Table 3.
Top 20 cited tools for analysing scRNA-seq data
| Tool name | System | Output | Tool overview | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| Platform | Input data | Quality control | Normalization | Integration | Clustering | Classification | Ordering | Diff. expression | Gene networks | Dim. reduction | Visuali-zation | |||
| STAR | C/C＋＋ | FASTQ | An ultrafast universal RNA-seq aligner designed to align RNA sequencing reads to a reference genome (63) | |||||||||||
| Seurat | R | Count Matrix | ∨ | ∨ | ∨ | ∨ | ∨ | ∨ | A toolkit for quality control, analysis, and exploration of scRNA-seq data (64) | |||||
| Monocle | R | FASTQ | ∨ | ∨ | ∨ | ∨ | ∨ | A toolkit for analysing single-cell gene expression to discover, explore, and visualize cell differentiation processes (65) | ||||||
| kallisto | C/C＋＋ | FASTQ | A program for quantifying abundances of transcripts from RNA-seq data, using pseudoalignment to speed up the process (66) | |||||||||||
| salmon | C＋＋ | FASTQ | A tool for fast transcript-level quantification from RNA-seq data using lightweight alignments (67) | |||||||||||
| CellRanger | Python/R | FASTQ | ∨ | ∨ | ∨ | ∨ | ∨ | A set of analysis pipelines that process Chromium scRNA-seq output to align reads, generate feature-barcode matrices, and perform clustering and gene expression analysis (58) | ||||||
| Scanpy | Python | Count Matrix | ∨ | ∨ | ∨ | ∨ | ∨ | ∨ | ∨ | An open-source, scalable toolkit for analysing single-cell gene expression data using Python (68) | ||||
| inferCNV | R | FASTQ | ∨ | Uses to investigate tumor scRNA-seq data to recognise evidence for large-scale chromosomal copy number variations (69) | ||||||||||
| CellPhoneDB | Python | Count Matrix | ∨ | ∨ | A publicly available repository of curated receptors, ligands, and their interactions, intended for analysing cell-cell communication (70) | |||||||||
| BackSPIN | Python | FASTQ | ∨ | A gene clustering and ordering algorithm based on a biclustering technique, used for single-cell data analysis (71) | ||||||||||
| SCENIC | Python/R | FASTQ | ∨ | ∨ | ∨ | A computational method for finding regulators and their target genes from scRNA-seq data to reconstruct gene regulatory networks (72) | ||||||||
| AUCell | R | FASTQ | ∨ | ∨ | A tool for analysing gene sets in single-cell data, identifying cells with active gene sets (73) | |||||||||
| velocyto | Python/R | FASTQ | ∨ | ∨ | A package for estimating RNA velocity in scRNA-seq data, predicting the future state of individual cells (74) | |||||||||
| scran | R | Count Matrix | ∨ | ∨ | ∨ | Implements methods for low-level analyses of scRNA-seq data such as normalization and cell cycle phase assignment (75) | ||||||||
| Harmony | R/C＋＋ | FASTQ | ∨ | ∨ | An algorithm for integrating scRNA-seq data across different datasets or experimental conditions (76) | |||||||||
| MAST | R | Count Matrix | ∨ | ∨ | ∨ | ∨ | A flexible statistical framework to assess differential expression in scRNA-seq data (77) | |||||||
| RaceID | R/C＋＋ | Count Matrix | ∨ | ∨ | ∨ | ∨ | ∨ | ∨ | Identifies rare cell types from single-cell gene expression data based on clustering (78) | |||||
| scvi-tools | Python | FASTQ | ∨ | ∨ | ∨ | ∨ | ∨ | ∨ | A suite of methods for analysing single-cell genomics data, leveraging variational inference to model cell heterogeneity and dependencies (79) | |||||
| SCDE | R | Count Matrix | ∨ | ∨ | An error model and differential expression analysis for scRNA-seq data, accounting for the unique characteristics of sparse and noisy data (80) | |||||||||
Diff.: differential, Dim.: dimension, scRNA-seq: single-cell RNA sequencing.