Single-Cell RNA-seq Nodes

scRNA-seq nodes take droplet-based sequencing data through quantification, quality control, and downstream analysis (clustering, marker genes, UMAP).

Cell Ranger count

10x Genomics’ pipeline for read alignment and per-cell gene quantification.

Inputs: reads: FASTQ, reference: Directory (transcriptome) Outputs: matrix: Matrix, report: Report

Parameter	Type	Default	Description
`expectCells`	Int	—	Expected number of cells
`chemistry`	String	auto	10x chemistry version

Cell Ranger is memory- and disk-heavy. Use Large (128 GB)+ for human data.

Open-source alternative to Cell Ranger built on STAR; produces a compatible count matrix.

Inputs: reads: FASTQ, reference: FASTA, annotation: GTF Outputs: matrix: Matrix

Loads a count matrix and computes per-cell QC metrics; filters cells/genes.

Inputs: matrix: Matrix Outputs: adata: Matrix (h5ad), report: Report

Parameter	Type	Default	Description
`minGenes`	Int	200	Drop cells below this gene count
`maxPctMito`	Float	5.0	Drop cells above this % mito reads

Normalization, PCA, neighbors, Leiden clustering, and UMAP embedding.

Inputs: adata: Matrix Outputs: adata: Matrix, umap: Report

Parameter	Type	Default	Description
`nPCs`	Int	50	Principal components
`resolution`	Float	1.0	Leiden clustering resolution

[FASTQ Input] → [Cell Ranger count] → [Scanpy QC & Filter] → [Scanpy Cluster & Embed] → [Report Output]