diff --git a/EAID_000083/.gitignore b/EAID_000083/.gitignore new file mode 100644 index 0000000..7096ccf --- /dev/null +++ b/EAID_000083/.gitignore @@ -0,0 +1,5 @@ +data +plots +figures +out +misc_out \ No newline at end of file diff --git a/EAID_000083/README.md b/EAID_000083/README.md new file mode 100644 index 0000000..c58a7c6 --- /dev/null +++ b/EAID_000083/README.md @@ -0,0 +1,35 @@ +# ENCODE Manual Analyses: Pancreas RNA & Multiome Datasets + +Analyses for integrating ENCODE scRNA and 10x Multiome datasets + +Contact information: +Ben Parks +bparks@stanford.edu + +## Running the analyses + +All analysis steps from data download to processed outputs are summarized in the `run_all.sh` script. + +Note that this script may take very long to run end-to-end, and assume high memory and CPU count availability. +It is recommended to run the steps one at a time in a supervized manner. + +Standardized outputs are saved in the `out` folder. +Figures are saved in the `figures` folder. +Intermediate data objects are stored in the `data` folder. + +## Analysis Steps +- Cells are filtered based on fragment count, TSS enrichment, and/or UMI count (parameters in config/sample_cutoffs.tsv) +- Run sctransform followed by Seurat anchor integration for the RNA (code in `analysis/01b_integrate_samples.R`) +- Use a combination of Seurat alignment with outside datasets (Tabula Sapiens, Azimuth) and + marker gene analysis (via Seurat AddModuleScore) to attach cell types to the unbiased clusters + +## Figure descriptions +- `Azimuth_clusters.pdf` Heatmap of jaccard overlap for cell types annotated by Azimuth alinment vs. + unbiased cluster IDs from initial integration +- `Azimuth_modules.pdf` Dotplot of module scores for Azimuth marker genes vs. unbiased cluter IDs from + initial integration +- `Hubmap_markers.pdf` Dotplot of gene expression for selected markers from the Hubmap ASCT+B tables vs. + unbiased cluster IDs from initial integration +- `TS_cluters.pdf` Same as `Azimuth_clusters.pdf`, but with alignment to Tabula Sapiens rather than Azimuth +- `umap_annotated_clusts.png` UMAP plot of the integrated datasets (based on RNA), labeled by annotated cell type +- `umap_automated_clusts.png` UMAP plot of the integrated datasets (based on RNA), labeled by automated cluter ID \ No newline at end of file diff --git a/EAID_000083/analysis/00a_export_qc_data.R b/EAID_000083/analysis/00a_export_qc_data.R new file mode 100644 index 0000000..0b66c29 --- /dev/null +++ b/EAID_000083/analysis/00a_export_qc_data.R @@ -0,0 +1,137 @@ +suppressPackageStartupMessages({ + library(tidyverse) + library(data.table) + library(dtplyr) + library(parallel) + library(Matrix) +}) +source("analysis/utils.R") + +meta <- read_tsv("config/panc_samples.tsv", col_types="cccccc") +cutoffs <-read_tsv("config/sample_cutoffs.tsv") + +panc_all <- meta$id +panc_multiome <- meta %>% filter(multiome_series != "n/a") %>% pull(id) + +gencode <- read_gtf("data/gencode.v29.annotation.gtf.gz", attributes=c("gene_type", "gene_id", "gene_name"), skip="chr1") %>% + as_tibble() %>% + filter(feature == "gene") +mito_genes <- gencode %>% filter(seqnames == "chrM") %>% pull(gene_id) +ribo_genes <- gencode %>% filter(gene_type == "rRNA") %>% pull(gene_id) + +# rna_dataset: ENCODE scRNA-Seq dataset ID +# rna_barcode: scRNA-Seq barcode +# rna_frac_mito: Fraction of mitochondrial RNA reads +# rna_frac_ribo: Fraction of ribosomal RNA reads +# rna_umi_count: scRNA UMI’s per cell +rna_qc <- mclapply(panc_all, mc.cores=6, function(s) { + mat <- readRDS(sprintf("data/seurat/%s/rna_raw.rds", s)) + tibble( + sample_name = s, + rna_barcode = colnames(mat), + cell_id = sprintf("%s#%s", sample_name, rna_barcode), + rna_umi_count = colSums(mat), + rna_frac_mito = colSums(mat[mito_genes,]) / rna_umi_count, + rna_frac_ribo = colSums(mat[ribo_genes,]) / rna_umi_count + ) +}) %>% bind_rows() + +# rna_dataset: ENCODE scRNA-Seq dataset ID +rna_qc <- rna_qc %>% + inner_join( + select(meta, rna_dataset=rna_id, sample_name=id, multiome_series), + by = "sample_name" + ) %>% + mutate(multiome_series=na_if(multiome_series, "n/a")) + +# atac_fragment_count: snATAC-Seq fragments per cell +# atac_tss_enrichment: snATAC-Seq transcription start site enrichment +atac_qc <- mclapply(panc_all, mc.cores=6, function(s) { + atac_qc <- readRDS(sprintf("data/archr/%1$s/ArchR_Project_unfiltered/QualityControl/%1$s/%1$s-Pre-Filter-Metadata.rds", s)) + tibble( + sample_name = s, + cell_id = atac_qc$cellNames, + atac_fragment_count = atac_qc$nFrags, + atac_tss_enrichment = atac_qc$TSSEnrichment + ) +}) %>% bind_rows() + + +reverse_complement <- function(x) { + stringi::stri_reverse(x) %>% + toupper() %>% + gsub("A", "t", .) %>% + gsub("T", "a", .) %>% + gsub("G", "c", .) %>% + gsub("C", "g", .) %>% + toupper() +} + +# atac_barcode: snATAC-Seq barcode +atac_qc <- atac_qc %>% + mutate(rna = str_remove(cell_id, "^[^#]+#")) %>% + left_join(read_tsv("config/10x_barcode_translation.txt.gz", col_types="cc"), by="rna") %>% + mutate(atac_barcode = if_else(is.na(atac), rna, reverse_complement(atac))) %>% + select(!c(rna, atac)) +# atac_dataset: ENCODE snATAC-Seq dataset ID +atac_qc <- atac_qc %>% + inner_join( + select(meta, atac_dataset=atac_id, sample_name=id, multiome_series), + by = "sample_name" + ) %>% + mutate(multiome_series=na_if(multiome_series, "n/a")) + +joint_qc <- mclapply(panc_all, mc.cores=6, function(s) { + m <- meta %>% filter(id == s) + rna_qc <- filter(rna_qc, rna_dataset == m$rna_id) + atac_qc <- filter(atac_qc, atac_dataset == m$atac_id) + if (m$multiome_series == "n/a") { + bind_rows(rna_qc, atac_qc) + } else { + rna_qc %>% + lazy_dt() %>% + select(!multiome_series) %>% + full_join(atac_qc, by=c("sample_name", "cell_id")) %>% + select(!multiome_series) %>% + mutate( + across(c(rna_umi_count, rna_frac_mito, rna_frac_ribo, atac_fragment_count, atac_tss_enrichment), + ~ replace_na(.x, 0)) + ) %>% + mutate( + rna_dataset = m$rna_id, + atac_dataset = m$atac_id + ) %>% + collect() + } +}) %>% bind_rows() + + +final_qc <- joint_qc %>% + inner_join(cutoffs, by=c("sample_name"="sample_id")) %>% + mutate( + passed_atac = atac_fragment_count >= min_nFrags & atac_tss_enrichment >= 5, + passed_rna = rna_umi_count >= min_umis, + passed_filtering = (passed_atac | is.na(atac_dataset)) & (passed_rna | is.na(rna_dataset)) + ) %>% + select(cell_id, sample_name, rna_dataset, rna_barcode, atac_dataset, atac_barcode, + rna_umi_count, atac_fragment_count, + rna_frac_mito, rna_frac_ribo, atac_tss_enrichment, + passed_filtering) + +header_text <- c( + "# cell_id: Cell ID used in integrated analysis", + "# rna_dataset: ENCODE snRNA-Seq dataset ID", + "# rna_barcode: snRNA-Seq barcode", + "# atac_dataset: ENCODE snATAC-Seq dataset ID", + "# atac_barcode: snATAC-Seq barcode", + "# rna_umi_count: snRNA UMI's per cell", + "# atac_fragment_count: snATAC-Seq fragments per cell", + "# rna_frac_mito: Fraction of mitochondrial RNA reads", + "# rna_frac_ribo: Fraction of ribosomal RNA reads", + "# atac_tss_enrichment: snATAC-Seq transcription start site enrichment", + "# passed_filtering: A binary (0/1) value indicating whether the cell passed manual filtering" +) + + +write_lines(header_text, "out/metadata.tsv.gz") +write_tsv(final_qc, "out/metadata.tsv.gz", append=TRUE, col_names=TRUE) diff --git a/EAID_000083/analysis/00b_raw_rna_and_qc_plots.R b/EAID_000083/analysis/00b_raw_rna_and_qc_plots.R new file mode 100644 index 0000000..a01f414 --- /dev/null +++ b/EAID_000083/analysis/00b_raw_rna_and_qc_plots.R @@ -0,0 +1,72 @@ +suppressPackageStartupMessages({ + library(tidyverse) + library(Seurat) + library(SeuratDisk) + library(patchwork) + library(data.table) + library(GenomicRanges) +}) +# This is adapted from 05_analysis/meetings/Mar30/PANC_analysis.R +# The aim is just to produce a list of Seurat objects with the filtered cells + +# raw counts matrices (subsetting to protein_coding, lincrna, and TR/IG genes) +source("analysis/utils.R") + +meta <- read_tsv("config/panc_samples.tsv") +cutoffs <-read_tsv("config/sample_cutoffs.tsv") + +panc_all <- meta %>% pull(id) +panc_multiome <- meta %>% filter(multiome_series != "n/a") %>% pull(id) + + +qc <- fread("out/metadata.tsv.gz", skip="cell_id\tsample_name") + + +tss_plots <- map(panc_all, function(s) { + c <- filter(cutoffs, sample_id == s) + cutoff_plot_hexbin(filter(qc, sample_name == s), log10(atac_fragment_count), atac_tss_enrichment, log10(c$min_nFrags), 5) + + theme_bw() + ggtitle(s) + ylim(0, 30) +}) +tss_plot <- patchwork::wrap_plots(tss_plots, ncol=3) + +rna_count_plots <- map(panc_all, function(s) { + c <- filter(cutoffs, sample_id == s) + read_count_cutoff_plot(filter(qc, sample_name == s) %>% pull(rna_umi_count), c$min_umis) + + theme_bw() + ggtitle(s) +}) +rna_count_plot <- patchwork::wrap_plots(rna_count_plots, ncol=3) + +gene_metadata <- read_tsv("data/ENCODE/samples/W61_PANC/rna/features.tsv.gz", col_names = c("id", "symbol", "type")) + +gencode <- read_gtf("data/gencode.v29.annotation.gtf.gz", attributes=c("gene_type", "gene_id", "gene_name"), skip="chr1") %>% + as_tibble() %>% + filter(feature == "gene") + +keeper_genes <- gencode %>% + filter(gene_type %in% c("protein_coding", "lincRNA") | str_detect(gene_type, "^IG_|TR_")) + + +rna_mats <- map(panc_all, function(s) { + c <- filter(cutoffs, sample_id == s) + passing_cells <- rna_qc %>% + filter(umis >= c$min_umis, sample_id == s) %>% + pull(cell_barcode) + passing_cells_atac <- atac_qc %>% + filter(TSSEnrichment >= 5, sample_id == s, nFrags >= c$min_nFrags) %>% + pull(cell_barcode) + if (filter(meta, id == s)[["multiome_series"]] != "n/a") + passing_cells <- intersect(passing_cells, passing_cells_atac) + mat <- readRDS(sprintf("data/seurat/%s/rna_raw.rds", s))[keeper_genes$gene_id,passing_cells] + rownames(mat) <- keeper_genes$gene_name + mat +}) + +seurat_raw <- mclapply(seq_along(rna_mats), mc.cores=4, function(i) { + mat <- rna_mats[[i]] + sample <- panc_all[i] + meta.data <- data.frame(sample=rep_len(sample, ncol(mat))) + rownames(meta.data) <- colnames(mat) + CreateSeuratObject(mat, project=sample, meta.data=meta.data) +}) + +saveRDS(seurat_raw, "data/seurat_raw.rds") + diff --git a/EAID_000083/analysis/01a_integrate_tabula_sapiens.R b/EAID_000083/analysis/01a_integrate_tabula_sapiens.R new file mode 100644 index 0000000..1d438b7 --- /dev/null +++ b/EAID_000083/analysis/01a_integrate_tabula_sapiens.R @@ -0,0 +1,40 @@ +suppressPackageStartupMessages({ + library(tidyverse) + library(Seurat) + library(SeuratDisk) + library(patchwork) + library(data.table) + library(GenomicRanges) +}) +# Prevent BLAS ops from trying to oversubscribe cores +RhpcBLASctl::blas_set_num_threads(2) + +seurat_vanilla <- readRDS("data/seurat_raw.rds") + +ts_panc <-LoadH5Seurat( + "data/TS_Pancreas.h5seurat", + assays = c("RNA"), + misc = FALSE, + tools = FALSE +) +ts_panc <- ts_panc %>% + NormalizeData() %>% + FindVariableFeatures() + +# About 80 seconds +system.time( + anchors <- mclapply(seurat_vanilla, mc.cores = 8, function(p) { + features <- SelectIntegrationFeatures(list(ts_panc, p)) + anchors <- FindTransferAnchors(ts_panc, p, features = features) + anchors + }) +) + +cell_labels <- map(seq_along(anchors), function(i) tibble( + cell_label=TransferData(anchors[[i]], ts_panc$cell_ontology_class)$predicted.id, + sample = seurat_vanilla[[i]]$sample, + cell_id = colnames(seurat_vanilla[[i]]) + )) %>% + bind_rows() + +write_tsv(cell_labels, "data/tabula_sapiens_cell_labels.tsv") diff --git a/EAID_000083/analysis/01b_integrate_samples.R b/EAID_000083/analysis/01b_integrate_samples.R new file mode 100644 index 0000000..3b872d3 --- /dev/null +++ b/EAID_000083/analysis/01b_integrate_samples.R @@ -0,0 +1,156 @@ +suppressPackageStartupMessages({ + library(tidyverse) + library(Seurat) + library(SeuratDisk) + library(patchwork) + library(data.table) + library(GenomicRanges) +}) +# Prevent BLAS ops from trying to oversubscribe cores +RhpcBLASctl::blas_set_num_threads(2) + +meta <- read_tsv("config/panc_samples.tsv") +panc_all <- meta %>% pull(id) + +seurat_list <- readRDS("data/seurat_raw.rds") + +# About 3 minutes +system.time( + seurat_list <- mclapply(seurat_list, mc.cores=8, function(proj) { + proj %>% + SCTransform(method = "glmGamPoi") %>% + RunPCA() + }) +) + +# About 1 minute +system.time( + seurat_list <- mclapply(seurat_list, mc.cores=8, function(proj) { + proj %>% + FindNeighbors(dims=1:30) %>% + RunUMAP(dims=1:30) %>% + FindClusters() + }) +) + + +cell_mapping <- read_tsv("data/tabula_sapiens_cell_labels.tsv") %>% + mutate(cell_label=as.factor(cell_label)) + +unique_clusts <- unique(cell_mapping$cell_label) +ts_colors <- set_names(clust_colors[seq_along(unique_clusts)], unique_clusts) + +# 10 seconds +system.time( + seurat_list <- mclapply(seurat_list, mc.cores=8, function(proj) { + proj[["ts_cell_mapping"]] <- cell_mapping %>% filter(sample == proj$sample[1]) %>% + pull(cell_label, name=cell_id) %>% + .[colnames(proj)] + proj + }) +) + +elbow_plots <- map(seq_along(seurat_list), function(i) ElbowPlot(seurat_list[[i]], ndims=50) + ggtitle(panc_all[i])) %>% + patchwork::wrap_plots() + +clust_colors <- c(ArchR:::ArchRPalettes$stallion, ArchR:::ArchRPalettes$kelly) +names(clust_colors) <- NULL + +umap_individual_plots <- map(seq_along(seurat_list), function(i) { + DimPlot(seurat_list[[i]], label=TRUE) + + ggtitle(panc_all[i]) + + scale_color_manual(values=clust_colors) + + guides(color="none") +}) %>% + patchwork::wrap_plots() + + +umap_cell_mapping <- map(seq_along(seurat_list), function(i) { + unique_clusts <- unique(cell_mapping$cell_label) + colors <- set_names(clust_colors[seq_along(unique_clusts)], unique_clusts) + DimPlot(seurat_list[[i]], group.by="ts_cell_mapping") + + ggtitle(panc_all[i]) + + scale_color_manual(values=colors) +}) %>% + patchwork::wrap_plots() + patchwork::plot_layout(guides="collect") + +saveRDS(seurat_list, "data/seurat_sct_list.rds", compress=FALSE) + +pdf("plots/sct_per_sample_plots.pdf", width=14, height=8) +elbow_plots +umap_individual_plots +umap_cell_mapping +dev.off() + +features <- SelectIntegrationFeatures(object.list = seurat_list, nfeatures = 3000) +seurat_list <- PrepSCTIntegration(object.list = seurat_list, anchor.features = features) + +#30 minutes +system.time( + integration.anchors <- FindIntegrationAnchors(object.list = seurat_list, normalization.method = "SCT", anchor.features = features) +) +# This also takes several minutes, ot sure of exact timing +seurat_combined <- IntegrateData(anchorset = integration.anchors, normalization.method = "SCT") + +# 7 min compressed, 25s uncompressed, definitely not worth waiting 6 minutes to save 4.5GB disk space +system.time( + saveRDS(seurat_combined, "data/seurat_combined.rds", compress=FALSE) +) + + +# Liberate some threads for PCA +RhpcBLASctl::blas_set_num_threads(8) +# 3 minutes +system.time( + seurat_combined <- seurat_combined %>% + RunPCA() %>% + FindNeighbors(dims=1:30) %>% + RunUMAP(dims=1:30) %>% + FindClusters() +) + + +pdf("plots/combined_summary_plots.pdf", width=14, height=8) + +# Some overall UMAP plots & cell type composition +DimPlot(seurat_combined, label = TRUE) + + scale_color_manual(values=clust_colors) + + guides(color="none") + + coord_fixed() + +DimPlot(seurat_combined, group.by="ts_cell_mapping", label = TRUE) + + scale_color_manual(values=ts_colors) + + coord_fixed() + +DimPlot(seurat_combined, group.by="sample", shuffle=TRUE) + + scale_color_brewer(palette="Dark2") + + coord_fixed() + +clust_mapping <- tibble( + sample = seurat_combined$sample, + clust = factor(str_c("C", seurat_combined$seurat_clusters), str_c("C", levels(seurat_combined$seurat_clusters))) +) %>% group_by(sample, clust) %>% + summarize(count = n()) %>% + ungroup() + +#cell_type_fraction_plot +clust_mapping %>% + group_by(sample) %>% + mutate(fraction=count/sum(count)) %>% + ggplot(aes(sample, fraction, fill=clust)) + + geom_col() + + scale_fill_manual(values=clust_colors) + + theme_classic() + + coord_flip() + +# sample_id_fraction_plot +clust_mapping %>% + group_by(clust) %>% + mutate(fraction=count/sum(count)) %>% + ggplot(aes(clust, fraction, fill=sample)) + + geom_col() + + scale_fill_brewer(palette="Set1") + + theme_classic() + + coord_flip() + +dev.off() diff --git a/EAID_000083/analysis/02_export_cell_coords.R b/EAID_000083/analysis/02_export_cell_coords.R new file mode 100644 index 0000000..7278ae9 --- /dev/null +++ b/EAID_000083/analysis/02_export_cell_coords.R @@ -0,0 +1,47 @@ +suppressPackageStartupMessages({ + library(tidyverse) + library(data.table) + library(dtplyr) + library(parallel) + library(Matrix) + library(Seurat) +}) + +seurat_combined <- readRDS("data/seurat_combined.rds") +joint_qc <- fread("out/metadata.tsv.gz") + +seurat_cells <- str_c(seurat_combined$orig.ident, "#", colnames(seurat_combined)) %>% + str_remove("_[0-9]$") + +all.equal( + rownames(seurat_combined@reductions$pca@cell.embeddings), + Cells(seurat_combined)) + +pca_comment <- c( + "# cell_id: unique cell identifier for analysis", + "# PC_*: PCA coordinate, ordered by decreasing variance explained" +) +pca_coords <- as_tibble(seurat_combined@reductions$pca@cell.embeddings) %>% + mutate(cell_id = seurat_cells) %>% + select(cell_id, everything()) + +dir.create("out/embeddings") +write_lines(pca_comment, "out/embeddings/pca.tsv.gz") +write_tsv(pca_coords, "out/embeddings/pca.tsv.gz", append=TRUE, col_names=TRUE) + + +all.equal( + rownames(seurat_combined@reductions$umap@cell.embeddings), + Cells(seurat_combined)) +umap_comment <- c( + "# cell_id: unique cell identifier for analysis", + "# UMAP_*: UMAP coordinate" +) +umap_coords <- as_tibble(seurat_combined@reductions$umap@cell.embeddings) %>% + mutate(cell_id = seurat_cells) %>% + select(cell_id, everything()) + +write_lines(umap_comment, "out/embeddings/umap.tsv.gz") +write_tsv(umap_coords, "out/embeddings/umap.tsv.gz", append=TRUE, col_names=TRUE) + + \ No newline at end of file diff --git a/EAID_000083/analysis/03_export_cell_type_calls_and_plots.R b/EAID_000083/analysis/03_export_cell_type_calls_and_plots.R new file mode 100644 index 0000000..f52da56 --- /dev/null +++ b/EAID_000083/analysis/03_export_cell_type_calls_and_plots.R @@ -0,0 +1,220 @@ +suppressPackageStartupMessages({ + library(Seurat) + library(Azimuth) + library(SeuratData) + library(patchwork) + library(tidyverse) + library(parallel) +}) + +annotation <- c( + "0" = "acinar", + "1" = "acinar", + "3" = "acinar", + "4" = "acinar", + "5" = "acinar", + "7" = "acinar", + "11" = "acinar", + "12" = "acinar", + "16" = "acinar", + "2" = "ductal", + "6" = "ductal", + "8" = "ductal", + "17" = "ductal", + "18" = "ductal", + "13" = "beta", + "22" = "gamma", + "14" = "alpha_gamma_epsilon", + "19" = "t", + "10" = "myeloid", + "20" = "immune", + "9" = "stellate", + "21" = "stellate", + "25" = "stellate", + "15" = "endothelial", + "23" = "endothelial", + "24" = "schwann" +) +# Load datasets + metadata +seurat_combined <- readRDS("data/seurat_combined.rds") +azimuth_panc <- readRDS("data/azimuth_references/azimuth_panc/ref.Rds") + +seurat_combined$annotated_clusters <- annotation[as.character(seurat_combined@active.ident)] +seurat_cells <- str_c(seurat_combined$orig.ident, "#", colnames(seurat_combined)) %>% + str_remove("_[0-9]$") + +annotation_comment <- c( + "# cell_id: Cell ID used in integrated analysis", + "# cell_type_id: ENCODE ID of the corresponding cell type object", + "# cell_type_name: Common name of the cell type", + "# membership_score: A numeric score for the labeling (Placeholder)" +) +annotations_table <- tibble( + cell_id = seurat_cells, + cell_type_id = seurat_combined$annotated_clusters, + cell_type_name = seurat_combined$annotated_clusters, + membership_score = NA +) + +dir.create("out/labels") +write_lines(annotation_comment, "out/labels/cell_types.tsv.gz") +write_tsv(annotations_table, "out/labels/cell_types.tsv.gz", append=TRUE, col_names=TRUE) + +automated_clusters <- tibble( + cell_id = seurat_cells, + cell_type_id = seurat_combined@active.ident, + cell_type_name = seurat_combined@active.ident, + membership_score = NA +) +write_lines(annotation_comment, "out/labels/automated_clusters.tsv.gz") +write_tsv(annotations_table, "out/labels/automated_clusters.tsv.gz", append=TRUE, col_names=TRUE) + + +passing_cells <- fread("out/metadata.tsv.gz") %>% + as_tibble() %>% + filter(passed_filtering, !is.na(rna_barcode)) %>% + pull(cell_id) + +meta <- read_tsv("config/panc_samples.tsv") +cutoffs <-read_tsv("config/sample_cutoffs.tsv") + +panc_all <- meta$id +panc_multiome <- meta %>% filter(multiome_series != "n/a") %>% pull(id) + +gene_metadata <- read_tsv("data/ENCODE/samples/W61_PANC/rna/features.tsv.gz", col_names = c("id", "symbol", "type")) +ensg_to_symbol <- pull(gene_metadata, symbol, name="id") + +seurat_mats <- mclapply(panc_all, mc.cores=6, function(s) { + mat <- readRDS(sprintf("data/seurat/%s/rna_raw.rds", s)) + rownames(mat) <- ensg_to_symbol[rownames(mat)] + colnames(mat) <- str_c(s, "#", colnames(mat)) + cells <- intersect(colnames(mat), passing_cells) + mat <- mat[rownames(azimuth_panc),cells] + mat +}) + +names(seurat_mats) <- panc_all + +# Perform alignment with the azimuth reference +#142 seconds with 4 cores +system.time({ + azimuth_results <- mclapply(seurat_mats, mc.cores=4, function(m) { + RunAzimuth(CreateSeuratObject(m), "data/azimuth_references/azimuth_panc/") + }) +}) + +azimuth_cell_type <- lapply(azimuth_results, function(x) x$predicted.annotation.l1) %>% + set_names(NULL) %>% + do.call(c, .) + + +#' Given two lists of alternate cluster assignements (e.g. one integer id per cell), plots a heatmap of the +#' pairwise overlap between clusters as measured by jaccard similarity. +cluster_overlap_heatmap <- function(clust1, clust2) { + jaccard_similarities <- tidyr::crossing(clust1 = clust1, + clust2 = clust2) %>% + rowwise() %>% + mutate( + jaccard = sum(.env$clust1 == clust1 & .env$clust2 == clust2) / + sum(.env$clust1 == clust1 | .env$clust2 == clust2), + jaccard = replace_na(jaccard, 0) + ) + + ggplot(jaccard_similarities, aes(clust1, clust2, fill=jaccard)) + + geom_tile() + + scale_fill_gradient(low="white", high="steelblue") +} + + +# Plot heatmaps for annotation overlaps + +dotplot_order <- c("0", "1", "3", "4", "5", "7", "11", "12", "16", "2", "6", "8", "17", + "18", "13", "22", "14", "19", "10", "20", "9", "21", "25", + "15", "23", "24") + +azimuth_order <- c("acinar_11", "ductal_10", "beta_12", "alpha_13", "delta_9", "gamma_8", + "epsilon_7", "immune_2", "activated_stellate_6", "quiescent_stellate_3", + "endothelial_4", "schwann_5", "cycling_1") + +ts_order <- c("pancreatic acinar cell", "pancreatic ductal cell", "pancreatic beta cell", + "pancreatic alpha cell", "pancreatic delta cell", "t cell", "myeloid cell", + "b cell", "mast cell", "plasma cell", "pancreatic stellate cell", "fibroblast", + "endothelial cell") + +saveRDS(azimuth_cell_type, "data/azimuth_cell_type.rds") + +p1 <- cluster_overlap_heatmap(seurat_combined$seurat_clusters, azimuth_cell_type) + + scale_x_discrete(limits=dotplot_order) + + scale_y_discrete(limits=str_remove(azimuth_order, "_[0-9]+$")) + + scale_fill_distiller(palette="Spectral") + + ggtitle("Azimuth cell calls") + +p2 <- cluster_overlap_heatmap(seurat_combined$seurat_clusters, seurat_combined$ts_cell_mapping) + + scale_x_discrete(limits=dotplot_order) + + scale_y_discrete(limits=ts_order) + + scale_fill_distiller(palette="Spectral") + + ggtitle("TS cell calls") + +dir.create("figures") +ggsave(plot = p1, "figures/Azimuth_clusters.pdf", width=7, height=4) +ggsave(plot = p2, "figures/TS_clusters.pdf", width=7, height=4) + + +# Plot external marker gene lists +azimuth_markers <- read_tsv("config/azimuth_panc_markers.tsv", col_types="") + + +azimuth_markers_list <- str_split(azimuth_markers$Markers, ", ") %>% + set_names(azimuth_markers$Label) + +system.time({ + seurat_combined_module <- AddModuleScore( + seurat_combined, azimuth_markers_list, search = FALSE, name=paste0(names(azimuth_markers_list), "_")) +}) + + +p3 <- DotPlot(seurat_combined_module, cluster.idents=TRUE, features=azimuth_order) + + coord_flip() + + theme_classic() + + scale_y_discrete(limits=dotplot_order) + + ggtitle("Azimuth marker module scores") +ggsave(plot = p3, "figures/Azimuth_modules.pdf", width=7, height=4) + +marker_genes_short <- c( + "KRT8", "PRSS1", "CFTR", "KRT19", "SYP", "INS", "GCG", "SST", "PTPRC", "CD3E", "CD8A","NKG7", "CD163", "ITGAX", + "ACTA2", "LAMA2", "PDGFRB", "FABP4", "LEP" +) + +p4 <- DotPlot(seurat_combined, cluster.idents=TRUE, + features=marker_genes_short) + + coord_flip() + + theme_classic() + + scale_y_discrete(limits=dotplot_order) + + ggtitle("Hubmap selected marker genes") +ggsave(plot = p4, "figures/Hubmap_markers.pdf", width=7, height=4) + + +# Plot UMAP + +umap_coords <- read_tsv("out/embeddings/umap.tsv.gz", comment="# ") + +clust_colors <- c("#D51F26" ,"#272E6A" ,"#208A42" ,"#89288F" ,"#F47D2B" ,"#FEE500" ,"#8A9FD1" ,"#C06CAB" ,"#E6C2DC" ,"#90D5E4" ,"#89C75F" + ,"#F37B7D" ,"#9983BD" ,"#D24B27" ,"#3BBCA8" ,"#6E4B9E" ,"#0C727C" ,"#7E1416" ,"#D8A767" ,"#3D3D3D" ,"#FFB300" ,"#803E75" + ,"#FF6800" ,"#A6BDD7" ,"#C10020" ,"#CEA262" ,"#817066" ,"#007D34" ,"#F6768E" ,"#00538A" ,"#FF7A5C" ,"#53377A" ,"#FF8E00" + ,"#B32851" ,"#F4C800" ,"#7F180D" ,"#93AA00" ,"#593315" ,"#F13A13" ,"#232C16") + +p5 <- DimPlot(seurat_combined, label=TRUE) + + scale_color_manual(values=clust_colors) + + guides(color="none") + + coord_fixed() + +ggsave(plot=p5, "figures/umap_automated_clusts.png", width = 4, height = 4, dpi=400) + +p6 <- DimPlot(seurat_combined, label=TRUE, group.by="annotated_clusters") + + scale_color_manual(values=clust_colors) + + guides(color="none") + + coord_fixed() + + labs(title = "automated clusters") + +ggsave(plot=p6, "figures/umap_annotated_clusts.png", width = 4, height = 4, dpi=400) + diff --git a/EAID_000083/analysis/04_export_misc.R b/EAID_000083/analysis/04_export_misc.R new file mode 100644 index 0000000..c9537e0 --- /dev/null +++ b/EAID_000083/analysis/04_export_misc.R @@ -0,0 +1,80 @@ +suppressPackageStartupMessages({ + library(tidyverse) +}) + +meta <- read_tsv("config/panc_samples.tsv", col_types="cccccc") + +datasets <- c(meta$rna_id, meta$atac_id[meta$multiome_series != "n/a"]) + +write_lines(datasets, "out/datasets.txt") + +# Output marker genes + +azimuth_markers <- read_tsv("config/azimuth_panc_markers.tsv", col_types="") + + +# My table columns: +# - gene_id: ENSEMBL Gene ID +# - gene_name: Common name of the gene +# - is_enriched: A binary (0/1) value indicating whether the given gene is enriched for the cell type +# - source: (hubmap | azimuth | hubmap+azimuth) + +# From asct+b +hubmap_markers <- list( + "acinar" = c("AMY2A", "PRSS1", "CTRB1", "CELA3A", "PNLIP", "KRT18", "KRT8"), + "ductal" = c("CFTR", "KRT19", "KRT7"), + "beta" = c("CHGA", "SYP", "UCHL1", "INS", "ENTPD3"), + "alpha_gamma_epsilon" = c("CHGA", "SYP", "GCG", "ARX", "SST"), + "t" = c("PTPRC", "CD3E", "CD8A", "CD4"), + "myeloid" = c("PTPRC", "CD163", "MRC1"), + "immune" = c("PTPRC", "ITGAX"), + "stellate" = c("ACTA2", "LAMA2", "PDGFRB") +) + +azimuth_markers <- read_tsv("config/azimuth_panc_markers.tsv", col_types="") +azimuth_markers_list <- str_split(azimuth_markers$Markers, ", ") %>% + set_names(azimuth_markers$Label) + +azimuth_markers_clean <- list( + "acinar" = azimuth_markers_list$acinar, + "ductal" = azimuth_markers_list$ductal, + "beta" = azimuth_markers_list$beta, + "alpha_gamma_epsilon" = c(azimuth_markers_list$alpha, azimuth_markers_list$gamma, azimuth_markers_list$epsilon), + "gamma" = azimuth_markers_list$gamma, + "immune" = azimuth_markers_list$immune, + "stellate" = c(azimuth_markers_list$activated_stellate, azimuth_markers_list$quiescent_stellate), + "schwann" = azimuth_markers_list$schwann, + "endothelial" = azimuth_markers_list$endothelial +) + +gene_metadata <- read_tsv("data/ENCODE/samples/W61_PANC/rna/features.tsv.gz", col_names = c("gene_id", "gene_name", "type")) + + +marker_list <- bind_rows( + enframe(hubmap_markers, name="cell_type", value="gene_name") %>% mutate("source"= "hubmap"), + enframe(azimuth_markers_clean, name="cell_type", value="gene_name") %>% mutate("source"="azimuth") +) %>% + unnest_longer(gene_name) %>% + inner_join(select(gene_metadata, gene_id, gene_name), by="gene_name") %>% + group_by(cell_type, gene_name, gene_id) %>% + summarize(source=paste(sort(source), collapse="+"), .groups="drop") + +# Actually save the marker gene files +dir.create("out/markers") +comments <- c( + "# gene_id: ENSEMBL Gene ID", + "# gene_name: Common name of the gene", + "# is_enriched: A binary (0/1) value indicating whether the given gene is enriched for the cell type (FDR < 0.1)", + "# source: hubmap asct+b (https://doi.org/10.48539/HBM557.XHHV.557) or azimuth (https://azimuth.hubmapconsortium.org/references/#Human%20-%20Pancreas)" +) +for (type in unique(marker_list$cell_type)) { + file_path <- sprintf("out/markers/%s.tsv.gz", type) + write_lines(comments, file_path) + marker_list %>% + filter(cell_type == type) %>% + mutate(is_enriched = 1) %>% + select(gene_id, gene_name, is_enriched, source) %>% + write_tsv(file_path, append=TRUE, col_names=TRUE) +} + + diff --git a/EAID_000083/analysis/utils.R b/EAID_000083/analysis/utils.R new file mode 100644 index 0000000..d0f2d0d --- /dev/null +++ b/EAID_000083/analysis/utils.R @@ -0,0 +1,173 @@ + +scale_fill_rainbow <- function(saturation=4, ...) { + rainbow_palette <- RColorBrewer::brewer.pal(10, "Spectral") %>% multiomeUtils:::saturate(saturation) %>% rev() + scale_fill_gradientn(colors=rainbow_palette, ...) +} + +cutoff_plot_hexbin <- function(data, x, y, x_cutoff, y_cutoff) { + #browser() + quadrant_counts <- data %>% + summarize( + top_right = sum({{x}} > x_cutoff & {{y}} > y_cutoff), + top_left = sum({{x}} <= x_cutoff & {{y}} > y_cutoff), + bot_right = sum({{x}} > x_cutoff & {{y}} <= y_cutoff), + bot_left = sum({{x}} <= x_cutoff & {{y}} <= y_cutoff) + ) %>% + mutate(across(everything(), scales::comma)) + + quadrant_label <- tribble( + ~x, ~y, ~hjust, ~vjust, ~text, + Inf, Inf, 1.5, 2, quadrant_counts$top_right, + -Inf, Inf, -0.5, 2, quadrant_counts$top_left, + Inf, -Inf, 1.5, -2, quadrant_counts$bot_right, + -Inf, -Inf, -0.5, -2, quadrant_counts$bot_left + ) + + ggplot(data, aes({{x}}, {{y}})) + + stat_bin_hex(bins = 100) + + scale_fill_rainbow(trans="log10") + + geom_hline(yintercept=y_cutoff, linetype="dashed") + + geom_vline(xintercept=x_cutoff, linetype="dashed") + + geom_text(data=quadrant_label, mapping=aes(x=x, y=y, hjust=hjust, vjust=vjust, label=text)) + + guides(fill="none") +} + +#' Make a knee plot for displaying results from read_count_cutoff +#' @param read_counts Vector of read counts per cell +#' @param cutoff Read cutoff calculated from read_count_cutoff +#' @export +read_count_cutoff_plot <- function(read_counts, cutoff) { + # Downsample the total cells considered while fitting spline. + # Keep ~1k cells per order of magnitude + ranks <- unique(floor(10^(1:(1000*ceiling(log10(length(read_counts))))/1000))) + ranks <- ranks[ranks < length(read_counts)] + + reads <- sort(read_counts, decreasing=TRUE)[ranks] + min_rank <- sum(read_counts > cutoff) + + ggplot(NULL, aes(log10(ranks), reads)) + + geom_point() + + scale_x_continuous(labels=scales::label_math()) + + scale_y_log10() + + geom_hline(yintercept=cutoff, linetype="dashed") + + labs(x = "Barcode Rank", y="Reads", + subtitle=sprintf("%s cells with > %s reads", + scales::comma(min_rank), + scales::comma(floor(cutoff)))) +} + +#' Load a gtf file into a GRanges object using data.table::fread. +#' I'm sure there's other ways of doing this, but this is simple and works for me. +#' @param path File path of gtf +#' @param attributes Attribute names to be parsed out into separate metadata columns. +#' These show up in the last column of the gtf as attribute_name "value";, e.g. transcript_id "ENST00000619216.1"; +#' @param keep_attribute_column Boolean for whether to preserve the text list of attributes as a metadata column. +#' (Useful if additional parsing of attributes is needed) +#' @param ... Additional arguments passed to fread. Of particular use is skip, for skipping commented lines at the start. +#' Set it to the text at the start of the first line to read. skip="chr" is often appropriate +#' @return GRanges object with one entry per line of the input GTF. +read_gtf <- function(path, attributes, keep_attribute_colum=FALSE, ...) { + gtf_colnames <- c("seqname", "source", "feature", "start", "end", "score", "strand", "frame", "attributes") + + ret <- data.table::fread(path, col.names=gtf_colnames, ...) + for (a in attributes) { + ret[[a]] <- str_match(ret[["attributes"]], sprintf('%s "([^"]*)"', a))[,2] + } + ret <- GenomicRanges::makeGRangesFromDataFrame(ret, keep.extra.columns = TRUE) + if(!keep_attribute_colum) + ret[["attributes"]] <- NULL + return(ret) +} + + + +#' Get a knn matrix given SVD matrix for reference and query +#' +#' @param data cell x dims matrix for reference dataset +#' @param query cell x dims matrix for query dataset (optional) +#' @param k number of neighbors to calculate +#' @param threads Number of threads to use. Note that result is non-deterministic +#' if threads > 1 +#' @param ef ef parameter for RccppHNSW::hnsw_search. Increase for slower search but +#' improved accuracy +#' @param verbose whether to print progress information during search +#' @return List of 2 matrices -- idx for cell x K neighbor indices, +#' dist for cell x K neighbor distances. +#' If no query is given, nearest neighbors are found mapping +#' the data matrix to itself, prohibiting self-neighbors +#' @export +get_knn <- function(data, query=NULL, k = 10, metric="euclidean", verbose=TRUE, threads=1, ef=100) { + index <- RcppHNSW::hnsw_build( + data, + distance=metric, + verbose=verbose, + n_threads=threads + ) + if (is.null(query)) { + res <- RcppHNSW::hnsw_search( + data, + index, + k+1, + ef=ef, + verbose=verbose, + n_threads = threads + ) + missed_searches <- sum(res$idx[,1] != seq_len(nrow(data))) + if (any(res$idx[,1] != seq_len(nrow(data)))) { + stop(sprintf("KNN search didn't find self-neighbor for %d datapoints", missed_searches)) + } + return(list( + idx = res$idx[,-1,drop=FALSE], + dist = res$dist[,-1,drop=FALSE] + )) + } else { + res <- RcppHNSW::hnsw_search( + query, + index, + k, + ef=ef, + verbose=verbose, + n_threads = threads + ) + return(res) + } +} + + +#' Internal worker function for label transfer and quality estimation +#' @param knn KNN object as returned from `get_knn`. +#' @param labels Vector of label values to transfer. Should be type integer with minimum >= 1 +#' (convert from strings or factors before calling this function) +#' @return Matrix of cells x labels +get_label_scores <- function(knn, labels) { + stopifnot(is.integer(labels)) + stopifnot(min(labels) >= 1) + + nearest_labels <- matrix(labels[knn$idx], nrow=nrow(knn$idx)) + + weights <- 1 - knn$dist / knn$dist[,ncol(knn$dist)] + weights <- weights / rowSums(weights) + + label_scores <- map( + seq_len(max(labels)), + ~ rowSums(weights * (nearest_labels == .x)) + ) %>% do.call(cbind, .) + + return(label_scores) +} + +#' Transfer labels using the most common value from the K nearest neighbors +#' +#' Neighbors are weighted similarly to Stuart et al. (2019), by 1 - (dist_i/dist_k) +#' @param knn KNN object as returned from `get_knn`. +#' @param labels Vector of label values to transfer +#' @export +transfer_labels <- function(knn, labels) { + label_values <- unique(labels) + label_indices <- match(labels, label_values) + + label_scores <- get_label_scores(knn, label_indices) + best_label <- max.col(label_scores, ties.method = "first") + + label_values[best_label] +} \ No newline at end of file diff --git a/EAID_000083/config/10x_barcode_translation.txt.gz b/EAID_000083/config/10x_barcode_translation.txt.gz new file mode 100644 index 0000000..e71fe59 Binary files /dev/null and b/EAID_000083/config/10x_barcode_translation.txt.gz differ diff --git a/EAID_000083/config/azimuth_panc_markers.tsv b/EAID_000083/config/azimuth_panc_markers.tsv new file mode 100644 index 0000000..659f13b --- /dev/null +++ b/EAID_000083/config/azimuth_panc_markers.tsv @@ -0,0 +1,14 @@ +Label Expanded Label OBO Ontology ID Markers +cycling cycling pancreatic endocrine cell UBE2C, TOP2A, CDK1, BIRC5, PBK, CDKN3, MKI67, CDC20, CCNB2, CDCA3 +immune immune leukocyte ACP5, APOE, HLA-DRA, TYROBP, LAPTM5, SDS, FCER1G, C1QC, C1QB, SRGN +quiescent_stellate quiescent stellate pancreatic stellate cell RGS5, C11orf96, FABP4, CSRP2, IL24, ADIRF, NDUFA4L2, GPX3, IGFBP4, ESAM +endothelial endothelial endothelial cell PLVAP, RGCC, ENG, PECAM1, ESM1, SERPINE1, CLDN5, STC1, MMP1, GNG11 +schwann schwann Schwann cell NGFR, CDH19, UCN2, SOX10, S100A1, PLP1, TSPAN11, WNT16, SOX2, TFAP2A +activated_stellate activated stellate pancreatic stellate cell COL1A1, COL1A2, COL6A3, COL3A1, TIMP3, TIMP1, CTHRC1, SFRP2, BGN, LUM +epsilon epsilon islet cell pancreatic epsilon cell BHMT, VSTM2L, PHGR1, TM4SF5, ANXA13, ASGR1, DEFB1, GHRL, COL22A1, OLFML3 +gamma gamma islet cell pancreatic PP cell PPY, AQP3, MEIS2, ID2, GPC5-AS1, CARTPT, PRSS23, ETV1, PPY2, TUBB2A +delta delta islet cell pancreatic D cell SST, RBP4, SERPINA1, RGS2, PCSK1, SEC11C, HHEX, LEPR, MDK, LY6H +ductal ductal pancreatic ductal cell SPP1, MMP7, IGFBP7, KRT7, ANXA4, SERPINA1, LCN2, CFTR, KRT19, SERPING1 +acinar acinar pancreatic acinar cell REG1A, PRSS1, CTRB2, CTRB1, REG1B, CELA3A, PRSS2, REG3A, CPA1, CLPS +beta beta islet cell type B pancreatic cell IAPP, INS, DLK1, INS-IGF2, G6PC2, HADH, ADCYAP1, GSN, NPTX2, C12orf75 +alpha alpha islet cell pancreatic A cell GCG, TTR, PPP1R1A, CRYBA2, TM4SF4, MAFB, GC, GPX3, PCSK2, PEMT \ No newline at end of file diff --git a/EAID_000083/config/hubmap_panc_markers.tsv b/EAID_000083/config/hubmap_panc_markers.tsv new file mode 100644 index 0000000..a495218 --- /dev/null +++ b/EAID_000083/config/hubmap_panc_markers.tsv @@ -0,0 +1,23 @@ +Cell type Gene1 Gene2 Gene3 Gene4 Gene5 Gene6 Gene7 +Acinar AMY2A PRSS1 CTRB1 CELA3A PNLIP KRT18 KRT8 +Smooth muscle ACTA2 +Ductal epithelial 1 (simple columnar) CFTR KRT19 KRT7 PDX1 +Ductal epithelial 2 (simple columnar) KRT19 PDX1 +Ductal epithelial 3 (low columnar) KRT19 +Centroacinar CA2 +Pancreatic goblet MUC2 +Tuft AVIL HPGDS +Enterochromaffin TPH1 CHGA SYP +B MS4A1 CD19 PTPRC +T CD3E CD8A CD4 PTPRC +Macrophage CD68 AIF1 CD163 MRC1 +Dendritic ITGAX +Stellate (activated) ACTA2 LAMA2 +Stellate (quiescent) PDGFRB +adipocyte LEP HOXC8 HOXC9 FABP4 +pancreatic alpha GCG ARX PDX1 CHGA SYP UCHL1 +pancreatic beta INS ENTPD3 PDX1 WFS1 CHGA SYP UCHL1 +pancreatic delta SST CHGA SYP UCHL1 +pancreatic pp PPY CHGA SYP UCHL1 +pancreatic epsilon GHRL CHGA SYP UCHL1 +pancreatic gastrin GAST \ No newline at end of file diff --git a/EAID_000083/config/panc_experiments.json b/EAID_000083/config/panc_experiments.json new file mode 100644 index 0000000..faa5512 --- /dev/null +++ b/EAID_000083/config/panc_experiments.json @@ -0,0 +1,50921 @@ +[ + { + "@context": "/terms/", + "@id": "/experiments/ENCSR229VVY/", + "@type": [ + "Experiment", + "Dataset", + "Item" + ], + "accession": "ENCSR229VVY", + "aliases": [ + "michael-snyder:pAS-361", + "michael-snyder:pAS-W73_PANC scATAC" + ], + "alternate_accessions": [], + 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b/EAID_000083/config/panc_samples.tsv @@ -0,0 +1,7 @@ +atac_id rna_id id donor multiome_series tissue +ENCSR229VVY ENCSR472RRP W73_PANC W73 ENCSR233SQG pancreas +ENCSR690NZI ENCSR478FQR W71_PANC W71 ENCSR033MDU pancreas +ENCSR496XXB ENCSR261BXB W64_PANC W64 ENCSR158DQA pancreas +ENCSR868CRK ENCSR281NBH W62_PANC W62 ENCSR316WAS pancreas +ENCSR042RUW ENCSR684KYI W80_PANC W80 n/a pancreas +ENCSR137FPX ENCSR445LCA W61_PANC W61 n/a pancreas \ No newline at end of file diff --git a/EAID_000083/config/sample_cutoffs.tsv b/EAID_000083/config/sample_cutoffs.tsv new file mode 100644 index 0000000..c788ae7 --- /dev/null +++ b/EAID_000083/config/sample_cutoffs.tsv @@ -0,0 +1,7 @@ +sample_id min_nFrags min_umis cells_atac cells_rna median_nFrags median_umis median_genes +W61_PANC 685 1000 4036 16259 1098 2635 0 +W80_PANC 478 1000 7829 6625 7363 4810 0 +W62_PANC 3000 1000 5180 5180 13991.5 3561 13991.5 +W64_PANC 1915 1000 11295 11295 3600 2339 3600 +W71_PANC 2125 1000 4151 4151 14909 3763 14909 +W73_PANC 3000 1000 9714 9714 10858.5 3546 10858.5 diff --git a/EAID_000083/data_download/01_download_metadata_json.py b/EAID_000083/data_download/01_download_metadata_json.py new file mode 100644 index 0000000..3602a8f --- /dev/null +++ b/EAID_000083/data_download/01_download_metadata_json.py @@ -0,0 +1,35 @@ +#!/usr/bin/env python +# Download JSON metadata corresponding to the ENCODE experiments +import argparse +import multiprocessing.pool + +import requests +import json + +import pandas as pd + +def main(): + parser = argparse.ArgumentParser(description='Download multiome FASTQ files from ENCODE') + parser.add_argument('--experiment_tsv', type=str, help='TSV with columns atac_id and rna_id') + parser.add_argument('--out_json', type=str, help='Output path for experiment json objects') + parser.add_argument('--threads', type=int, help="Number of files to download in parallel", default=8) + + args = parser.parse_args() + + experiments = pd.read_csv(args.experiment_tsv, sep="\t") + experiment_ids = [x for x in list(experiments.atac_id) + list(experiments.rna_id) if x.startswith("ENCSR")] + + pool = multiprocessing.pool.ThreadPool(args.threads) + def download_experiment_metadata(experiment_id): + return encode_fetch_json(f"/experiments/{experiment_id}") + + experiments = pool.map(download_experiment_metadata, experiment_ids) + json.dump(experiments, open(args.out_json, "w"), indent=2, sort_keys=True) + +def encode_fetch_json(path): + headers = {'accept': 'application/json'} + response = requests.get(f"https://www.encodeproject.org/{path}", headers=headers) + return response.json() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/EAID_000083/data_download/02_encode_download.py b/EAID_000083/data_download/02_encode_download.py new file mode 100644 index 0000000..2fb65a1 --- /dev/null +++ b/EAID_000083/data_download/02_encode_download.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python +# Download files from ENCODE related to a given set of experiments +import argparse +import multiprocessing.pool +import os.path +import requests +import json + +import tqdm + +# Example usage: +# python 03_code/encode_download/02_encode_download.py --experiment_ids 02_config/encode_experiment_ids.txt --cookies 02_config/encode_cookies.json --threads 6 --out_dir 01_raw_data/ENCODE/files + +# Description: +# Downloads all files listed under the given experiment_ids which have a correct output type. +# Saves files under out_dir/ENCFF[...].[extension] +# (So far, it seems each output_type is guaranteed to always have the same file_type, so I'm +# stopping filtering by file_type) + +def main(): + parser = argparse.ArgumentParser(description='Download multiome FASTQ files from ENCODE') + parser.add_argument('--out_dir', type=str, help='base dir for outputs') + parser.add_argument('--experiment_json', type=str, help='output path for experiment details json') + parser.add_argument('--output_types', type=str, nargs="+", help="List of output types to download", default=["reads", "index reads"]) + parser.add_argument('--threads', type=int, help="Number of files to download in parallel", default=8) + + args = parser.parse_args() + + pool = multiprocessing.pool.ThreadPool(args.threads) + os.makedirs(args.out_dir, exist_ok=True) + + experiments = json.load(open(args.experiment_json, "r")) + + files = [f["href"] for e in experiments for f in e["files"] if f["output_type"] in args.output_types] + file_sizes = {f["href"]: f["file_size"] for e in experiments for f in e["files"] if f["output_type"] in args.output_types} + total_size = sum(file_sizes.values()) + print(f"Downloading {len(files)} files, {total_size/1e9:.1f}GB total") + + progress = tqdm.tqdm(total=total_size, disable=None, unit_scale=True, unit="B") + + def download_files(file_href): + out_path = os.path.join( + args.out_dir, + os.path.basename(file_href) + ) + # Check if it's already downloaded and the size is correct + if os.path.isfile(out_path) and os.stat(out_path).st_size == file_sizes[file_href]: + progress.update(file_sizes[file_href]) + return + + encode_download_file(file_href, out_path, progress) + + + for _ in pool.imap_unordered(download_files, files): + pass + + +def encode_download_file(url_path, out_path, progress=None): + with requests.get(f"https://www.encodeproject.org/{url_path}", stream=True) as r: + r.raise_for_status() + with open(out_path, 'wb') as f: + for chunk in r.iter_content(chunk_size=1000000): + f.write(chunk) + if progress: + progress.update(len(chunk)) + + +if __name__ == "__main__": + main() + diff --git a/EAID_000083/data_download/03_link_files.py b/EAID_000083/data_download/03_link_files.py new file mode 100644 index 0000000..0a9a737 --- /dev/null +++ b/EAID_000083/data_download/03_link_files.py @@ -0,0 +1,50 @@ +#!/usr/bin/env python +# Link downloaded fastq files into a directory format usable by cellranger +import argparse +import os.path +import json + +import pandas as pd + +import tqdm + +# symlink files into a more reasonable directory structure (not designed for use +# on the fastqs) +def main(): + parser = argparse.ArgumentParser(description='Download multiome FASTQ files from ENCODE') + parser.add_argument('--experiment_json', type=str, help="Path of json file with downloaded experiment data") + parser.add_argument('--experiment_tsv', type=str, help='TSV with columns atac_id, rna_id, and id') + parser.add_argument('--output_types', type=str, nargs="+", help="List of output types to download", default=["fragments", "unfiltered sparse gene count matrix of unique reads"]) + parser.add_argument('--file_dir', type=str, help="Directory with raw downloads named by ENCODE file accession") + parser.add_argument('--out_dir', type=str, help="Directory to put output symlinks") + + args = parser.parse_args() + + os.makedirs(args.out_dir, exist_ok=True) + + experiments_json = json.load(open(args.experiment_json, "r")) + + experiments = pd.read_csv(args.experiment_tsv, sep="\t") + + sample_ids = {} + for e in experiments.itertuples(): + sample_ids[e.atac_id] = e.id + sample_ids[e.rna_id] = e.id + + files = [(sample_ids[e["accession"]], f) for e in experiments_json for f in e["files"] if f["output_type"] in args.output_types] + + for e, f in tqdm.tqdm(files): + experiment_dir = os.path.join(args.out_dir, e) + os.makedirs(experiment_dir, exist_ok=True) + + src = os.path.join(args.file_dir, os.path.basename(f["href"])) + dst = os.path.join(experiment_dir, os.path.basename(f["submitted_file_name"])) + src = os.path.relpath(src, start = os.path.dirname(dst)) + + if os.path.islink(dst) and os.readlink(dst) == src: + pass + else: + os.symlink(src, dst) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/EAID_000083/data_download/04_unpack_tar.smk b/EAID_000083/data_download/04_unpack_tar.smk new file mode 100644 index 0000000..f8b2f04 --- /dev/null +++ b/EAID_000083/data_download/04_unpack_tar.smk @@ -0,0 +1,56 @@ +import json + +import pandas as pd + +# Must have experiments_tsv set in config +samples = pd.read_csv(config["experiments_tsv"], sep="\t") + +rule all: + input: + expand("data/ENCODE/samples/{sample}/rna", sample=samples.id), + expand("data/ENCODE/samples/{sample}/fragments.tsv.gz", sample=samples.id) + +translate_py = srcdir("04b_translate_barcodes.py") + +def translate_barcodes_command(w, output): + sample = [e for e in samples.itertuples() if e.id == w.sample][0] + dir = f"data/ENCODE/samples/{w.sample}" + if not str(sample.multiome_series).startswith("ENCSR"): + # No translation needed, so just copy files + return ( + f"cp {dir}/encode_scatac_dcc_2/results/*/fragments/fragments.tsv.gz {output.fragments}; " + f"cp {dir}/encode_scatac_dcc_2/results/*/fragments/fragments.tsv.gz.tbi {output.idx}" + ) + else: + # Translate barcodes and re-compress + return ( + f"python {translate_py} {dir}/encode_scatac_dcc_2/results/*/fragments/fragments.tsv.gz | " + f"bgzip > {output.fragments}; " + f"tabix --preset bed {output.fragments}" + ) + +rule unpack_atac: + input: + tar = "data/ENCODE/samples/{sample}/fragments.tar.gz", + barcode_translation = "config/10x_barcode_translation.txt.gz" + output: + fragments = "data/ENCODE/samples/{sample}/fragments.tsv.gz", + idx = "data/ENCODE/samples/{sample}/fragments.tsv.gz.tbi" + params: + dir = "data/ENCODE/samples/{sample}", + translate_command = translate_barcodes_command + shell: "tar -xzf {input.tar} --directory {params.dir}; " + "{params.translate_command}; " + "rm -r {params.dir}/encode_scatac_dcc_2/; " + +rule unpack_rna: + input: + tar = "data/ENCODE/samples/{sample}/GeneFull_Ex50pAS_Unique_raw.tar.gz" + output: + matrix = directory("data/ENCODE/samples/{sample}/rna") + params: + dir = "data/ENCODE/samples/{sample}" + shell: "tar -xzf {input.tar} --directory {params.dir}; " + "mv {params.dir}/GeneFull_Ex50pAS/raw {params.dir}/rna; " + "gzip {params.dir}/rna/*; " + "rm -r {params.dir}/GeneFull_Ex50pAS/; " diff --git a/EAID_000083/data_download/04b_translate_barcodes.py b/EAID_000083/data_download/04b_translate_barcodes.py new file mode 100644 index 0000000..d1f4f13 --- /dev/null +++ b/EAID_000083/data_download/04b_translate_barcodes.py @@ -0,0 +1,19 @@ +import sys +import gzip + +# Usage: python translate_barcodes.py < fragments.tsv.gz | bgzip -c > renamed.tsv.gz +# then run tabix on the bgzipped output + +atac_to_rna = {} +for l in gzip.open("config/10x_barcode_translation.txt.gz"): + atac, rna = l.strip().split(b"\t") + atac_to_rna[atac] = rna +assert atac_to_rna[b"atac"] == b"rna" + +comp = bytes.maketrans(b'ATGC', b'TACG') + +for l in gzip.open(sys.argv[1]): + fields = l.split(b"\t") + rev_comp = fields[3].translate(comp)[::-1] + fields[3] = atac_to_rna[rev_comp] + sys.stdout.buffer.write(b"\t".join(fields)) \ No newline at end of file diff --git a/EAID_000083/data_import/01_archr_create_project.R b/EAID_000083/data_import/01_archr_create_project.R new file mode 100644 index 0000000..2649052 --- /dev/null +++ b/EAID_000083/data_import/01_archr_create_project.R @@ -0,0 +1,77 @@ + +# Command-line arguments: +# 1. sample name +# 2. path of input arrow file +# 3. path of output base directory + +# example: Rscript create_project.R sample1.arrow my/directory/sample1 +# Outputs to my/directory/sample1/ArchR_Project_unfiltered + +#args <- c("W73_PANC", "04_data/cellranger_arc_v2/workspace/ENCSR229VVY/outs/atac_fragments.tsv.gz", "04_data/archr/W73_PANC") +args <- commandArgs(trailing=TRUE) +stopifnot(length(args) == 3) +sample_name <- args[1] +input_fragments <- args[2] +output_base <- args[3] + +# wd <- getwd() +# setwd("/oak/stanford/groups/wjg/bparks/multiome/") +# source("renv/activate.R") +# setwd(wd) + +suppressPackageStartupMessages({ + library(ArchR) + library(tidyverse) +}) + +dir.create(output_base, recursive=TRUE, showWarnings=FALSE) + +addArchRThreads(1) + +base_dir <- getwd() + +arrow_output <- file.path(base_dir, output_base, paste0(sample_name, ".arrow")) +qc_output <- file.path(base_dir, output_base, "QualityControl") +logs_output <- file.path(base_dir, output_base, "ArchRLogs") + + +tmp <- tempdir() + +# Copy arrow files into tmp for potentially better I/O performance +tmp_fragments <- file.path(tmp, "fragments.tsv.gz") +tmp_arrow_output <- file.path(tmp, sample_name) +tmp_qc_output <- file.path(tmp, "QualityControl") + +file.copy(file.path(base_dir, input_fragments), tmp_fragments) +file.copy(file.path(base_dir, paste0(input_fragments, ".tbi")), paste0(tmp_fragments, ".tbi")) + +setwd(tmp) + +addArchRGenome("hg38") + +ArrowFiles <- createArrowFiles( + inputFiles = tmp_fragments, + sampleNames = sample_name, + outputNames = file.path(tmp, sample_name), + QCDir = file.path(tmp, "QualityControl"), + minTSS=0, + minFrags=0, + addTileMat = FALSE, + addGeneScoreMat = FALSE +) + +# Copy outputs into the destination locations +file.copy(ArrowFiles, arrow_output) + +dir.create(qc_output) +dir.create(logs_output) +file.copy( + file.path(tmp, "QualityControl", sample_name), + qc_output, + recursive=TRUE +) +file.copy( + file.path(tmp, "ArchRLogs"), + logs_output, + recursive=TRUE +) diff --git a/EAID_000083/data_import/02_rna_mat_to_rds.R b/EAID_000083/data_import/02_rna_mat_to_rds.R new file mode 100644 index 0000000..85d7a0d --- /dev/null +++ b/EAID_000083/data_import/02_rna_mat_to_rds.R @@ -0,0 +1,16 @@ +# wd <- getwd() +# setwd("/oak/stanford/groups/wjg/bparks/multiome/") +# source("renv/activate.R") +# setwd(wd) + +args <- commandArgs(trailing=TRUE) +stopifnot(length(args) == 2) +input_path <- args[1] +output_path <- args[2] + +suppressPackageStartupMessages({ + library(Seurat) +}) + +x <- Seurat::Read10X(input_path, gene.column=1) +saveRDS(x, output_path) \ No newline at end of file diff --git a/EAID_000083/data_import/03_convert_h5seurat.R b/EAID_000083/data_import/03_convert_h5seurat.R new file mode 100644 index 0000000..a7f0a73 --- /dev/null +++ b/EAID_000083/data_import/03_convert_h5seurat.R @@ -0,0 +1,23 @@ +args <- commandArgs(trailing=TRUE) +stopifnot(length(args) == 2) +input_h5ad <- args[1] +output_h5seurat <- args[2] + +# source("renv/activate.R") + +suppressPackageStartupMessages({ + library(Seurat) + library(SeuratObject) + library(SeuratDisk) +}) + +Convert(input_h5ad, output_h5seurat, + overwrite = TRUE, + verbose = FALSE) + +proj <- LoadH5Seurat( + output_h5seurat, + assays = c("RNA"), + misc = FALSE, + tools = FALSE +) diff --git a/EAID_000083/data_import/04_data_import.smk b/EAID_000083/data_import/04_data_import.smk new file mode 100644 index 0000000..2c5fb1e --- /dev/null +++ b/EAID_000083/data_import/04_data_import.smk @@ -0,0 +1,41 @@ +import collections +import json +import os.path +import re + +import pandas as pd + +samples = pd.read_csv("config/panc_samples.tsv", sep="\t") + +sample_ids = list(samples.id) + + +rule all: + input: + expand("data/archr/{sample_id}/ArchR_Project_unfiltered", sample_id=sample_ids), + expand("data/seurat/{sample_id}/rna_raw.rds", sample_id=sample_ids), + "data/TS_Pancreas.h5seurat" + +rule rna_mats: + input: "data/ENCODE/samples/{sample_id}/rna" + output: "data/seurat/{sample_id}/rna_raw.rds" + params: + script = srcdir("02_rna_mat_to_rds.R") + log: "data/seurat/logs/import_{sample_id}.log" + shell: "Rscript {params.script} {input} {output} > {log} 2> {log}" + +rule archr_import: + input: "data/ENCODE/samples/{sample_id}/fragments.tsv.gz" + output: directory("data/archr/{sample_id}/ArchR_Project_unfiltered") + params: + output_base = "data/archr/{sample_id}/ArchR_Project_unfiltered", + script = srcdir("01_archr_create_project.R") + log: "data/archr/logs/import_{sample_id}.log" + shell: "Rscript {params.script} {wildcards.sample_id} {input} {params.output_base} > {log} 2> {log}" + +rule convert_tabula_sapiens: + input: "data/TS_Pancreas.h5ad" + output: "data/TS_Pancreas.h5seurat" + params: + script = srcdir("03_convert_h5seurat.R") + shell: "Rscript {params.script} {input} {output}" \ No newline at end of file diff --git a/EAID_000083/run_all.sh b/EAID_000083/run_all.sh new file mode 100644 index 0000000..1418249 --- /dev/null +++ b/EAID_000083/run_all.sh @@ -0,0 +1,57 @@ +# Note: This script can in principle be run end-to-end, but +# is better used by copy-pasting commands one at a time with any modifications +# needed for number of threads/cores to use + +mkdir -p data +mkdir -p plots +mkdir -p data/azimuth_references/azimuth_panc + +# Download Azimuth reference datasets +wget https://zenodo.org/record/4546926/files/idx.annoy?download=1 -O data/azimuth_references/azimuth_panc/idx.annoy +wget https://zenodo.org/record/4546926/files/ref.Rds?download=1 -O data/azimuth_references/azimuth_panc/ref.Rds + + +# Download gene annotation gtf +curl https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.annotation.gtf.gz > \ + data/gencode.v29.annotation.gtf.gz + +# Download Tabula Sapiens Pancreas reference +wget https://figshare.com/ndownloader/files/34702003 -O data/TS_Pancreas.h5ad.zip +unzip data/TS_Pancreas.h5ad.zip -d data + + +# Fetch metadata for desired experiment IDs +python data_download/01_download_metadata_json.py \ + --experiment_tsv config/panc_samples.tsv \ + --out_json config/panc_experiments.json \ + --threads 25 + +# Download experiments (Just fragments and gene count matrices) +python data_download/02_encode_download.py \ + --experiment_json config/panc_experiments.json \ + --out_dir data/ENCODE/files \ + --threads 25 \ + --output_types "fragments" "unfiltered sparse gene count matrix of unique reads" + +# Construct a nice directory listing by sample +python data_download/03_link_files.py \ + --experiment_json config/panc_experiments.json \ + --experiment_tsv config/panc_samples.tsv \ + --file_dir data/ENCODE/files \ + --out_dir data/ENCODE/samples \ + --output_types "fragments" "unfiltered sparse gene count matrix of unique reads" + +# Unpack the tar.gz files into directly readable files +snakemake -j 16 -s data_download/04_unpack_tar.smk --config experiments_tsv="config/panc_samples.tsv" + +# Run data import tasks with 16 cores +snakemake -j 16 -s data_import/04_data_import.smk + +# Run all the analysis scripts in order +Rscript analysis/00a_export_qc_data.R +Rscript analysis/00b_raw_rna_and_qc_plots.R +Rscript analysis/01a_integrate_tabula_sapiens.R +Rscript analysis/01b_integrate_samples.R +Rscript analysis/02_export_cell_coords.R +Rscript analysis/03_export_cell_type_calls_and_plots.R +Rscript analysis/04_export_misc.R \ No newline at end of file