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1.5.2_D0_Ref_TF_renaming.Rmd
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---
title: "1.5.2 D0 assignment, Adipo_ref & Myo_ref calculation, and TF renaming"
author: "Vincent Gardeux"
date: "2023/12/19"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = "./")
getwd()
```
## Libraries & functions
First, I'm loading the required libraries & functions
```{r}
suppressPackageStartupMessages(library(Seurat)) # For handling 10x h5 file
suppressPackageStartupMessages(library(data.table)) # For fread/fwrite functions
suppressPackageStartupMessages(library(crayon)) # Just for bolding the console output :D
cat(bold("Seurat"), "version", as.character(packageVersion("Seurat")), "\n")
cat(bold("data.table"), "version", as.character(packageVersion("data.table")), "\n")
```
### Calculating Adipo_ref from D4-D5 adipo cells in all exps (except exp12-13)
Calculation was done outside of this script, I just load the cells
```{r}
# Adipo_ref
adipo_ref <- fread("metadata/Adipo_ref_cells.txt", data.table = F, header = F)$V1
message(length(adipo_ref), " Adipo_ref cells")
```
Creating a list of all exps
```{r}
## Full list
data.seurat_all <- list()
```
#### Exp05
```{r}
# D0 metadata
d0 <- c("mCherry-BC1", "mCherry-BC3")
d0_confluent <- c("mCherry-BC11", "mCherry-BC12")
# Reading file
data.seurat <- readRDS("results/C3H10_10X_exp05_enriched.cellranger_kneepoint_outliers_cellcycle.rds")
# Assigning D0 cells
data.seurat$TF[data.seurat$TF %in% d0] <- "D0"
data.seurat$TF[data.seurat$TF %in% d0_confluent] <- "D0_confluent"
# Assigning Adipo_ref cells
data.seurat$TF[colnames(data.seurat) %in% adipo_ref] <- "Adipo_ref"
# Filtering the Seurat object
data.seurat <- data.seurat[,!startsWith(data.seurat$TF, "mCherry-")]
# Renaming deprecated TF names
data.seurat$TF[data.seurat$TF == "NeuroD2"] <- "Neurod2"
# Removing TF cells with less than 8 cells / TF
removed_tfs <- names(table(data.seurat$TF))[table(data.seurat$TF) < 8]
message("Removing ",sum(data.seurat$TF %in% removed_tfs), " TF cells with less than 8 cells / TF: ", paste0("[", paste0(removed_tfs, collapse = ", "), "]"))
data.seurat <- data.seurat[,!data.seurat$TF %in% removed_tfs]
# Putting Seurat object in the merged list
data.seurat_all[["exp05"]] <- data.seurat
```
#### Exp06
```{r}
# D0 metadata
d0 <- c("mCherry-BC1", "mCherry-BC3")
d0_confluent <- c("mCherry-BC11", "mCherry-BC12")
# Reading file
data.seurat <- readRDS("results/C3H10_10X_exp06_enriched.cellranger_kneepoint_outliers_cellcycle.rds")
# Assigning D0 cells
data.seurat$TF[data.seurat$TF %in% d0] <- "D0"
data.seurat$TF[data.seurat$TF %in% d0_confluent] <- "D0_confluent"
# Assigning Adipo_ref cells
data.seurat$TF[colnames(data.seurat) %in% adipo_ref] <- "Adipo_ref"
# Filtering the Seurat object
data.seurat <- data.seurat[,!startsWith(data.seurat$TF, "mCherry-")]
# Renaming deprecated TF names
data.seurat$TF[data.seurat$TF == "B230358A15Rik"] <- "Hsf3"
data.seurat$TF[data.seurat$TF == "2810426N06Rik"] <- "Zfp788"
data.seurat$TF[data.seurat$TF == "Tcfap4"] <- "Tfap4"
# Removing TF cells with less than 8 cells / TF
removed_tfs <- names(table(data.seurat$TF))[table(data.seurat$TF) < 8]
message("Removing ",sum(data.seurat$TF %in% removed_tfs), " TF cells with less than 8 cells / TF: ", paste0("[", paste0(removed_tfs, collapse = ", "), "]"))
data.seurat <- data.seurat[,!data.seurat$TF %in% removed_tfs]
# Putting Seurat object in the merged list
data.seurat_all[["exp06"]] <- data.seurat
```
#### Exp07
```{r}
# D0 metadata
d0 <- c("mCherry-BC1", "mCherry-BC3")
d0_confluent <- c("mCherry-BC17", "mCherry-BC22")
# Reading file
data.seurat <- readRDS("results/C3H10_10X_exp07_enriched.cellranger_kneepoint_outliers_cellcycle.rds")
# Assigning D0 cells
data.seurat$TF[data.seurat$TF %in% d0] <- "D0"
data.seurat$TF[data.seurat$TF %in% d0_confluent] <- "D0_confluent"
# Assigning Adipo_ref cells
data.seurat$TF[colnames(data.seurat) %in% adipo_ref] <- "Adipo_ref"
# Filtering the Seurat object
data.seurat <- data.seurat[,!startsWith(data.seurat$TF, "mCherry-")]
# Renaming deprecated TF names
data.seurat$TF[data.seurat$TF == "myoD"] <- "Myod1"
data.seurat$TF[data.seurat$TF == "Gm239"] <- "Myrfl"
data.seurat$TF[data.seurat$TF == "Fkhl18"] <- "Foxs1"
data.seurat$TF[data.seurat$TF == "Bhlhb4"] <- "Bhlhe23"
data.seurat$TF[data.seurat$TF == "Tgif2lx"] <- "Tgif2lx1"
data.seurat$TF[data.seurat$TF == "EG194588"] <- "Obox7"
# Removing TF cells with less than 8 cells / TF
removed_tfs <- names(table(data.seurat$TF))[table(data.seurat$TF) < 8]
message("Removing ",sum(data.seurat$TF %in% removed_tfs), " TF cells with less than 8 cells / TF: ", paste0("[", paste0(removed_tfs, collapse = ", "), "]"))
data.seurat <- data.seurat[,!data.seurat$TF %in% removed_tfs]
# Putting Seurat object in the merged list
data.seurat_all[["exp07"]] <- data.seurat
```
#### Exp08
```{r}
# D0 metadata
d0 <- c("mCherry-BC1", "mCherry-BC3")
d0_confluent <- c("mCherry-BC17", "mCherry-BC22")
# Reading file
data.seurat <- readRDS("results/C3H10_10X_exp08_enriched.cellranger_kneepoint_outliers_cellcycle.rds")
# Assigning D0 cells
data.seurat$TF[data.seurat$TF %in% d0] <- "D0"
data.seurat$TF[data.seurat$TF %in% d0_confluent] <- "D0_confluent"
# Assigning Adipo_ref cells
data.seurat$TF[colnames(data.seurat) %in% adipo_ref] <- "Adipo_ref"
# Filtering the Seurat object
data.seurat <- data.seurat[,!startsWith(data.seurat$TF, "mCherry-")]
# Renaming deprecated TF names
data.seurat$TF[data.seurat$TF == "Bhlhb2"] <- "Bhlhe40"
data.seurat$TF[data.seurat$TF == "C330016O10Rik"] <- "N4bp3"
data.seurat$TF[data.seurat$TF == "1700029I01Rik"] <- "Zfp991"
data.seurat$TF[data.seurat$TF == "8430426H19Rik"] <- "Zfp935"
data.seurat$TF[data.seurat$TF == "Zfp191"] <- "Zfp24"
data.seurat$TF[data.seurat$TF == "6430604K15Rik"] <- "Zfp747"
data.seurat$TF[data.seurat$TF == "BC050078"] <- "Zfp867"
data.seurat$TF[data.seurat$TF == "1700065O13Rik"] <- "Zfp763"
data.seurat$TF[data.seurat$TF == "Duxbl"] <- "Duxbl1"
data.seurat$TF[data.seurat$TF == "myoD"] <- "Myod1"
data.seurat$TF[data.seurat$TF == "Zfp192"] <- "Zkscan8"
# Removing TF cells with less than 8 cells / TF
removed_tfs <- names(table(data.seurat$TF))[table(data.seurat$TF) < 8]
message("Removing ",sum(data.seurat$TF %in% removed_tfs), " TF cells with less than 8 cells / TF: ", paste0("[", paste0(removed_tfs, collapse = ", "), "]"))
data.seurat <- data.seurat[,!data.seurat$TF %in% removed_tfs]
# Putting Seurat object in the merged list
data.seurat_all[["exp08"]] <- data.seurat
```
#### Exp09
```{r}
# D0 metadata
d0 <- c("mCherry-BC21", "mCherry-BC22")
d0_confluent <- c("mCherry-P5A8", "mCherry-P5A10", "mCherry-P5A11", "mCherry-P5B12")
# Reading file
data.seurat <- readRDS("results/C3H10_10X_exp09_enriched.cellranger_kneepoint_outliers_cellcycle.rds")
# Assigning D0 cells
data.seurat$TF[data.seurat$TF %in% d0] <- "D0"
data.seurat$TF[data.seurat$TF %in% d0_confluent] <- "D0_confluent"
# Assigning Adipo_ref cells
data.seurat$TF[colnames(data.seurat) %in% adipo_ref] <- "Adipo_ref"
# Filtering the Seurat object
data.seurat <- data.seurat[,!startsWith(data.seurat$TF, "mCherry-")]
# Renaming deprecated TF names
data.seurat$TF[data.seurat$TF == "Pparg1_1"] <- "Pparg1"
data.seurat$TF[data.seurat$TF == "Pparg1_2"] <- "Pparg1"
data.seurat$TF[data.seurat$TF == "Tcfap2c"] <- "Tfap2c"
data.seurat$TF[data.seurat$TF == "Tcfec"] <- "Tfec"
data.seurat$TF[data.seurat$TF == "Tcfeb"] <- "Tfeb"
data.seurat$TF[data.seurat$TF == "Tref1"] <- "Trerf1"
data.seurat$TF[data.seurat$TF == "Hoxd10_1"] <- "Hoxd10"
data.seurat$TF[data.seurat$TF == "Hoxd10_2"] <- "Hoxd10"
data.seurat$TF[data.seurat$TF == "Tcfap2b"] <- "Tfap2b"
data.seurat$TF[data.seurat$TF == "MyoD"] <- "Myod1"
data.seurat$TF[data.seurat$TF == "Bhlhb2"] <- "Bhlhe40"
data.seurat$TF[data.seurat$TF == "Tada2l"] <- "Tada2a"
# Removing TF cells with less than 8 cells / TF
removed_tfs <- names(table(data.seurat$TF))[table(data.seurat$TF) < 8]
message("Removing ",sum(data.seurat$TF %in% removed_tfs), " TF cells with less than 8 cells / TF: ", paste0("[", paste0(removed_tfs, collapse = ", "), "]"))
data.seurat <- data.seurat[,!data.seurat$TF %in% removed_tfs]
# Putting Seurat object in the merged list
data.seurat_all[["exp09"]] <- data.seurat
```
#### Exp10
```{r}
# D0 metadata
d0 <- c("mCherry-BC1", "mCherry-BC3", "mCherry-BC6")
d0_confluent <- c("mCherry-P5A5", "mCherry-P5A8", "mCherry-P5A10")
# Reading file
data.seurat <- readRDS("results/C3H10_10X_exp10_enriched.cellranger_kneepoint_outliers_cellcycle.rds")
# Assigning D0 cells
data.seurat$TF[data.seurat$TF %in% d0] <- "D0"
data.seurat$TF[data.seurat$TF %in% d0_confluent] <- "D0_confluent"
# Assigning Adipo_ref cells
data.seurat$TF[colnames(data.seurat) %in% adipo_ref] <- "Adipo_ref"
# Filtering the Seurat object
data.seurat <- data.seurat[,!startsWith(data.seurat$TF, "mCherry-")]
# Renaming deprecated TF names
data.seurat$TF[data.seurat$TF == "Gm1008"] <- "Zfp663"
data.seurat$TF[data.seurat$TF == "Zfp187"] <- "Zscan26"
data.seurat$TF[data.seurat$TF == "Zfp422-rs1"] <- "Zfp955a"
data.seurat$TF[data.seurat$TF == "Zfp85-rs1"] <- "Zfp85"
data.seurat$TF[data.seurat$TF == "Zfp238"] <- "Zbtb18"
data.seurat$TF[data.seurat$TF == "AI894139"] <- "Zfp956"
data.seurat$TF[data.seurat$TF == "Bhlhb2"] <- "Bhlhe40"
# Removing TF cells with less than 8 cells / TF
removed_tfs <- names(table(data.seurat$TF))[table(data.seurat$TF) < 8]
message("Removing ",sum(data.seurat$TF %in% removed_tfs), " TF cells with less than 8 cells / TF: ", paste0("[", paste0(removed_tfs, collapse = ", "), "]"))
data.seurat <- data.seurat[,!data.seurat$TF %in% removed_tfs]
# Putting Seurat object in the merged list
data.seurat_all[["exp10"]] <- data.seurat
```
#### Exp11
```{r}
# D0 metadata
d0 <- c("mCherry-BC1", "mCherry-BC3", "mCherry-BC6")
d0_confluent <- c()
# Reading file
data.seurat <- readRDS("results/C3H10_10X_exp11_enriched.cellranger_kneepoint_outliers_cellcycle.rds")
# Assigning D0 cells
data.seurat$TF[data.seurat$TF %in% d0] <- "D0"
data.seurat$TF[data.seurat$TF %in% d0_confluent] <- "D0_confluent"
# Assigning Adipo_ref cells
data.seurat$TF[colnames(data.seurat) %in% adipo_ref] <- "Adipo_ref"
# Filtering the Seurat object
data.seurat <- data.seurat[,!startsWith(data.seurat$TF, "mCherry-")]
# Renaming deprecated TF names
data.seurat$TF[data.seurat$TF == "Hmgb2l1"] <- "Hmgxb4"
data.seurat$TF[data.seurat$TF == "Sox2_1"] <- "Sox2"
data.seurat$TF[data.seurat$TF == "Sox2_2"] <- "Sox2"
data.seurat$TF[data.seurat$TF == "Fam164a"] <- "Zc2hc1a"
data.seurat$TF[data.seurat$TF == "CEBPA"] <- "Cebpa"
data.seurat$TF[data.seurat$TF == "MESP1"] <- "Mesp1"
# Removing TF cells with less than 8 cells / TF
removed_tfs <- names(table(data.seurat$TF))[table(data.seurat$TF) < 8]
message("Removing ",sum(data.seurat$TF %in% removed_tfs), " TF cells with less than 8 cells / TF: ", paste0("[", paste0(removed_tfs, collapse = ", "), "]"))
data.seurat <- data.seurat[,!data.seurat$TF %in% removed_tfs]
# Putting Seurat object in the merged list
data.seurat_all[["exp11"]] <- data.seurat
```
#### Exp12-13
```{r}
# D0 metadata
d0 <- c("mCherry_BC17")
d0_confluent <- c("mCherry_BC15")
# We filter out mCherry_BC14, which had weird location
# Reading file
data.seurat <- readRDS("results/C3H10_10X_exp12-13_enriched.cellranger_kneepoint_outliers_cellcycle.rds")
# Assigning D0 cells
data.seurat$TF[data.seurat$TF %in% d0] <- "D0"
data.seurat$TF[data.seurat$TF %in% d0_confluent] <- "D0_confluent"
# Assigning Adipo_ref cells
data.seurat$TF[colnames(data.seurat) %in% adipo_ref] <- "Adipo_ref" # There are no exp12-13 cells that we keep as Adipo_ref
# Filtering the Seurat object
data.seurat <- data.seurat[,!startsWith(data.seurat$TF, "mCherry_")]
# Renaming deprecated TF names
data.seurat$TF[data.seurat$TF == "Cebpa_1"] <- "Cebpa"
data.seurat$TF[data.seurat$TF == "Cebpa_2"] <- "Cebpa"
data.seurat$TF[data.seurat$TF == "Cebpa_4"] <- "Cebpa"
data.seurat$TF[data.seurat$TF == "Mycn_1"] <- "Mycn"
data.seurat$TF[data.seurat$TF == "Mycn_2"] <- "Mycn"
data.seurat$TF[data.seurat$TF == "Mycn_3"] <- "Mycn"
data.seurat$TF[data.seurat$TF == "Mycn_4"] <- "Mycn"
data.seurat$TF[data.seurat$TF == "Myog_BC1"] <- "Myo_ref"
data.seurat$TF[data.seurat$TF == "Myog_BC2"] <- "Myo_ref"
data.seurat$TF[data.seurat$TF == "Myog_BC3"] <- "Myo_ref"
data.seurat$TF[data.seurat$TF == "Myog_BC4"] <- "Myo_ref"
data.seurat$TF[data.seurat$TF == "Myog_BC5"] <- "Myo_ref"
data.seurat$TF[data.seurat$TF == "Myog_BC7"] <- "Myog"
data.seurat$TF[data.seurat$TF == "Myog_BC8"] <- "Myog"
data.seurat$TF[data.seurat$TF == "Myog_BC9"] <- "Myog"
data.seurat$TF[data.seurat$TF == "Pparg_A6"] <- "Pparg"
data.seurat$TF[data.seurat$TF == "Pparg_G6"] <- "Pparg"
data.seurat$TF[data.seurat$TF == "Runx2_5"] <- "Runx2"
# Putting Seurat object in the merged list
data.seurat_all[["exp12-13"]] <- data.seurat
```
```{r}
saveRDS(data.seurat_all, "results/C3H10_10X_all_exps_merged.rds")
```