scPipe

scPipe performs preprocessing of single-cell RNA-seq and ATAC-seq data and constructs SingleCellExperiment objects for downstream single-cell multi-omics analysis.

Key Features:

• Platform Independence: Supports sequencing protocols including CEL-seq, MARS-seq, Drop-seq, Chromium 10x, and SMART-seq for RNA-seq data.
• Data Cleaning and Quality Control: Removes duplicated reads, low-abundance features, and cells of poor quality and produces a sparse count matrix with features in rows and cells in columns.
• Quality Metrics and Annotations: Stores counts per cell, features per cell, total number of fragments, fraction of fragments per peak, and relevant feature annotations as metadata.
• Fine-tunable QC Thresholds: Quality control thresholds can be adjusted using feature- and cell-based metrics collected during preprocessing.
• Integration with Bioconductor Tools: Outputs are compatible with downstream Bioconductor analyses including dimensionality reduction, clustering, motif enrichment, differential accessibility, and cis-regulatory network analysis.

Scientific Applications:

• Cell identification: Identifies 'true' cells from raw sequencing data to ensure accurate downstream single-cell analyses.
• Integrative genomics: Enables integrative analysis of gene regulation and chromatin accessibility at the single-cell level using RNA-seq and ATAC-seq data.

Methodology:

Processes raw fastq files through stages of quality control and data cleaning (including removal of duplicated reads, low-abundance features, and poor-quality cells) and outputs a SingleCellExperiment object containing a sparse count matrix; integrates RNA-seq and ATAC-seq data when applicable.