PMC7391988		Data format conversion We developed the R package sceasy for converting data formats frequently used in scRNA-seq analysis, namely Seurat object, SingleCellExperiment (SCE) object, Loom object, to AnnData object.
PMC7391988		It also supports conversion from Seurat object to SCE object, and between SCE and Loom objects.
PMC7391988		The package complements existing conversion functions such as those in Seurat and scran.
PMC7391988		The rows marked Loom, h5ad, SCE (SingleCellExperiment), Seurat, csv / txt indicate the different standard input data formats that are in use.
PMC7391988		R-based SingleCellExperiment (SCE) and Seurat are accepted by one and three tools, respectively.
PMC7079336	CNV_calling	ScDNA-seq data processing and CNV calling Sequencing data were processed with the Cellranger-DNA pipeline, which automates sample demultiplexing, read alignment, CNV calling and report generation.
PMC7079336	CNV_calling	Cellranger-DNA output includes copy number calls for each cell.
PMC7079336		Cellranger-DNA is freely available at https : //support.10xgenomics.com / single-cell-gene-expression / software / pipelines / latest / algorithms / overview and details of the pipeline are described in Supplementary Methods.
PMC7079336	alignment	ScRNA-seq data processing Cellranger software suite 1.2.1 was used to process scRNA data, including sample demultiplexing, barcode processing and single cell 3′ gene counting.
PMC7079336	quantification	Cellranger provided a gene-by-cell matrix, containing the read count distribution of each gene for each cell.
PMC7079336	differential_expression	Intra-cell line differential gene expression We used Seurat version 2.3.4 to identify differentially expressed genes between members of a given clone and members of any other clone detected in the same cell line.
PMC7079336	marker_genes	For each comparison we used the Wilcox rank-sum test implemented in Seurat (function ' FindMarkers '), while accounting for variability in gene coverage across cells.
PMC7775989	alignment	Acquisition of single‐cell gene expression matrices and cell clustering Raw reads were cleaned using Cutadapt (Version 1.15) and mapped to hg38 using STAR (Version 020201).
PMC7775989	clustering	Cell clustering was accomplished by the Seurat R package (Version 3.0.1).
PMC7775989	normalization,variable_genes	TPM matrices from each patient were log‐normalized, and 2,000 variably expressed genes were selected using Seurat 's FindVariableFeatures function.
PMC7775989	integration	To remove batch effects, TPM matrices of different patients were integrated by Anchors, using the FindIntegrationAnchors and the IntegrateData functions from the Seurat R package.
PMC7775989	marker_genes	To better identify T‐cell and B‐cell subtypes, we extracted T cells and B cells and ran Seurat, respectively.
PMC7775989	alignment	The filtered reads were aligned to hg38 genome using STAR and sorted by sambamba (Version 441 0.7.0).
PMC7775989	marker_genes	Identifying marker genes We used the Seurat 's FindAllMarkers function to identify marker genes for each cluster.
PMC7775989	normalization	We first used the “ relative2abs ” function in Monocle to convert TPM into normalized mRNA counts and created an object with parameter “ expressionFamily = negbinomial.size ” following the Monocle2 tutorial.
PMC7902236	alignment,quality_control	Getting output of simulated differential analysis and changes in parameters Simulated RNA-seq reads were aligned to the hg38 genome with the STAR aligner (42) with default paired-end sequencing parameters before being filtered for ENCODE blacklist regions with bedtools (43).
PMC7902236	marker_genes	We use the fold-change (non-log) output in the ‘ FindMarkers ’ function within Seurat V3 (49,50) to populate these signature matrices.
PMC7902236	normalization,clustering,integration	Then, normalization, clustering, scaling and integration of technical replicates were completed using Seurat V3 with the integration anchors feature (49,50).
PMC7902236	marker_genes	Cell-type markers are identified using the ‘ FindMarkers ’ function in Seurat v3 (default parameters) (49,50).
PMC7902236		Users can optionally provide their own scRNA-seq count matrix, which is converted into a Seurat (50) object that is then processed and converted into a signature matrix using the same methods described above.
PMC7902236	marker_genes	Specifically, scMappR saves the Seurat object, all cell-type markers, and all possible cell-type labels from both CellMarker and Panglao (using GSVA and the Fisher ’ s exact test) (17,18,49,50,53–55).
PMC7902236		Finally, the vignette stored in CRAN provides the functions required to convert a Seurat object into a signature matrix.
PMC7902236	alignment	Samples were aligned to the hg38 genome with the STAR aligner (42) using default parameters for paired-end sequencing and filtered for blacklist regions.
PMC7902236	alignment	These RNA-seq bulk kidney samples were aligned to the mm10 genome with the STAR aligner (42) using default parameters for paired-end sequencing and filtered for blacklist regions.
PMC7902236	normalization	This function provides more flexibility than using Seurat and the Wilcoxon test exclusively such as choosing different parameters in scRNA-seq normalization (e.g.
PMC7902236	marker_genes	Seurat or scTransform) (49,50,58) and cell-type marker techniques (e.g.
PMC7902236	differential_expression	scRNA-seq processing with Seurat V3 versus scTransform inherently influenced how scMappR would calculate cwFold-changes because they identified a different number of clusters (i.e.
PMC6956120		Preprocessing for 10× Genomics single-cell RNA-seq data Seurat v2.1 (http : //satijalab.org / seurat/) was used to analyze the 10× Genomics data (23).
PMC8168129	alignment	For the expression profile generated with the next-generation RNA-seq platform, we aligned RNA-seq reads in BAM format using a two-pass method with STAR.
PMC8168129	clustering,marker_genes,differential_expression	For single cell RNA-seq, we following the Seurat Clustering Tutorial (https : //satijalab.org / seurat) to cluster the cells and find cell markers that define clusters via differential expression (22).
PMC8383063	quality_control	Single cell sequencing R package Seurat v3.1.2 was used to process the single-cell data expression matrix.
PMC7334875	alignment	Raw sequence data were first de-multiplexed from BCL files into FASTQ files by using “ cellranger mkfastq ”, with 10X Cell Ranger software.
PMC7334875	alignment	This 67 gRNA extended hg38 was indexed by “ cellranger mkref ” with extension “ .fa ” and “ .gtf ” files as input.
PMC7334875	quantification,alignment	Single-cell gene counts were generated by “ cellranger count ” by aligning reads to the extended hg38 by STAR aligner [ 17 ] with default settings.
PMC7334875	quantification	Single-cell RNA sequencing and data processing The count of unique molecular identifiers (UMIs) for each gRNA per cell was quantified by “ cellranger count.
PMC7334875	quality_control,normalization	Expression data quality control and normalization The R package Seurat V3.0 [ 18 ] downloaded from https : //github.com / satijalab / seurat was used for scRNAseq expression data processing and analysis.
PMC7334875	dimensionality_reduction	Linear dimensional reduction principal component analysis (PCA) was first performed with the top 2000 identified highly variable genes and default settings in Seurat [ 18 ].
PMC7334875	dimensionality_reduction	The “ JackStraw ” function implemented in Seurat was used to determine the significant PCs.
PMC7334875	classification	Each cell was assigned a score summarizing expression of G2 / M and S phase gene markers implemented in Seurat package, and thereby classified into either G2 M, S, or G1 phase according to its cell cycle score.
PMC7334875	dimensionality_reduction	(A) Nonlinear dimensional reduction of 20PCs of single-cell transcriptome profiles by UMAP in Seurat (18).
