dev/test_simulation_singleCell_makeflow_pipeline/parse_benign.R
In stemangiola/ARMET: Higher-Order Deconvolution Survival Analyses
library(purrr)
library(readr)
library(tibble)
library(dplyr)
library(tidyr)
library(tidyseurat)
library(Seurat)
library(tidysc)
library(ggplot2)
options(future.globals.maxSize = 50068 * 1024^2)
my_dir = "~/PostDoc/oligo_breast/expanded_analyses_with_control"
sample_info = read_csv(sprintf("%s/OMBC - Sheet1.csv", my_dir))
my_theme =
theme_bw() +
theme(
panel.border = element_blank(),
axis.line = element_line(),
panel.grid.major = element_line(size = 0.2),
panel.grid.minor = element_line(size = 0.1),
text = element_text(size=8),
legend.position="bottom",
strip.background = element_blank()
)
# Load Saskia dataset = 1 SAMPLE
load(sprintf("%s/../data/benign_PBMC/saskia_GSE115189/Sce_Dataset1.RData", my_dir))
# add gene symbol
sce_count = sce@assays$data@listData$counts
rownames(sce_count) = tibble(ens = rownames(sce_count)) %>% tidybulk::ensembl_to_symbol(ens) %>% pull(transcript)
sce_count = sce_count[!is.na(rownames(sce_count)), ]
sce_count = sce_count[!duplicated(rownames(sce_count)), ]
saskia_PBMC =
sce_count %>%
CreateSeuratObject() %>% list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::mutate(sample = "GSE115189") %>%
tidyseurat::mutate(source_data = "GSE115189")
# Broad SCP345 - 2 SAMPLES
broad_SCP345 =
read.delim(sprintf("%s/../data/benign_PBMC/broad_SCP345/pbmc_site_bc.scp.expr.txt", my_dir), row.names = 1) %>%
as.matrix %>%
Seurat::CreateSeuratObject() %>%
list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
#tidyseurat::select(-sample) %>%
tidyseurat::left_join(
read_delim(sprintf("%s/../data/benign_PBMC/broad_SCP345/pbmc_cca_final_metadata_lineage1lineage2.txt", my_dir), "\t", escape_double = FALSE, trim_ws = TRUE, skip = 1) %>%
setNames(c("cell", "Lineages", "kmeans", "Site" , "sample")) %>%
mutate(cell = gsub("-", ".", cell))
) %>%
tidyseurat::filter(sample %>% is.na %>% `!`) %>%
tidyseurat::mutate(sample = sprintf("SCP345_%s", sample)) %>%
tidyseurat::mutate(source_data = "SCP345") %>%
tidyseurat::rename(old_cell_type = Lineages)
# Broad SCP424 - 2 SAMPLES
counts = Matrix::readMM("/stornext/Bioinf/data/bioinf-data/Papenfuss_lab/projects/mangiola.s/PostDoc/oligo_breast/data/benign_PBMC/broad_SCP424/matrix.mtx.gz") %>% as.matrix
rownames(counts) = read.csv("/stornext/Bioinf/data/bioinf-data/Papenfuss_lab/projects/mangiola.s/PostDoc/oligo_breast/data/benign_PBMC/broad_SCP424/features.tsv.gz", sep="", header = F) %>% as_tibble() %>% tidyr::extract(V1, into = "symbol", ".+_(.+)") %>% pull(symbol)
colnames(counts) = read.csv("/stornext/Bioinf/data/bioinf-data/Papenfuss_lab/projects/mangiola.s/PostDoc/oligo_breast/data/benign_PBMC/broad_SCP424/barcodes.tsv.gz", sep="", header = F)[,1]
counts = counts[!duplicated(rownames(counts)),]
broad_SCP424 =
counts %>%
Seurat::CreateSeuratObject() %>%
list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::left_join(
read_delim(sprintf("%s/../data/benign_PBMC/broad_SCP424/meta_data_broad_PBMC.txt", my_dir), "\t", escape_double = FALSE, trim_ws = TRUE, skip = 1) %>%
setNames(c("cell" , "nGene" , "nUMI" , "percent.mito" , "Cluster", "CellType" , "Experiment", "Method" )) %>%
filter(Method %in% c("10x Chromium (v3)", "10x Chromium (v2)"))
) %>%
tidyseurat::filter(Method %>% is.na %>% `!`) %>%
tidyseurat::mutate(sample = sprintf("SCP424_%s", orig.ident)) %>%
tidyseurat::mutate(source_data = "SCP424") %>%
tidyseurat::rename(old_cell_type = CellType)
# Broad SCP589 - ALL depressed not healthy
broad_SCP589 =
read.delim(sprintf("%s/../data/benign_PBMC/broad_SCP589/Human_PBMC_IFNb_Processed_Log_UMI.txt", my_dir), row.names = 1) %>%
as.matrix() %>%
Seurat::CreateSeuratObject() %>%
list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::left_join(
read_delim(sprintf("%s/../data/benign_PBMC/broad_SCP589/Human_PBMC_IFNb_Metadata.txt", my_dir), "\t", escape_double = FALSE, trim_ws = TRUE, skip = 1) %>%
setNames(c("cell" , "sample_condition", "Cell_Types" ,"Condition" ,"Sex" ,"Age" ,"HCV" ,"CanDep" ,"StimDep" ,"SedDep" ,"CocDep", "AlcDep", "NicDep", "Major_Depressive", "PTSD" )) %>%
filter(Condition=="Control")
) %>%
tidyseurat::rename(sample = sample_condition)
# Broad SCP591 - 1 SAMPLE
broad_SCP591 =
read.delim(sprintf("%s/../data/benign_PBMC/broad_SCP591/Human_PBMC_Morphine_Processed_Log_UMI.txt", my_dir), row.names = 1) %>%
as.matrix() %>%
Seurat::CreateSeuratObject() %>%
list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::left_join(
read_delim(sprintf("%s/../data/benign_PBMC/broad_SCP591/Human_PBMC_Morphine_Metadata.txt", my_dir), "\t", escape_double = FALSE, trim_ws = TRUE, skip = 1) %>%
setNames(c("cell" , "Cell_type", "group" )) %>%
filter(group=="Untreated")
) %>%
tidyseurat::filter(group %>% is.na %>% `!`) %>%
tidyseurat::mutate(sample = "SCP591") %>%
tidyseurat::mutate(source_data = "SCP591") %>%
tidyseurat::rename(old_cell_type = Cell_type)
# Cell ranger
cellranger_PBMC =
c(
sprintf("%s/../data/benign_PBMC/10x_6K_PBMC_CallRanger_1_1_0/filtered_gene_bc_matrices/filtered_matrices_mex/hg19/", my_dir),
sprintf("%s/../data/benign_PBMC/10x_8K_PBMC_CallRanger_2_1_0/filtered_gene_bc_matrices/GRCh38/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR6260181/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR6260182/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR6260183/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR11038995/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR11038989/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR7722939/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR7244582/outs/filtered_feature_bc_matrix/", my_dir)
) %>%
tidysc::tidysc_cell_ranger( species = "Human" ) %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::extract(orig.ident, "sample", "../data/benign_PBMC/([a-zA-Z0-9_]+)/.+") %>%
tidyseurat::mutate(sample = dplyr::case_when(
sample == "10x_6K_PBMC_CallRanger_1_1_0" ~ "10x_6K",
sample == "10x_8K_PBMC_CallRanger_2_1_0" ~ "10x_8K",
TRUE ~ sample
))
# Merge
saskia_PBMC %>%
tidyseurat::bind_rows(cellranger_PBMC) %>%
tidyseurat::bind_rows(broad_SCP345) %>%
tidyseurat::bind_rows(broad_SCP424) %>%
tidyseurat::bind_rows(broad_SCP591) %>%
tidyseurat::bind_rows(broad_SCP589) %>%
tidyseurat::filter(sample %>% is.na %>% `!`) %>%
# Process
tidysc::adjust_abundance(
~ integrate(sample),
reference_samples = "SCP345_580"
) %>%
tidysc::reduce_dimensions(method="PCA") %>%
tidysc::cluster_elements() %>%
tidyseurat::mutate(cluster = seurat_clusters) %>%
tidysc::deconvolve_cellularity(species="Human") %>%
tidysc::reduce_dimensions("UMAP") %>%
saveRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_integrated.rds", compress = "gzip")
# # Process
# tidyseurat::nest(data = -sample) %>%
# tidyseurat::mutate(data = map(
# data,
# ~ .x %>%
# tidysc::scale_abundance() %>%
# tidysc::reduce_dimensions(method="PCA") %>%
# tidysc::cluster_elements() %>%
# tidyseurat::mutate(cluster = seurat_clusters) %>%
# tidysc::deconvolve_cellularity(species="Human") %>%
# tidysc::reduce_dimensions("UMAP") %>%
# tidysc::reduce_dimensions("tSNE")
# )) %>%
# saveRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_nested.rds", compress = "gzip")
# # PCA
# (
# PBMC %>%
#
# # Annotate
# tidyseurat::left_join(
# sample_info %>% distinct(sample, race),
# by = "sample"
# ) %>%
#
# aggregate_cells() %>%
# tidybulk::identify_abundant(sample, transcript, abundance_RNA) %>%
# tidybulk::scale_abundance(sample, transcript, abundance_RNA) %>%
# tidybulk::reduce_dimensions(sample, transcript, abundance_RNA_scaled, method = "PCA", action="get") %>%
# ggplot(aes(PC1, PC2, color=race, label=sample)) +
# geom_text() +
# scale_color_brewer(palette="Set1") +
# my_theme +
# theme(aspect.ratio=1)
# ) %>%
# plotly::ggplotly()
readRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_integrated.rds") %>%
# tidyseurat::unnest(data) %>%
# Harmonize the cell types
tidyseurat::mutate(
cell_type_curated = case_when(
old_cell_type == "1. T" & label_blueprint == "B-cells" ~ "b_cell",
old_cell_type == "1. T" & label_blueprint == "CD4+ T-cells" ~ "t_CD4",
old_cell_type == "1. T" & label_blueprint == "CD8+ T-cells" ~ "t_CD8",
old_cell_type == "1. T" & label_blueprint == "Monocytes" ~ "",
old_cell_type == "1. T" & label_blueprint == "NK cells" ~ "natural_killer",
old_cell_type == "2. CD14+ Monocyte" & label_blueprint == "Monocytes" ~ "monocyte",
old_cell_type == "3. NK" & label_blueprint == "B-cells" ~ "",
old_cell_type == "3. NK" & label_blueprint == "CD4+ T-cells" ~ "",
old_cell_type == "3. NK" & label_blueprint == "CD8+ T-cells" ~ "",
old_cell_type == "3. NK" & label_blueprint == "Monocytes" ~ "",
old_cell_type == "3. NK" & label_blueprint == "NK cells" ~ "natural_killer",
old_cell_type == "4. Memory B cell" & label_blueprint == "B-cells" ~ "b_cell",
old_cell_type == "4. Memory B cell" & label_blueprint == "CD4+ T-cells" ~ "",
old_cell_type == "4. Memory B cell" & label_blueprint == "Monocytes" ~ "",
old_cell_type == "4. Memory B cell" & label_blueprint == "NK cells" ~ "",
old_cell_type == "5. DC" & label_blueprint == "Monocytes" ~ "dendritic_myeloid",
old_cell_type == "6. CD16+ Monocyte" & label_blueprint == "Monocytes" ~ "monocyte",
old_cell_type == "7. Naive B cell" & label_blueprint == "B-cells" ~ "b_cell",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "B-cells" ~ "",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "CD4+ T-cells" ~ "",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "CD8+ T-cells" ~ "",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "Monocytes" ~ "",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "NK cells" ~ "" ,
old_cell_type == "B cell" & label_blueprint == "B-cells" ~ "b_cell" ,
old_cell_type == "B cell" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "B cell" & label_blueprint == "Monocytes" ~ "" ,
old_cell_type == "B Cells" & label_blueprint == "B-cells" ~ "" ,
old_cell_type == "CD14+ monocyte" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "CD14+ monocyte" & label_blueprint == "Monocytes" ~ "monocyte" ,
old_cell_type == "CD16+ monocyte" & label_blueprint == "Monocytes" ~ "monocyte" ,
old_cell_type == "Monocytes" & label_blueprint == "Monocytes" ~ "monocyte",
old_cell_type == "CD4 T Cells" & label_blueprint == "B-cells" ~ "" ,
old_cell_type == "CD4 T Cells" & label_blueprint == "CD4+ T-cells" ~ "t_CD4" ,
old_cell_type == "CD4 T Cells" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "B-cells" ~ "" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "CD4+ T-cells" ~ "t_CD4" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "Monocytes" ~ "" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "NK cells" ~ "" ,
old_cell_type == "CD8 T Cells" & label_blueprint == "CD4+ T-cells" ~ "" ,
old_cell_type == "CD8 T Cells" & label_blueprint == "CD8+ T-cells" ~ "t_CD8" ,
old_cell_type == "Cytotoxic T cell" & label_blueprint == "CD4+ T-cells" ~ "" ,
old_cell_type == "Cytotoxic T cell" & label_blueprint == "CD8+ T-cells" ~ "t_CD8" ,
old_cell_type == "Cytotoxic T cell" & label_blueprint == "NK cells" ~ "" ,
old_cell_type == "Dendritic cell" & label_blueprint == "CD4+ T-cells" ~ "" ,
old_cell_type == "Dendritic cell" & label_blueprint == "Monocytes" ~ "dendritic_myeloid" ,
old_cell_type == "Megakaryocyte" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "Monocytes" & label_blueprint == "Macrophages" ~ "" ,
old_cell_type == "Natural killer cell" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "Natural killer cell" & label_blueprint == "NK cells" ~ "natural_killer" ,
old_cell_type == "NK Cells" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "Plasmacytoid dendritic cell" & label_blueprint == "B-cells" ~ "" ,
old_cell_type == "Plasmacytoid dendritic cell" & label_blueprint == "Monocytes" ~ "" ,
old_cell_type %>% is.na & label_blueprint == "Adipocytes" ~ "adipocyte" ,
old_cell_type %>% is.na & label_blueprint == "B-cells" ~ "b_cell" ,
old_cell_type %>% is.na & label_blueprint == "CD4+ T-cells" ~ "t_CD4" ,
old_cell_type %>% is.na & label_blueprint == "CD8+ T-cells" ~ "t_CD8" ,
old_cell_type %>% is.na & label_blueprint == "Monocytes" ~ "monocyte" ,
old_cell_type %>% is.na & label_blueprint == "NK cells" ~ "natural_killer" ,
old_cell_type == "NK Cells" & label_blueprint == "NK cells" ~ "natural_killer" ,
TRUE ~ "THIS SHOULD NOT EXIST"
)
) %>%
filter(cell_type_curated != "") %>%
filter(cell_type_curated != "THIS SHOULD NOT EXIST") %>%
saveRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_integrated_curated.rds", compress = "gzip")
#
# readRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_integrated_curated.rds") %>%
#
# tidyseurat::nest(data = -cell_type_curated) %>%
# filter(cell_type_curated != "") %>%
# tidyseurat::mutate(data = map(data, ~ .x %>% tidysc::aggregate_cells(sample))) %>%
# unnest(data) %>%
#
# # Prepare the scaled bulk
# tidyr::unite("sample_ct", c(sample, cell_type_curated), remove = F) %>%
# select(sample_ct, cell_type_curated, transcript, abundance_RNA, sample) %>%
# tidybulk::identify_abundant(sample_ct, transcript, abundance_RNA) %>%
# tidybulk::scale_abundance(sample_ct, transcript, abundance_RNA) %>%
#
# # Save
# saveRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_sudo_bulk.rds", compress = "gzip")
#
#
#
# readRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_sudo_bulk.rds") %>%
# tidybulk::reduce_dimensions(sample_ct, transcript, abundance_RNA_scaled, method="PCA", action="get", scale=F) %>%
# ggplot(aes(PC1, PC2, color=cell_type_curated)) +
# geom_point()
stemangiola/ARMET documentation built on July 9, 2022, 1:25 a.m.
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library(purrr)
library(readr)
library(tibble)
library(dplyr)
library(tidyr)
library(tidyseurat)
library(Seurat)
library(tidysc)
library(ggplot2)
options(future.globals.maxSize = 50068 * 1024^2)
my_dir = "~/PostDoc/oligo_breast/expanded_analyses_with_control"
sample_info = read_csv(sprintf("%s/OMBC - Sheet1.csv", my_dir))
my_theme =
theme_bw() +
theme(
panel.border = element_blank(),
axis.line = element_line(),
panel.grid.major = element_line(size = 0.2),
panel.grid.minor = element_line(size = 0.1),
text = element_text(size=8),
legend.position="bottom",
strip.background = element_blank()
)
# Load Saskia dataset = 1 SAMPLE
load(sprintf("%s/../data/benign_PBMC/saskia_GSE115189/Sce_Dataset1.RData", my_dir))
# add gene symbol
sce_count = sce@assays$data@listData$counts
rownames(sce_count) = tibble(ens = rownames(sce_count)) %>% tidybulk::ensembl_to_symbol(ens) %>% pull(transcript)
sce_count = sce_count[!is.na(rownames(sce_count)), ]
sce_count = sce_count[!duplicated(rownames(sce_count)), ]
saskia_PBMC =
sce_count %>%
CreateSeuratObject() %>% list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::mutate(sample = "GSE115189") %>%
tidyseurat::mutate(source_data = "GSE115189")
# Broad SCP345 - 2 SAMPLES
broad_SCP345 =
read.delim(sprintf("%s/../data/benign_PBMC/broad_SCP345/pbmc_site_bc.scp.expr.txt", my_dir), row.names = 1) %>%
as.matrix %>%
Seurat::CreateSeuratObject() %>%
list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
#tidyseurat::select(-sample) %>%
tidyseurat::left_join(
read_delim(sprintf("%s/../data/benign_PBMC/broad_SCP345/pbmc_cca_final_metadata_lineage1lineage2.txt", my_dir), "\t", escape_double = FALSE, trim_ws = TRUE, skip = 1) %>%
setNames(c("cell", "Lineages", "kmeans", "Site" , "sample")) %>%
mutate(cell = gsub("-", ".", cell))
) %>%
tidyseurat::filter(sample %>% is.na %>% `!`) %>%
tidyseurat::mutate(sample = sprintf("SCP345_%s", sample)) %>%
tidyseurat::mutate(source_data = "SCP345") %>%
tidyseurat::rename(old_cell_type = Lineages)
# Broad SCP424 - 2 SAMPLES
counts = Matrix::readMM("/stornext/Bioinf/data/bioinf-data/Papenfuss_lab/projects/mangiola.s/PostDoc/oligo_breast/data/benign_PBMC/broad_SCP424/matrix.mtx.gz") %>% as.matrix
rownames(counts) = read.csv("/stornext/Bioinf/data/bioinf-data/Papenfuss_lab/projects/mangiola.s/PostDoc/oligo_breast/data/benign_PBMC/broad_SCP424/features.tsv.gz", sep="", header = F) %>% as_tibble() %>% tidyr::extract(V1, into = "symbol", ".+_(.+)") %>% pull(symbol)
colnames(counts) = read.csv("/stornext/Bioinf/data/bioinf-data/Papenfuss_lab/projects/mangiola.s/PostDoc/oligo_breast/data/benign_PBMC/broad_SCP424/barcodes.tsv.gz", sep="", header = F)[,1]
counts = counts[!duplicated(rownames(counts)),]
broad_SCP424 =
counts %>%
Seurat::CreateSeuratObject() %>%
list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::left_join(
read_delim(sprintf("%s/../data/benign_PBMC/broad_SCP424/meta_data_broad_PBMC.txt", my_dir), "\t", escape_double = FALSE, trim_ws = TRUE, skip = 1) %>%
setNames(c("cell" , "nGene" , "nUMI" , "percent.mito" , "Cluster", "CellType" , "Experiment", "Method" )) %>%
filter(Method %in% c("10x Chromium (v3)", "10x Chromium (v2)"))
) %>%
tidyseurat::filter(Method %>% is.na %>% `!`) %>%
tidyseurat::mutate(sample = sprintf("SCP424_%s", orig.ident)) %>%
tidyseurat::mutate(source_data = "SCP424") %>%
tidyseurat::rename(old_cell_type = CellType)
# Broad SCP589 - ALL depressed not healthy
broad_SCP589 =
read.delim(sprintf("%s/../data/benign_PBMC/broad_SCP589/Human_PBMC_IFNb_Processed_Log_UMI.txt", my_dir), row.names = 1) %>%
as.matrix() %>%
Seurat::CreateSeuratObject() %>%
list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::left_join(
read_delim(sprintf("%s/../data/benign_PBMC/broad_SCP589/Human_PBMC_IFNb_Metadata.txt", my_dir), "\t", escape_double = FALSE, trim_ws = TRUE, skip = 1) %>%
setNames(c("cell" , "sample_condition", "Cell_Types" ,"Condition" ,"Sex" ,"Age" ,"HCV" ,"CanDep" ,"StimDep" ,"SedDep" ,"CocDep", "AlcDep", "NicDep", "Major_Depressive", "PTSD" )) %>%
filter(Condition=="Control")
) %>%
tidyseurat::rename(sample = sample_condition)
# Broad SCP591 - 1 SAMPLE
broad_SCP591 =
read.delim(sprintf("%s/../data/benign_PBMC/broad_SCP591/Human_PBMC_Morphine_Processed_Log_UMI.txt", my_dir), row.names = 1) %>%
as.matrix() %>%
Seurat::CreateSeuratObject() %>%
list() %>%
tidysc:::create_tt_from_seurat(species = "Human") %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::left_join(
read_delim(sprintf("%s/../data/benign_PBMC/broad_SCP591/Human_PBMC_Morphine_Metadata.txt", my_dir), "\t", escape_double = FALSE, trim_ws = TRUE, skip = 1) %>%
setNames(c("cell" , "Cell_type", "group" )) %>%
filter(group=="Untreated")
) %>%
tidyseurat::filter(group %>% is.na %>% `!`) %>%
tidyseurat::mutate(sample = "SCP591") %>%
tidyseurat::mutate(source_data = "SCP591") %>%
tidyseurat::rename(old_cell_type = Cell_type)
# Cell ranger
cellranger_PBMC =
c(
sprintf("%s/../data/benign_PBMC/10x_6K_PBMC_CallRanger_1_1_0/filtered_gene_bc_matrices/filtered_matrices_mex/hg19/", my_dir),
sprintf("%s/../data/benign_PBMC/10x_8K_PBMC_CallRanger_2_1_0/filtered_gene_bc_matrices/GRCh38/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR6260181/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR6260182/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR6260183/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR11038995/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR11038989/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR7722939/outs/filtered_feature_bc_matrix/", my_dir),
sprintf("%s/../data/benign_PBMC/SRR7244582/outs/filtered_feature_bc_matrix/", my_dir)
) %>%
tidysc::tidysc_cell_ranger( species = "Human" ) %>%
attr("seurat") %>%
.[[1]] %>%
tidyseurat::tidy() %>%
tidyseurat::extract(orig.ident, "sample", "../data/benign_PBMC/([a-zA-Z0-9_]+)/.+") %>%
tidyseurat::mutate(sample = dplyr::case_when(
sample == "10x_6K_PBMC_CallRanger_1_1_0" ~ "10x_6K",
sample == "10x_8K_PBMC_CallRanger_2_1_0" ~ "10x_8K",
TRUE ~ sample
))
# Merge
saskia_PBMC %>%
tidyseurat::bind_rows(cellranger_PBMC) %>%
tidyseurat::bind_rows(broad_SCP345) %>%
tidyseurat::bind_rows(broad_SCP424) %>%
tidyseurat::bind_rows(broad_SCP591) %>%
tidyseurat::bind_rows(broad_SCP589) %>%
tidyseurat::filter(sample %>% is.na %>% `!`) %>%
# Process
tidysc::adjust_abundance(
~ integrate(sample),
reference_samples = "SCP345_580"
) %>%
tidysc::reduce_dimensions(method="PCA") %>%
tidysc::cluster_elements() %>%
tidyseurat::mutate(cluster = seurat_clusters) %>%
tidysc::deconvolve_cellularity(species="Human") %>%
tidysc::reduce_dimensions("UMAP") %>%
saveRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_integrated.rds", compress = "gzip")
# # Process
# tidyseurat::nest(data = -sample) %>%
# tidyseurat::mutate(data = map(
# data,
# ~ .x %>%
# tidysc::scale_abundance() %>%
# tidysc::reduce_dimensions(method="PCA") %>%
# tidysc::cluster_elements() %>%
# tidyseurat::mutate(cluster = seurat_clusters) %>%
# tidysc::deconvolve_cellularity(species="Human") %>%
# tidysc::reduce_dimensions("UMAP") %>%
# tidysc::reduce_dimensions("tSNE")
# )) %>%
# saveRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_nested.rds", compress = "gzip")
# # PCA
# (
# PBMC %>%
#
# # Annotate
# tidyseurat::left_join(
# sample_info %>% distinct(sample, race),
# by = "sample"
# ) %>%
#
# aggregate_cells() %>%
# tidybulk::identify_abundant(sample, transcript, abundance_RNA) %>%
# tidybulk::scale_abundance(sample, transcript, abundance_RNA) %>%
# tidybulk::reduce_dimensions(sample, transcript, abundance_RNA_scaled, method = "PCA", action="get") %>%
# ggplot(aes(PC1, PC2, color=race, label=sample)) +
# geom_text() +
# scale_color_brewer(palette="Set1") +
# my_theme +
# theme(aspect.ratio=1)
# ) %>%
# plotly::ggplotly()
readRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_integrated.rds") %>%
# tidyseurat::unnest(data) %>%
# Harmonize the cell types
tidyseurat::mutate(
cell_type_curated = case_when(
old_cell_type == "1. T" & label_blueprint == "B-cells" ~ "b_cell",
old_cell_type == "1. T" & label_blueprint == "CD4+ T-cells" ~ "t_CD4",
old_cell_type == "1. T" & label_blueprint == "CD8+ T-cells" ~ "t_CD8",
old_cell_type == "1. T" & label_blueprint == "Monocytes" ~ "",
old_cell_type == "1. T" & label_blueprint == "NK cells" ~ "natural_killer",
old_cell_type == "2. CD14+ Monocyte" & label_blueprint == "Monocytes" ~ "monocyte",
old_cell_type == "3. NK" & label_blueprint == "B-cells" ~ "",
old_cell_type == "3. NK" & label_blueprint == "CD4+ T-cells" ~ "",
old_cell_type == "3. NK" & label_blueprint == "CD8+ T-cells" ~ "",
old_cell_type == "3. NK" & label_blueprint == "Monocytes" ~ "",
old_cell_type == "3. NK" & label_blueprint == "NK cells" ~ "natural_killer",
old_cell_type == "4. Memory B cell" & label_blueprint == "B-cells" ~ "b_cell",
old_cell_type == "4. Memory B cell" & label_blueprint == "CD4+ T-cells" ~ "",
old_cell_type == "4. Memory B cell" & label_blueprint == "Monocytes" ~ "",
old_cell_type == "4. Memory B cell" & label_blueprint == "NK cells" ~ "",
old_cell_type == "5. DC" & label_blueprint == "Monocytes" ~ "dendritic_myeloid",
old_cell_type == "6. CD16+ Monocyte" & label_blueprint == "Monocytes" ~ "monocyte",
old_cell_type == "7. Naive B cell" & label_blueprint == "B-cells" ~ "b_cell",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "B-cells" ~ "",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "CD4+ T-cells" ~ "",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "CD8+ T-cells" ~ "",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "Monocytes" ~ "",
old_cell_type == "8. Ambiguous / Potential Doublets" & label_blueprint == "NK cells" ~ "" ,
old_cell_type == "B cell" & label_blueprint == "B-cells" ~ "b_cell" ,
old_cell_type == "B cell" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "B cell" & label_blueprint == "Monocytes" ~ "" ,
old_cell_type == "B Cells" & label_blueprint == "B-cells" ~ "" ,
old_cell_type == "CD14+ monocyte" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "CD14+ monocyte" & label_blueprint == "Monocytes" ~ "monocyte" ,
old_cell_type == "CD16+ monocyte" & label_blueprint == "Monocytes" ~ "monocyte" ,
old_cell_type == "Monocytes" & label_blueprint == "Monocytes" ~ "monocyte",
old_cell_type == "CD4 T Cells" & label_blueprint == "B-cells" ~ "" ,
old_cell_type == "CD4 T Cells" & label_blueprint == "CD4+ T-cells" ~ "t_CD4" ,
old_cell_type == "CD4 T Cells" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "B-cells" ~ "" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "CD4+ T-cells" ~ "t_CD4" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "Monocytes" ~ "" ,
old_cell_type == "CD4+ T cell" & label_blueprint == "NK cells" ~ "" ,
old_cell_type == "CD8 T Cells" & label_blueprint == "CD4+ T-cells" ~ "" ,
old_cell_type == "CD8 T Cells" & label_blueprint == "CD8+ T-cells" ~ "t_CD8" ,
old_cell_type == "Cytotoxic T cell" & label_blueprint == "CD4+ T-cells" ~ "" ,
old_cell_type == "Cytotoxic T cell" & label_blueprint == "CD8+ T-cells" ~ "t_CD8" ,
old_cell_type == "Cytotoxic T cell" & label_blueprint == "NK cells" ~ "" ,
old_cell_type == "Dendritic cell" & label_blueprint == "CD4+ T-cells" ~ "" ,
old_cell_type == "Dendritic cell" & label_blueprint == "Monocytes" ~ "dendritic_myeloid" ,
old_cell_type == "Megakaryocyte" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "Monocytes" & label_blueprint == "Macrophages" ~ "" ,
old_cell_type == "Natural killer cell" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "Natural killer cell" & label_blueprint == "NK cells" ~ "natural_killer" ,
old_cell_type == "NK Cells" & label_blueprint == "CD8+ T-cells" ~ "" ,
old_cell_type == "Plasmacytoid dendritic cell" & label_blueprint == "B-cells" ~ "" ,
old_cell_type == "Plasmacytoid dendritic cell" & label_blueprint == "Monocytes" ~ "" ,
old_cell_type %>% is.na & label_blueprint == "Adipocytes" ~ "adipocyte" ,
old_cell_type %>% is.na & label_blueprint == "B-cells" ~ "b_cell" ,
old_cell_type %>% is.na & label_blueprint == "CD4+ T-cells" ~ "t_CD4" ,
old_cell_type %>% is.na & label_blueprint == "CD8+ T-cells" ~ "t_CD8" ,
old_cell_type %>% is.na & label_blueprint == "Monocytes" ~ "monocyte" ,
old_cell_type %>% is.na & label_blueprint == "NK cells" ~ "natural_killer" ,
old_cell_type == "NK Cells" & label_blueprint == "NK cells" ~ "natural_killer" ,
TRUE ~ "THIS SHOULD NOT EXIST"
)
) %>%
filter(cell_type_curated != "") %>%
filter(cell_type_curated != "THIS SHOULD NOT EXIST") %>%
saveRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_integrated_curated.rds", compress = "gzip")
#
# readRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_integrated_curated.rds") %>%
#
# tidyseurat::nest(data = -cell_type_curated) %>%
# filter(cell_type_curated != "") %>%
# tidyseurat::mutate(data = map(data, ~ .x %>% tidysc::aggregate_cells(sample))) %>%
# unnest(data) %>%
#
# # Prepare the scaled bulk
# tidyr::unite("sample_ct", c(sample, cell_type_curated), remove = F) %>%
# select(sample_ct, cell_type_curated, transcript, abundance_RNA, sample) %>%
# tidybulk::identify_abundant(sample_ct, transcript, abundance_RNA) %>%
# tidybulk::scale_abundance(sample_ct, transcript, abundance_RNA) %>%
#
# # Save
# saveRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_sudo_bulk.rds", compress = "gzip")
#
#
#
# readRDS("dev/test_simulation_singleCell_makeflow_pipeline/PBMC_sudo_bulk.rds") %>%
# tidybulk::reduce_dimensions(sample_ct, transcript, abundance_RNA_scaled, method="PCA", action="get", scale=F) %>%
# ggplot(aes(PC1, PC2, color=cell_type_curated)) +
# geom_point()
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