ISCVAM - Interactive Single Cell Visual Analytics for Multiomics

About

ISCVAM is a fast, interactive tool for visualizing and investigating single-cell multi-omics data. This repository contains:

1. Web Application - Frontend and backend code for the ISCVAM visualization platform
2. Analysis Pipelines - R pipelines for processing single-cell data into ISCVAM-compatible H5 format

ISCVAM can be accessed at https://chenlab.utah.edu/iscvam/

Contact: ann.chen@hci.utah.edu

---

Analysis Pipelines

We provide two analysis pipelines for preparing data for ISCVAM:

1. scRNA-seq Pipeline

We provide two analysis pipelines for preparing data for ISCVAM:

1. scRNA-seq Pipeline

For single-cell RNA sequencing data only.

• Location: pipelines/sc_rna_pipeline/
• Input: 10x Genomics scRNA-seq data
• Output: H5 file with expression data, clustering, and annotations
• See scRNA-seq Pipeline README for detailed instructions

Example Data:

• Example dadaset can be downloaded directly from TISCH2: https://tisch.compbio.cn/gallery/?cancer=NSCLC&species=
• Place downloaded files in pipelines/sc_rna_pipeline/example_data/ as appropriate.

2. Multiome Pipeline

For 10x Multiome data (scRNA-seq + scATAC-seq).

• Location: pipelines/multiome_pipeline/
• Input: 10x Genomics Multiome data (Cell Ranger ARC output)
• Output: H5 file with RNA, ATAC, and integrated WNN analysis
• See Multiome Pipeline README for detailed instructions

Example Data:

• Example datasets are downloaded from 10x genomics
  • https://www.10xgenomics.com/datasets/frozen-human-healthy-brain-tissue-3-k-1-standard-2-0-0
  • https://www.10xgenomics.com/datasets/human-kidney-cancer-nuclei-isolated-with-chromium-nuclei-isolation-kit-saltyez-protocol-and-10x-complex-tissue-dp-ct-sorted-and-ct-unsorted-1-standard
• Place downloaded files in pipelines/multiome_pipeline/example_data/ as appropriate.

Before running the code:
Please download the example data and results from the provided links and place them in the specified folders.
This ensures the pipelines have the necessary input files and example outputs for testing and demonstration. For single-cell RNA sequencing data only.

• Location: pipelines/sc_rna_pipeline/
• Input: 10x Genomics scRNA-seq data
• Output: H5 file with expression data, clustering, and annotations
• See scRNA-seq Pipeline README for detailed instructions

2. Multiome Pipeline

For 10x Multiome data (scRNA-seq + scATAC-seq).

• Location: pipelines/multiome_pipeline/
• Input: 10x Genomics Multiome data (Cell Ranger ARC output)
• Output: H5 file with RNA, ATAC, and integrated WNN analysis
• See Multiome Pipeline README for detailed instructions

---

Web Application

Requirements

Be sure to have the following technologies installed with the required version:

• Docker
  • With the CLI commands enabled (for running docker and docker-compose)
  • https://docs.docker.com/engine/install/
• If not using Docker, make sure you have:
  • For the backend:
    • Node v16.20.0
    • node hdf5 addon: https://github.com/zhihua-chen/hdf5.node
  • For the frontend:
    • Node v18.16.0

Folder Structure

ISCVAM/
├── backend/                    # Backend server (Node.js)
├── frontend/                   # Frontend web application (React)
├── pipelines/
│   ├── sc_rna_pipeline/        # scRNA-seq analysis pipeline
│   └── multiome_pipeline/      # Multiome (RNA + ATAC) analysis pipeline
├── example_scripts/            # Example processing scripts
│   ├── multiome/
│   └── scRNA_seq/
├── orchestration/
│   └── docker_files/
│       ├── backend/
│       ├── frontend/
│       ├── pipeline/
│       └── compose/            # Docker Compose configuration
│           ├── backend/config/datasets.json
│           ├── frontend/config/app-settings.json
│           ├── datasets/       # Place your .h5 files here
│           └── docker-compose.yml
└── 

---

Example Datasets

We applied ISCVAM to investigate cell populations using multiple multiome datasets and scRNAseq datasets for proof of principle: Example datasets listed in this github:

Dataset	Cells	Description
Human Brain	3,233	10x Genomics healthy brain tissue
Human Kidney Cancer	22,722	10x Genomics kidney cancer nuclei
NSCLC_GSE127471	1108	TISCH2 Lung Cancer

Find more datasets in our website https://chenlab.chpc.utah.edu/iscvam/

References

• Anadon et al. Ovarian cancer immunogenicity is governed by a narrow subset of progenitor tissue-resident memory T cell. Cancer Cell (2022)
• 10x Multiome PBMC
• 10x Multiome Human Brain
• 10x Multiome Human Kidney Cancer