scMultiome Analysis

Input data

This walkthrough uses the 10x Genomics PBMC from a Healthy Donor - Granulocytes Removed Through Cell Sorting (10k) dataset, processed with Cell Ranger ARC. CellPilot is designed for biologists to use directly with standard commercial platform outputs. If you have your own Cell Ranger ARC output folder, you can select the output folder directly and do not need to rename files.

The download and rename commands below are only for this public 10x demo dataset, because 10x download files include the sample ID in each file name.

Open the 10x Genomics dataset page

mkdir -p pbmc_multiome_10k
cd pbmc_multiome_10k

BASE=https://cf.10xgenomics.com/samples/cell-arc/2.0.0/pbmc_granulocyte_sorted_10k
SAMPLE=pbmc_granulocyte_sorted_10k

curl -O ${BASE}/${SAMPLE}_filtered_feature_bc_matrix.h5
curl -O ${BASE}/${SAMPLE}_filtered_feature_bc_matrix.tar.gz
curl -O ${BASE}/${SAMPLE}_analysis.tar.gz
curl -O ${BASE}/${SAMPLE}_atac_peak_annotation.tsv
curl -O ${BASE}/${SAMPLE}_atac_fragments.tsv.gz
curl -O ${BASE}/${SAMPLE}_atac_fragments.tsv.gz.tbi
curl -O ${BASE}/${SAMPLE}_summary.csv
curl -O ${BASE}/${SAMPLE}_web_summary.html

1. Prepare the folder

CellPilot expects standard Cell Ranger ARC-style names inside the selected folder. After downloading and unzipping this demo dataset, remove the sample ID prefix from the files and folders.

tar -xzf ${SAMPLE}_filtered_feature_bc_matrix.tar.gz
tar -xzf ${SAMPLE}_analysis.tar.gz

mv ${SAMPLE}_filtered_feature_bc_matrix.h5 filtered_feature_bc_matrix.h5
mv ${SAMPLE}_atac_peak_annotation.tsv atac_peak_annotation.tsv
mv ${SAMPLE}_atac_fragments.tsv.gz atac_fragments.tsv.gz
mv ${SAMPLE}_atac_fragments.tsv.gz.tbi atac_fragments.tsv.gz.tbi
mv ${SAMPLE}_summary.csv summary.csv
mv ${SAMPLE}_web_summary.html web_summary.html

After cleanup, the selected folder should contain filtered_feature_bc_matrix.h5, filtered_feature_bc_matrix/, analysis/, atac_peak_annotation.tsv, summary.csv, and optionally atac_fragments.tsv.gz plus its index.

2. Load the dataset

1. Open CellPilot.
2. In the Data Type menu, choose scMultiome.
3. In the Data menu, choose Single sample.
4. Click Browse and select the prepared Cell Ranger ARC output folder, not an individual file.
5. Wait for CellPilot to read the paired RNA and ATAC data.

CellPilot requires filtered_feature_bc_matrix.h5 for Multiome loading. It also uses the MatrixMarket barcodes when present, analysis/ for precomputed RNA and ATAC UMAP/clusters, atac_peak_annotation.tsv for gene activity and peak-gene interpretation, and summary.csv to detect the genome build.

3. Review RNA and ATAC views

When the Cell Ranger ARC analysis/ folder is present, CellPilot can load both gene expression and chromatin accessibility embeddings. You can inspect RNA clusters and ATAC clusters side by side, then use chat to ask for cluster information or marker features.

Tell me about RNA cluster 1
Find markers for RNA cluster 1
Tell me about ATAC cluster 3
Find markers for ATAC cluster 3

For ambiguous requests, CellPilot asks whether you mean RNA or ATAC. This keeps the independent cluster labels for each view clear.

4. Plot genes and gene activity

For Multiome data, CellPilot can show RNA expression from the gene expression matrix and ATAC gene activity from the peak matrix and peak annotations. Use familiar marker genes to compare the two modalities.

Plot MS4A1
Violin plot MS4A1
Dotplot MS4A1 CD3D LST1 NKG7
Change color to green black red

The color command changes the active gene or dot plot color scale, so use it after creating a feature plot, violin plot, or dot plot.

5. View coverage tracks

If atac_fragments.tsv.gz and atac_fragments.tsv.gz.tbi are present in the selected folder, CellPilot can query fragment-level accessibility and show a coverage-style view for a gene region.

Coverage plot MS4A1

Coverage plots require fragment files. RNA expression plots, ATAC gene activity plots, and marker tables can still work without fragment coverage.

6. Compare RNA and ATAC clusters

Because RNA and ATAC are measured in the same cells, CellPilot can highlight a cluster from one modality on the other modality's UMAP. This helps identify whether transcriptomic and chromatin-defined states agree.

Highlight RNA cluster 1 on ATAC
Highlight ATAC cluster 3 on RNA

7. Link peaks to genes and run TF analysis

Unlike standalone scATAC-seq analysis, Multiome includes matched RNA expression and chromatin accessibility from the same cells. That makes peak-gene linking and TF analysis appropriate for this workflow.

Link peaks to genes
Show peak-gene links for MS4A1
Run TF motif analysis for cluster 2
Prioritize TF for cluster 2

CellPilot uses linked peaks and marker genes for TF motif analysis. TF prioritization uses RNA clusters because the ranking depends on RNA expression together with peak-gene links.

8. Adjust analysis parameters

CellPilot can rerun RNA or ATAC analysis steps from chat. If a full reanalysis filters cells in one modality, CellPilot keeps the paired RNA and ATAC views aligned to the same cells.

Rerun clustering for RNA with resolution = 1.2
Rerun clustering for ATAC with resolution = 1.2
Rerun RNA UMAP with min dist = 0.4
Rerun ATAC UMAP with min dist = 0.4