Single Cell Multiomics


Single cell multiomics is a next-generation sequencing (NGS) application that performs RNA-seq and ATAC-seq on the same cell simultaneously to reveal insights into the epigenetic regulation of gene expression.

General Workflow

A typical single cell multiomics experimental workflow involves the isolation of single intact cells. After the cells are isolated, they are permeabilized to allow entry of Tn5 transposase enzyme, which tagments open chromatin regions. The cells are then lysed and mRNA is captured with oligo dT magnetic beads. After magnetic separation, the tagmented genomic DNA is purified, and PCR extension and amplification is performed to create an NGS library. The mRNA captured on oligo dT beads is reverse transcribed and the subsequent mRNA/cDNA hybrids are directly tagmented by Tn5. After initial end extension, the tagmented cDNA is amplified with indexed PCR to prepare the mRNA-seq library.

Data Analysis

SciDAP is a no-code bioinformatics platform that enables biologists to analyze NGS-based data without a bioinformatician. It has a collection of 18 built-in pipelines based on open-source workflows to analyze data from single cell multiomic libraries. The set includes pipelines for initial processing of scRNA-Seq and scATAC-Seq data, aggregation of multiple samples and filtering out bad cells, clustering and differential expression, and abundance estimation.

SciDAP starts from the FASTQ files provided by most DNA core facilities and commercial service providers. Starting from raw data allows SciDAP to ensure that all experiments have been processed in the same way and simplifies the deposition of data to GEO upon publication. The data can be uploaded from the end-users’ computer, downloaded directly from an FTP server of the core facility by providing a URL, or from GEO by providing SRA accession number. The processing results can be visualized on the UCSC cell browser.

Steps in the SciDAP Single Cell Multiomics workflow


Start with raw data

SciDAP can start with fastq files or NCBI accession numbers for published data
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Quantitation of Gene Expression
CellRanger ARC output

Aggregate multiple experiments

Aggregate experiment count data
scMultiome CellRanger ARCAggr output

Filter out bad cells

Filter out bad cells
scRNA-Seq filtering

Dimensionality reduction and integration for both GEX and ATAC data

Integrate multiple samples with scTriangulate or Harmony
scMultiome Integration


Browse Seurat clustering for GEX data, Signac clustering for ATAC data or WNN clustering combining both data types
scRNA clustering with Seurat

Differential expression and accessibility analysis

Differential expression or accessibility analysis for subsets of cells or between conditions using cell-based or pseudobulk approach
Differential binding or accessibility analysis with Seurat or DESeq2 for pseudobulks

Easy-to-use, no-code bioinformatics software for NGS data analysis