On the public citation graph

scRNA-seq mixology: towards better benchmarking of single cell RNA-seq analysis methods

https://doi.org/10.1101/433102
0 signed · 62 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/bf01908075openalex-snapshot
Comparing partitions
inferredcites →https://doi.org/10.1016/j.cell.2015.04.044openalex-snapshot
Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells
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/10.1016/j.cels.2016.09.002openalex-snapshot
A Single-Cell Transcriptome Atlas of the Human Pancreas
inferredcites →https://doi.org/10.1016/j.cels.2019.03.010openalex-snapshot
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
inferredcites →https://doi.org/10.1038/nbt.2957openalex-snapshot
A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium
inferredcites →https://doi.org/10.1038/nbt.3192openalex-snapshot
Spatial reconstruction of single-cell gene expression data
inferredcites →https://doi.org/10.1038/nbt.4042openalex-snapshot
Multiplexed droplet single-cell RNA-sequencing using natural genetic variation
inferredcites →https://doi.org/10.1038/nbt.4091openalex-snapshot
Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
inferredcites →https://doi.org/10.1038/nbt.4096openalex-snapshot
Integrating single-cell transcriptomic data across different conditions, technologies, and species
inferredcites →https://doi.org/10.1038/ng.3818openalex-snapshot
Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors
inferredcites →https://doi.org/10.1038/nmeth.2645openalex-snapshot
Accounting for technical noise in single-cell RNA-seq experiments
inferredcites →https://doi.org/10.1038/nmeth.2930openalex-snapshot
Validation of noise models for single-cell transcriptomics
inferredcites →https://doi.org/10.1038/nmeth.3971openalex-snapshot
Diffusion pseudotime robustly reconstructs lineage branching
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/nmeth.4263openalex-snapshot
SCnorm: robust normalization of single-cell RNA-seq data
inferredcites →https://doi.org/10.1038/nmeth.4402openalex-snapshot
Reversed graph embedding resolves complex single-cell trajectories
inferredcites →https://doi.org/10.1038/nmeth.4612openalex-snapshot
Bias, robustness and scalability in single-cell differential expression analysis
inferredcites →https://doi.org/10.1038/nmeth.4662openalex-snapshot
FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data
inferredcites →https://doi.org/10.1038/s41586-018-0590-4openalex-snapshot
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
inferredcites →https://doi.org/10.1038/s41587-019-0071-9openalex-snapshot
A comparison of single-cell trajectory inference methods
inferredcites →https://doi.org/10.1038/s41587-019-0113-3openalex-snapshot
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
inferredcites →https://doi.org/10.1038/s41592-018-0254-1openalex-snapshot
A test metric for assessing single-cell RNA-seq batch correction
inferredcites →https://doi.org/10.1038/srep39921openalex-snapshot
Batch effects and the effective design of single-cell gene expression studies
inferredcites →https://doi.org/10.1073/pnas.1820006116openalex-snapshot
scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets
inferredcites →https://doi.org/10.1093/bib/bby011openalex-snapshot
Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data
inferredcites →https://doi.org/10.1093/bioinformatics/btg410openalex-snapshot
A benchmark for Affymetrix GeneChip expression measures
inferredcites →https://doi.org/10.1093/bioinformatics/btp616openalex-snapshot
edgeR : a Bioconductor package for differential expression analysis of digital gene expression data
inferredcites →https://doi.org/10.1093/gigascience/giy083openalex-snapshot
Clustering trees: a visualization for evaluating clusterings at multiple resolutions
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/10.1093/nar/gkw1046openalex-snapshot
Division of labor among Mycobacterium smegmatis RNase H enzymes: RNase H1 activity of RnhA or RnhC is essential for growth whereas RnhB and RnhA guard against killing by hydrogen peroxide in stationary phase
inferredcites →https://doi.org/10.1093/nar/gkw430openalex-snapshot
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis
inferredcites →https://doi.org/10.1093/nar/gkx828openalex-snapshot
Linnorm: improved statistical analysis for single cell RNA-seq expression data
inferredcites →https://doi.org/10.1101/138677openalex-snapshot
SAVER: Gene expression recovery for UMI-based single cell RNA sequencing
inferredcites →https://doi.org/10.1101/175927openalex-snapshot
scPipe: a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data
inferredcites →https://doi.org/10.1101/217737openalex-snapshot
K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data
inferredcites →https://doi.org/10.1101/gr.121095.111openalex-snapshot
Synthetic spike-in standards for RNA-seq experiments
inferredcites →https://doi.org/10.1186/gb-2010-11-3-r25openalex-snapshot
A scaling normalization method for differential expression analysis of RNA-seq data
inferredcites →https://doi.org/10.1186/s12859-017-1553-8openalex-snapshot
MINT: a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms
inferredcites →https://doi.org/10.1186/s12859-018-2226-yopenalex-snapshot
DrImpute: imputing dropout events in single cell RNA sequencing data
inferredcites →https://doi.org/10.1186/s12864-018-4772-0openalex-snapshot
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
inferredcites →https://doi.org/10.1186/s13059-014-0550-8openalex-snapshot
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
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/s13059-016-0947-7openalex-snapshot
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
inferredcites →https://doi.org/10.1186/s13059-016-0975-3openalex-snapshot
SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data
inferredcites →https://doi.org/10.12688/f1000research.15666.1openalex-snapshot
A systematic performance evaluation of clustering methods for single-cell RNA-seq data
inferredcites →https://doi.org/10.12688/f1000research.15809.1openalex-snapshot
Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data
inferredcites →https://doi.org/10.12688/f1000research.16613.2openalex-snapshot
False signals induced by single-cell imputation
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.1005752openalex-snapshot
mixOmics: An R package for ‘omics feature selection and multiple data integration

What points at this work

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

inferred← citeshttps://doi.org/10.1038/s41587-019-0071-9openalex-snapshot
A comparison of single-cell trajectory inference methods
inferred← citeshttps://doi.org/10.1038/s41592-019-0529-1openalex-snapshot
Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling
inferred← citeshttps://doi.org/10.1038/s41592-019-0654-xopenalex-snapshot
Orchestrating single-cell analysis with Bioconductor
inferred← citeshttps://doi.org/10.1073/pnas.1820006116openalex-snapshot
scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets
inferred← citeshttps://doi.org/10.1101/2022.04.26.489628openalex-snapshot
Projecting clumped transcriptomes onto single cell atlases to achieve single cell resolution
inferred← citeshttps://doi.org/10.1186/s13059-019-1766-4openalex-snapshot
scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
inferred← citeshttps://doi.org/10.1186/s13059-020-02096-yopenalex-snapshot
Demystifying “drop-outs” in single-cell UMI data
inferred← citeshttps://doi.org/10.1186/s13059-020-02136-7openalex-snapshot
pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools
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/10.12688/f1000research.73600.1openalex-snapshot
Doublet identification in single-cell sequencing data using scDblFinder
inferred← citeshttps://doi.org/10.12688/f1000research.73600.2openalex-snapshot
Doublet identification in single-cell sequencing data using scDblFinder
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.