Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
gptomics avatar

Bio Workflows Scrnaseq Pipeline

gptomics/bioskills

  • 5 installs
  • 1.1k repo stars
  • Updated July 25, 2026
  • gptomics/bioskills

Run an end-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types with Seurat.

About

Orchestrates QC, normalization, doublet detection, clustering, marker detection, and cell-type annotation for scRNA-seq. A developer uses it to analyze 10X Genomics single-cell data from raw matrices to annotated cell types.

  • Data I/O, preprocessing, doublet detection, and clustering
  • Marker detection and cell-type annotation with staged QC gates

Bio Workflows Scrnaseq Pipeline by the numbers

  • 5 all-time installs (skills.sh)
  • Ranked #1,598 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/gptomics/bioskills --skill bio-workflows-scrnaseq-pipeline

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs5
repo stars★ 1.1k
Last updatedJuly 25, 2026
Repositorygptomics/bioskills ↗

What it does

Run an end-to-end single-cell RNA-seq workflow from 10X Genomics data to annotated cell types with Seurat.

Files

SKILL.mdMarkdownGitHub ↗

Version Compatibility

Reference examples tested with: Cell Ranger 8.0+, ggplot2 3.5+, numpy 1.26+, scanpy 1.10+

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures
  • R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Single-Cell RNA-seq Pipeline

"Analyze my single-cell RNA-seq data from counts to cell types" -> Orchestrate QC filtering, normalization (scanpy/Seurat), batch integration (scVI/Harmony), clustering, marker detection, cell type annotation, and trajectory inference.

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

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

# 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

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

# SCTransform (recommended for most analyses)
seurat_obj <- SCTransform(seurat_obj, vars.to.regress = 'percent.mt', verbose = FALSE)

Alternative: Standard normalization

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

# 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

# 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

# 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

# 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)

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

StepParameterRecommendation
QCmin.features200-500
QCmax.features2500-5000 (depends on data)
QCpercent.mt<10-20%
SCTransformvars.to.regresspercent.mt
PCAnpcs30-50
UMAPdims15-30 (check elbow plot)
Clusteringresolution0.4-0.8 (start with 0.5)

Troubleshooting

IssueLikely CauseSolution
All cells filteredQC too strictRelax thresholds
Poor UMAP separationToo few HVGs or PCsIncrease nfeatures, check n_pcs
Too many/few clustersWrong resolutionAdjust resolution parameter
Unknown cell typesMissing markersCheck known marker genes manually

Complete R Workflow

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

  • database-access/geo-data - Resolve GSE to SRA; detect SuperSeries before processing
  • database-access/sra-data - Download 10x records with --include-technical for barcodes/UMIs
  • 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

Related skills

microsoftMicrosoft FoundryDeploy, evaluate, and continuously improve Microsoft Foundry agents from a single agent interface.490k1.3klllllllamaAi Research ReproductionOrchestrate trustworthy, auditable reproduction of deep learning repositories directly from their READMEs.176k511lllllllamaRun TrainSafely execute selected deep learning training commands with standardized evidence capture.176k511lllllllamaExplore RunSafely run isolated exploratory experiments with clear recording and conservative selection before committing changes.176k511lllllllamaPaper Context ResolverFetch precise reproduction-critical details like dataset splits, preprocessing steps, or evaluation protocols from the original academic paper when the repo README leav141k511lllllllamaRepo Intake And PlanScan unfamiliar AI research repositories and receive a minimal, trustworthy reproduction target before investing significant time.140k511
Data Science & MLpipelinesanalytics
Browse by category
AI & AgentsFrontend & DesignBackend & DataDev ToolsAutomationTesting & ReviewSecurityMonitoring & CloudDocs & Planning
Design & frontend
Design systemsAnimation & motionAccessibility (a11y)CSS & TailwindUI designUXDesign to codeshadcn/uiLanding pagesBest design skills
Most searched skills
find-skillscavemanfrontend-designbrainstormingsystematic-debuggingstop-slopemil-design-engui-ux-pro-maxkarpathy-guidelinesweb-design-guidelinesreact-best-practicesgrill-meimprovemicrosoft-foundryagent-browser
Browse all listings
All agent skillsAll MCP serversAll marketplacesAll plugins

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.