Table 1.

List of packages or tools recommended by the authors

Package or tool	First author	Year	Language	Title or explanation	Ref
Step 1. Obtain FASTQ files from public database
fasterq-dump	The SRA Toolkit Development Team	2023	C	extracting data in FASTQ- or FASTA-format from SRA-accessions	[16]
parallel-fastq-dump	Valieris	2021	Python	Speed up the process by dividing the work into multiple threads	[17]
Step 2. Quality check and mapping to the reference genome
Cell Ranger	Zheng	2017		Massively parallel digital transcriptional profiling of single cells	[20]
STARsolo	Kaminow	2021	C	STARsolo: accurate, fast, and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data	[21]
Step 3. Preparation environment for the in silico analysis
R	R Core Team	2023		R: A Language and Environment for Statistical Computing	[22]
Tidyverse	Wickham	2019	R	Welcome to the Tidyverse	[23]
ggplot2	Wickham		R	Elegant Graphics for Data Analysis	[24]
Python3	Van Rossum	2009		Python 3 Reference Manual	[25]
Matplotlib	Hunter	2007	Python	Matplotlib: A 2D graphics environment	[26]
seaborn	Waskom	2021	Python	seaborn: statistical data visualization	[27]
Step 4. Preprocess of datasets
Seurat 4	Hao	2021	R	Integrated analysis of multimodal single-cell data	[28]
Seurat 5	Hao	2022	R	Dictionary learning for integrative, multimodal, and scalable single-cell analysis	[29••]
sctransform	Hafemeister	2019	R	Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression	[30]
Scanpy	Wolf	2018	Python	SCANPY: large-scale single-cell gene expression data analysis	[31]
scverse	Virshup	2023	Python	The scverse project provides a computational ecosystem for single-cell omics data analysis	[32]
Step 5. Dataset integration
Seurat 5	Hao	2022	R		[29••]
scvi-tools	Gayoso	2022	Python	A Python library for probabilistic analysis of single-cell omics data	[34•]
benchmark study	Luecken	2022		Benchmarking atlas-level data integration in single-cell genomics	[33•]
Step 6. Unbiased clustering
t-SNE	van der Maaten	2008		Visualizing Data using t-SNE	[35]
UMAP	Leland McInnes	2020		UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction	[36]
Step 7. Functional annotation
AUCell	Aibar	2016	R, Python	AUCell: Analysis of “gene set” activity in single-cell RNA-seq data	[38]
SCENIC	Aibar	2017	R	SCENIC: single-cell regulatory network inference and clustering	[39]
pySCENIC	Van de Sande	2020	Python	A scalable SCENIC workflow for single-cell gene regulatory network analysis	[39, 40]
decoupleR	Badia	2022	R, Python	decoupleR: ensemble of computational methods to infer biological activities from omics data	[41•]
CellAssign	Zhang	2019	Python	Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling	[42]
Nitchenet	Browaeys	2020	R	NicheNet: modeling intercellular communication by linking ligands to target genes	[44]
Omnipath	Turei	2021	R, Python	Integrated intra- and intercellular signaling knowledge for multicellular omics analysis	[45]
scTensor	Tsuyuzaki	2019	R	Uncovering hypergraphs of cell–cell interaction from single cell RNA-sequencing data	[46]
Cellular interaction review	Armingol	2021		Deciphering cell–cell interactions and communication from gene expression	[43]
Step 8. Trajectory analysis
Velocyto	La Manno	2018	Python, R	RNA velocity of single cells	[47]
scVelo	Bergen	2020	Python	Generalizing RNA velocity to transient cell states through dynamical modeling	[48•]
Dynamo	Qiu	2022	Python	Mapping transcriptomic vector fields of single cells	[49]
Monocle 3	Trapnell	2014	R	The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells	[50]
PAGA	Wolf	2019	Python	graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells	[51]
benchmark study	Saelens	2019		A comparison of single-cell trajectory inference methods	[52•]