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scPipe: a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data

https://doi.org/10.1101/175927
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-1-4614-6868-4openalex-snapshot
Seamless R and C++ Integration with Rcpp
inferredcites →https://doi.org/10.1016/j.cell.2015.05.002openalex-snapshot
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
inferredcites →https://doi.org/10.1038/nbt.2859openalex-snapshot
The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
inferredcites →https://doi.org/10.1038/ncomms14049openalex-snapshot
Massively parallel digital transcriptional profiling of single cells
inferredcites →https://doi.org/10.1038/nmeth.3252openalex-snapshot
Orchestrating high-throughput genomic analysis with Bioconductor
inferredcites →https://doi.org/10.1038/nmeth.4220openalex-snapshot
Power analysis of single-cell RNA-sequencing experiments
inferredcites →https://doi.org/10.1038/nmeth.4236openalex-snapshot
SC3: consensus clustering of single-cell RNA-seq data
inferredcites →https://doi.org/10.1038/s41467-017-02554-5openalex-snapshot
A general and flexible method for signal extraction from single-cell RNA-seq data
inferredcites →https://doi.org/10.1093/bioinformatics/bts635openalex-snapshot
STAR: ultrafast universal RNA-seq aligner
inferredcites →https://doi.org/10.1093/bioinformatics/btt656openalex-snapshot
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
inferredcites →https://doi.org/10.1093/bioinformatics/btw777openalex-snapshot
Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R
inferredcites →https://doi.org/10.1093/nar/gkt214openalex-snapshot
The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote
inferredcites →https://doi.org/10.1101/gr.209601.116openalex-snapshot
UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy
inferredcites →https://doi.org/10.1126/science.1254257openalex-snapshot
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
inferredcites →https://doi.org/10.1186/s13059-016-0938-8openalex-snapshot
CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq
inferredcites →https://doi.org/10.1186/s13059-018-1449-6openalex-snapshot
dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments
inferredcites →https://doi.org/10.12688/f1000research.9501.2openalex-snapshot
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
inferredcites →https://doi.org/10.1371/journal.pcbi.1004333openalex-snapshot
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
inferredcites →https://doi.org/10.1371/journal.pcbi.1006245openalex-snapshot
Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database
inferredcites →https://doi.org/10.18637/jss.v040.i13openalex-snapshot
topicmodels : An R Package for Fitting Topic Models
inferredcites →https://doi.org/10.32614/rj-2016-021openalex-snapshot
mclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.coisb.2017.12.007openalex-snapshot
Methods and challenges in the analysis of single-cell RNA-sequencing data
inferred← citeshttps://doi.org/10.1093/gigascience/giy059openalex-snapshot
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs
inferred← citeshttps://doi.org/10.1093/gigascience/giz087openalex-snapshot
ascend : R package for analysis of single-cell RNA-seq data
inferred← citeshttps://doi.org/10.1101/250811openalex-snapshot
Sharq, A versatile preprocessing and QC pipeline for Single Cell RNA-seq
inferred← citeshttps://doi.org/10.1101/433102openalex-snapshot
scRNA-seq mixology: towards better benchmarking of single cell RNA-seq analysis methods
inferred← citeshttps://doi.org/10.1186/s12859-019-2797-2openalex-snapshot
scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data
inferred← citeshttps://doi.org/10.12688/f1000research.15809.1openalex-snapshot
Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1006245openalex-snapshot
Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database
inferred← citeshttps://doi.org/10.4018/979-8-3373-4849-0.ch001openalex-snapshot
Spatial and Single-Cell Multi-Omics for Precision Medicine
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