Single Cell RNA-Seq


Single cell RNA-Seq is a next-generation sequencing (NGS) application that allows analysis and comparison of the transcriptomes of individual cells. Analyzing the heterogeneity of cells can identify rare cell populations and trace lineage of development.

General Workflow

A typical single cell RNA-Seq experimental workflow involves the isolation of single intact cells or nuclei. Polyadenylated mRNA is captured using oligo dT primers, converted to cDNA, ligated with NGS platform adaptors, amplified by indexed PCR, pooled, and sequenced by NGS.

Data Analysis

SciDAP is a no-code bioinformatics platform that enables biologists to analyze NGS-based data without a bioinformatician. It has built-in pipelines based on open-source workflows, specific to analyzing gene expression at the single-cell level.

Starting with FASTQ files, analysis of single cell RNA-Seq data begins with raw data quality control (QC) and read trimming followed by alignment of reads against a reference transcriptome. Specific algorithms are applied for downstream analysis such as expression estimation, and preliminary clustering. Multiple samples can be integrated and normalized with Seurat's SCTransform or Harmony. Filtering out bad cells, dimensionality evaluation and clustering is performed with Seurat. Analysis ends with summarization and visualization of results.

With SciDAP, you can easily identify cell types, compare expression between clusters or conditions and generate publication quality images in just a few hours.

Steps in the SciDAP Single Cell RNA-Seq workflow


Start with raw data

SciDAP can start with fastq files or NCBI accession numbers for published data
fastq files


Quantitation of Gene Expression
CellRanger output

Experiment Data

Aggregate experiment count data
CellRanger Aggr output

Filter out bad cells

Filter out bad cells
scRNA-Seq filtering

Data integration and dimensioanlity reduction

Samples can be integrated with scTriangulate or Harmony
scRNA-Seq data integration with scTriangulate or Harmony

Clustering with Seurat

Browse clusters with UCSC cell browser and identify cell types based on gene expression
scRNA clustering with Seurat

Expression analysis

Differential expression analysis for a subset of cells between conditions
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Easy-to-use, no-code bioinformatics software for NGS data analysis