sex_age))
velm_num_cells <- separate(velm_num_cells, Var1, into=c("id", "sex", "age"), sep="_")
velm_num_cells <- cbind("proj" = rep(velm_2_4_years@project.name, nrow(velm_num_cells)), velm_num_cells)
num_cells <- rbind(num_cells, velm_num_cells)
write.csv(num_cells, "UCSC/outputs/Velmeshev_num_cells_per_age.csv")
################
velm_2_4_years <- readRDS("UCSC/Seurat_UCSC/Velmeshev_2022_2_4_years.rds")
velm_2_4_years@meta.data$cluster_final <- rep("no_data", nrow(velm_2_4_years@meta.data))
present_clusters <- ann_df[which(ann_df$og_clusters %in% velm_2_4_years@meta.data$cluster),]
for (ct in unique(present_clusters$cts)) {
print(ct)
for (og_cl in present_clusters[which(present_clusters$cts==ct), "og_clusters"]) {
velm_2_4_years@meta.data[which(velm_2_4_years@meta.data$cluster==og_cl), "cluster_final"] <- ct
}
}
DimPlot(velm_2_4_years, reduction = "umap", group.by = "cluster_final")
pdf(paste0(main, velm_2_4_years@project.name, "_cluster_final.pdf"))
print(DimPlot(velm_2_4_years, reduction = "umap", group.by = "cluster_final"))
dev.off()
saveRDS(velm_2_4_years, paste0("UCSC/Seurat_UCSC/", velm_2_4_years@project.name, ".rds"))
################
velm_2_4_years <- readRDS("UCSC/Seurat_UCSC/Velmeshev_2022_2_4_years.rds")
velm_2_4_years@meta.data$sex_ct <- paste(velm_2_4_years@meta.data$sex, velm_2_4_years@meta.data$cluster_final, sep="_")
velm_num_sex_ct <- as.data.frame(table(velm_2_4_years$sex_ct))
velm_num_sex_ct <- separate(velm_num_sex_ct, Var1, into=c("sex", "ct"), sep="_")
velm_num_sex_ct <- cbind("proj" = rep(velm_2_4_years@project.name, nrow(velm_num_sex_ct)), velm_num_sex_ct)
velm_num_sex_ct_all <- read.csv("UCSC/outputs/Velmeshev/Velmeshev_num_sex_ct_per_age.csv")
velm_num_sex_ct_all[,1] <- NULL
velm_num_sex_ct_all <- rbind(velm_num_sex_ct_all, velm_num_sex_ct)
write.csv(velm_num_sex_ct_all, paste0(main, "Velmeshev_num_sex_ct_per_age.csv"))
################ FOR DEGs ANALYSIS
source("scripts/UCSC/RDS_preparation/prep_DEG_files.R")
velm_2_4_years <- readRDS(paste0(rds_path, "Velmeshev_2022_2_4_years.rds"))
velm_2_4_years@meta.data$sex_ct <- paste(velm_2_4_years@meta.data$sex, velm_2_4_years@meta.data$cluster_final, sep="_")
num_sex_ct <- NumSexCt(velm_2_4_years)
min_num_cells <- c(10,50,100)
velm_2_4_years_deg <- paste0(main_deg, velm_2_4_years@project.name, "/outputs/")
dir.create(velm_2_4_years_deg, recursive = T, showWarnings = F)
PlotFiltDf(min_num_cells, num_sex_ct, velm_2_4_years_deg)
############ For 02C_Conservation
cell_info <- CreateCellInfo(velm_2_4_years, main_deg)
expr_mat_all <- GetAssayData(velm_2_4_years[["RNA"]], slot="data")
expr_mat_all <- as.data.frame(as.matrix(expr_mat_all))
df_list <- list()
df_list_n <- vector()
for (df_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==df_id), "cell_id"])
df_og_df <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
df_list <- append(df_list, list(df_og_df))
df_list_n <- c(df_list_n, df_id)
}
names(df_list) <- df_list_n
GenerateTotGenesDf(df_list, main_deg, velm_2_4_years)
############ For SCENIC - HIGHEST VARIABLE GENES
velm_2_4_years@meta.data$sample_id <- rownames(velm_2_4_years@meta.data)
velm_2_4_years@meta.data$sex_ct_sample <- paste(velm_2_4_years@meta.data$sex_ct, velm_2_4_years@meta.data$sample_id, sep="_")
Idents(velm_2_4_years) <- "sex_ct_sample"
velm_2_4_years_scenic <- paste0(main_scenic, velm_2_4_years@project.name, "/")
dir.create(velm_2_4_years_scenic, recursive = T, showWarnings = F)
Top2000ExprMtx(expr_mat_all, velm_2_4_years_scenic, velm_2_4_years)
##### Map the samples back to the groups they belong to
group_list <- list()
group_list_n <- vector()
for (group_id in unique(cell_info$og_group)) {
og_cells <- c("Genes", cell_info[which(cell_info$og_group==group_id), "cell_id"])
df_og_group <- expr_mat_all[ , (names(expr_mat_all) %in% og_cells)]
group_list <- append(group_list, list(df_og_group))
group_list_n <- c(group_list_n, group_id)
}
names(group_list) <- group_list_n
PlotGroupNumbers(group_list, 100, velm_2_4_years_scenic)
PlotGroupNumbers(group_list, 500, velm_2_4_years_scenic)
###### Create Randomly sampled dfs
expr_mat_all <- readRDS(paste0(velm_2_4_years_scenic, "top_2000_SD_expr_matrix_", velm_2_4_years@project.name, ".rds"))
cell_info <- read.csv(paste0(main_deg, velm_2_4_years@project.name, "/cell_info_", velm_2_4_years@project.name, ".csv"))
cell_info$X <- NULL
df_list100 <- group_list
df_list100 <- RemoveDfs(df_list100, 100)
CheckDfs(df_list100)
RandomSampling(df_list100, 3, 100, velm_2_4_years_scenic)
saveRDS(velm_2_4_years, paste0(rds_path, velm_2_4_years@project.name, ".rds"))
####################################################################################################
#
# 4-10 YEARS
#
####################################################################################################
meta <- read.csv("UCSC/UCSC_downloads/new_meta_Velmeshev_2022.csv", header=T, sep=",", as.is=T, row.names=1)
rownames(meta) <- meta$cell
meta_4_10_years <- subset(meta, age=="4-10 years")
mat_4_10_years <- fread("UCSC/UCSC_downloads/Velmeshev_outs/4_10_years.tsv.gz")
genes <- mat_4_10_years[,1][[1]]
genes <- gsub(".+[|]", "", genes)
mat_4_10_years <- data.frame(mat_4_10_years[,-1], row.names=genes)
colnames(mat_4_10_years) <- rownames(meta_4_10_years)
velm_4_10_years <- CreateSeuratObject(counts = mat_4_10_years, project = "Velmeshev_2022_4_10_years", meta.data=meta_4_10_years, assay = "RNA")
rm(meta_4_10_years, mat_4_10_years, genes)
#saveRDS(velm_4_10_years, paste0("UCSC/Seurat_UCSC/", velm_4_10_years@project.name, ".rds"))
# Seurat tutorial https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
velm_4_10_years[["percent.mt"]] <- PercentageFeatureSet(velm_4_10_years, pattern = "^MT-")
# Visualize QC metrics as a violin plot
VlnPlot(velm_4_10_years, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(velm_4_10_years, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(velm_4_10_years, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
velm_4_10_years <- subset(velm_4_10_years, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
velm_4_10_years <- NormalizeData(velm_4_10_years)
velm_4_10_years <- FindVariableFeatures(velm_4_10_years, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes