--- name: bio-workflows-scrnaseq-pipeline description: End-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types. Covers QC, normalization, clustering, marker detection, and cell type annotation. Use when analyzing single-cell RNA-seq data. tool_type: mixed primary_tool: Seurat workflow: true depends_on: - single-cell/data-io - single-cell/preprocessing - single-cell/doublet-detection - single-cell/clustering - single-cell/markers-annotation qc_checkpoints: - after_loading: "Expected cell count, reasonable UMI distribution" - after_qc: "Remove low-quality cells and doublets" - after_normalization: "No batch effects, HVGs look sensible" - after_clustering: "Clusters are biologically meaningful" measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- # Single-Cell RNA-seq Pipeline Complete workflow from 10X Genomics Cell Ranger output to annotated cell types. ## Workflow Overview ``` 10X data (filtered_feature_bc_matrix) | v [1. Load Data] ---------> Read10X / read_10x_h5 | v [2. QC Filtering] ------> nFeature, percent.mt, doublets | v [3. Normalization] -----> SCTransform or LogNormalize | v [4. HVG Selection] -----> FindVariableFeatures | v [5. Dim Reduction] -----> PCA → UMAP | v [6. Clustering] --------> FindNeighbors → FindClusters | v [7. Markers] -----------> FindAllMarkers | v [8. Annotation] --------> Manual or automated | v Annotated Seurat/AnnData object ``` ## Primary Path: Seurat (R) ### Step 1: Load 10X Data ```r library(Seurat) library(ggplot2) library(dplyr) # Load from Cell Ranger output data_dir <- 'cellranger_output/filtered_feature_bc_matrix' counts <- Read10X(data.dir = data_dir) # Create Seurat object seurat_obj <- CreateSeuratObject(counts = counts, project = 'my_project', min.cells = 3, min.features = 200) ``` ### Step 2: Quality Control ```r # Calculate QC metrics seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-') seurat_obj[['percent.ribo']] <- PercentageFeatureSet(seurat_obj, pattern = '^RP[SL]') # Visualize QC metrics VlnPlot(seurat_obj, features = c('nFeature_RNA', 'nCount_RNA', 'percent.mt'), ncol = 3) # Filter cells seurat_obj <- subset(seurat_obj, nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 20 & nCount_RNA > 500) cat('Cells after QC:', ncol(seurat_obj), '\n') ``` **QC Checkpoint 1:** Review QC plots - Remove cells with very low/high gene counts - Remove cells with high mitochondrial content (dying cells) ### Step 3: Doublet Detection ```r library(scDblFinder) # Convert to SCE for scDblFinder sce <- as.SingleCellExperiment(seurat_obj) sce <- scDblFinder(sce) # Add back to Seurat seurat_obj$doublet_class <- sce$scDblFinder.class seurat_obj$doublet_score <- sce$scDblFinder.score # Remove doublets seurat_obj <- subset(seurat_obj, doublet_class == 'singlet') cat('Cells after doublet removal:', ncol(seurat_obj), '\n') ``` ### Step 4: Normalization with SCTransform ```r # SCTransform (recommended for most analyses) seurat_obj <- SCTransform(seurat_obj, vars.to.regress = 'percent.mt', verbose = FALSE) ``` Alternative: Standard normalization ```r seurat_obj <- NormalizeData(seurat_obj) seurat_obj <- FindVariableFeatures(seurat_obj, selection.method = 'vst', nfeatures = 2000) seurat_obj <- ScaleData(seurat_obj, vars.to.regress = 'percent.mt') ``` ### Step 5: Dimensionality Reduction ```r # PCA seurat_obj <- RunPCA(seurat_obj, npcs = 50, verbose = FALSE) # Determine optimal PCs ElbowPlot(seurat_obj, ndims = 50) # UMAP n_pcs <- 30 # Choose based on elbow plot seurat_obj <- RunUMAP(seurat_obj, dims = 1:n_pcs, verbose = FALSE) ``` ### Step 6: Clustering ```r # Find neighbors seurat_obj <- FindNeighbors(seurat_obj, dims = 1:n_pcs, verbose = FALSE) # Find clusters (try multiple resolutions) seurat_obj <- FindClusters(seurat_obj, resolution = c(0.2, 0.4, 0.6, 0.8, 1.0), verbose = FALSE) # Visualize DimPlot(seurat_obj, reduction = 'umap', group.by = 'SCT_snn_res.0.4', label = TRUE) ``` **QC Checkpoint 2:** Assess clustering - Clusters should be visually separable on UMAP - Resolution 0.4-0.8 is often appropriate ### Step 7: Find Marker Genes ```r # Set identity to chosen resolution Idents(seurat_obj) <- 'SCT_snn_res.0.4' # Find markers for all clusters markers <- FindAllMarkers(seurat_obj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) # Top markers per cluster top_markers <- markers %>% group_by(cluster) %>% slice_max(n = 10, order_by = avg_log2FC) # Visualize top markers DoHeatmap(seurat_obj, features = top_markers$gene) + NoLegend() ``` ### Step 8: Cell Type Annotation ```r # Manual annotation based on known markers # Example for PBMC data: cluster_annotations <- c( '0' = 'CD4 T cells', '1' = 'CD14 Monocytes', '2' = 'B cells', '3' = 'CD8 T cells', '4' = 'NK cells', '5' = 'CD16 Monocytes', '6' = 'Dendritic cells' ) seurat_obj$cell_type <- cluster_annotations[as.character(Idents(seurat_obj))] # Final UMAP DimPlot(seurat_obj, reduction = 'umap', group.by = 'cell_type', label = TRUE) # Save object saveRDS(seurat_obj, 'seurat_annotated.rds') ``` ## Alternative Path: Scanpy (Python) ```python import scanpy as sc import numpy as np # Load 10X data adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5') adata.var_names_make_unique() # QC metrics adata.var['mt'] = adata.var_names.str.startswith('MT-') sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True) # Filter sc.pp.filter_cells(adata, min_genes=200) sc.pp.filter_genes(adata, min_cells=3) adata = adata[adata.obs.n_genes_by_counts < 5000, :] adata = adata[adata.obs.pct_counts_mt < 20, :] # Doublet detection sc.pp.scrublet(adata) adata = adata[~adata.obs['predicted_doublet'], :] # Normalize and HVGs sc.pp.normalize_total(adata, target_sum=1e4) sc.pp.log1p(adata) sc.pp.highly_variable_genes(adata, n_top_genes=2000) # PCA, neighbors, UMAP sc.pp.scale(adata, max_value=10) sc.tl.pca(adata, n_comps=50) sc.pp.neighbors(adata, n_neighbors=15, n_pcs=30) sc.tl.umap(adata) # Clustering sc.tl.leiden(adata, resolution=0.5) # Markers sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon') sc.pl.rank_genes_groups(adata, n_genes=10, sharey=False) # Save adata.write('scanpy_annotated.h5ad') ``` ## Parameter Recommendations | Step | Parameter | Recommendation | |------|-----------|----------------| | QC | min.features | 200-500 | | QC | max.features | 2500-5000 (depends on data) | | QC | percent.mt | <10-20% | | SCTransform | vars.to.regress | percent.mt | | PCA | npcs | 30-50 | | UMAP | dims | 15-30 (check elbow plot) | | Clustering | resolution | 0.4-0.8 (start with 0.5) | ## Troubleshooting | Issue | Likely Cause | Solution | |-------|--------------|----------| | All cells filtered | QC too strict | Relax thresholds | | Poor UMAP separation | Too few HVGs or PCs | Increase nfeatures, check n_pcs | | Too many/few clusters | Wrong resolution | Adjust resolution parameter | | Unknown cell types | Missing markers | Check known marker genes manually | ## Complete R Workflow ```r library(Seurat) library(scDblFinder) library(ggplot2) library(dplyr) # Configuration data_dir <- 'filtered_feature_bc_matrix' output_dir <- 'results' dir.create(output_dir, showWarnings = FALSE) # Load counts <- Read10X(data.dir = data_dir) seurat_obj <- CreateSeuratObject(counts = counts, min.cells = 3, min.features = 200) cat('Initial cells:', ncol(seurat_obj), '\n') # QC seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-') seurat_obj <- subset(seurat_obj, nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 20) cat('After QC:', ncol(seurat_obj), '\n') # Doublets sce <- as.SingleCellExperiment(seurat_obj) sce <- scDblFinder(sce) seurat_obj$doublet <- sce$scDblFinder.class seurat_obj <- subset(seurat_obj, doublet == 'singlet') cat('After doublet removal:', ncol(seurat_obj), '\n') # Normalize seurat_obj <- SCTransform(seurat_obj, vars.to.regress = 'percent.mt', verbose = FALSE) # Dimension reduction seurat_obj <- RunPCA(seurat_obj, npcs = 50, verbose = FALSE) seurat_obj <- RunUMAP(seurat_obj, dims = 1:30, verbose = FALSE) # Cluster seurat_obj <- FindNeighbors(seurat_obj, dims = 1:30, verbose = FALSE) seurat_obj <- FindClusters(seurat_obj, resolution = 0.5, verbose = FALSE) # Markers markers <- FindAllMarkers(seurat_obj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) write.csv(markers, file.path(output_dir, 'markers.csv')) # Save saveRDS(seurat_obj, file.path(output_dir, 'seurat_object.rds')) # Plots pdf(file.path(output_dir, 'umap.pdf'), width = 10, height = 8) DimPlot(seurat_obj, reduction = 'umap', label = TRUE) dev.off() cat('Pipeline complete. Object saved to:', output_dir, '\n') ``` ## Related Skills - single-cell/data-io - Loading different formats - single-cell/preprocessing - QC details - single-cell/doublet-detection - Doublet methods comparison - single-cell/clustering - Clustering parameters - single-cell/markers-annotation - Annotation strategies - single-cell/multimodal-integration - CITE-seq, multiome --- Source attribution: GPTomics Original project: https://github.com/GPTomics/bioSkills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Dave AI. ## Upstream license notice MIT License Copyright (c) 2026 FridrichMethod Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. --- NOTE ON BUNDLED CONTENT This repository aggregates skills from multiple upstream projects (see the README for the source list). Each `skills//` directory may carry its own license inherited from its upstream source. The MIT license above applies to this repository's curation and the scripts/workflows in `/`, `/scripts/`, and `/.github/` — not to the upstream skill content itself. Consult the upstream repository linked in the README and any LICENSE file inside a skill directory before reusing skill content commercially. # Dave AI resource metadata Creator / publisher: GPTomics Original project: https://github.com/GPTomics/bioSkills Attribution basis: Exact package in biomedical source index and publisher source table Fields of work: Data & Analytics, Education & Research Roles: Business Intelligence Analyst, Data Scientist, Researcher Specialties: Backend, Playwright Tools: Seurat Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 9b81b7e6bab31715d43fefa075507172e84c5d8a30ae1d8cad46e6e59918e476 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Dave AI catalog attribution. Original instruction and supporting files are preserved.