bio-workflows-multiome-pipeline

Version Compatibility

Reference examples tested with: ggplot2 3.5+

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

• 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.

Multiome Pipeline

"Analyze my 10X Multiome data jointly" → Orchestrate Cell Ranger ARC processing, Seurat/Signac scRNA+scATAC integration via WNN, chromatin accessibility peak calling, motif enrichment, and gene regulatory network inference.

Complete workflow for 10X Multiome (joint scRNA + scATAC) analysis using Seurat and Signac.

Workflow Overview

10X Multiome data
    |
    v
[1. Load Data] ---------> Read RNA + ATAC
    |
    v
[2. RNA Processing] ----> Standard scRNA workflow
    |
    v
[3. ATAC Processing] ---> Peak calling, LSI
    |
    v
[4. WNN Integration] ---> Weighted nearest neighbors
    |
    v
[5. Joint Analysis] ----> Clustering, markers
    |
    v
[6. Linked Features] ---> Gene-peak links
    |
    v
Integrated multiome object

Step 1: Load Multiome Data

library(Seurat)
library(Signac)
library(EnsDb.Hsapiens.v86)
library(ggplot2)

# Load RNA
rna_counts <- Read10X_h5('filtered_feature_bc_matrix.h5')
# For multiome, this returns a list with 'Gene Expression' and 'Peaks'

# Create Seurat object with RNA
seurat_obj <- CreateSeuratObject(
    counts = rna_counts$`Gene Expression`,
    assay = 'RNA'
)

# Load ATAC
atac_counts <- rna_counts$Peaks
# Or from fragments file
frags <- CreateFragmentObject('atac_fragments.tsv.gz', cells = colnames(seurat_obj))

# Create ChromatinAssay
atac_assay <- CreateChromatinAssay(
    counts = atac_counts,
    sep = c(':', '-'),
    fragments = frags,
    annotation = GetGRangesFromEnsDb(ensdb = EnsDb.Hsapiens.v86)
)

seurat_obj[['ATAC']] <- atac_assay

Step 2: RNA Quality Control and Processing

# QC metrics
seurat_obj[['percent.mt']] <- PercentageFeatureSet(seurat_obj, pattern = '^MT-')

# Filter
seurat_obj <- subset(seurat_obj,
    nCount_RNA > 1000 &
    nCount_RNA < 25000 &
    percent.mt < 20
)

# Normalize RNA
seurat_obj <- SCTransform(seurat_obj, assay = 'RNA', verbose = FALSE)

# PCA
seurat_obj <- RunPCA(seurat_obj, assay = 'SCT', verbose = FALSE)