Normalization for sampled count data

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works

Fast unfolding of communities in large networks

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2008/10/p10008 · Cited by 21,424 · see citing works

SCANPY: large-scale single-cell gene expression data analysis

Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · see citing works

A scaling normalization method for differential expression analysis of RNA-seq data

Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876 · see citing works

From Louvain to Leiden: guaranteeing well-connected communities

Traag, V. A., Waltman, L., & van Eck, N. J. (2019) · Scientific Reports · doi:10.1038/s41598-019-41695-z · Cited by 5,635 · see citing works

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01767-y · Cited by 5,631 · see citing works

The single-cell transcriptional landscape of mammalian organogenesis

Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490 · see citing works

Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression

Hafemeister, C., & Satija, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1874-1 · Cited by 4,479 · see citing works

On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known

Deming, W. E., & Stephan, F. F. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731829 · Cited by 1,537 · see citing works

The transformation of poisson, binomial and negative-binomial data

ANSCOMBE, F. J. (1948) · Biometrika · doi:10.1093/biomet/35.3-4.246 · Cited by 1,300 · see citing works

Differential expression analysis for sequence count dataPreprint

Anders, S., & Huber, W. K. (2010) · Nature Precedings · doi:10.1038/npre.2010.4282.2 · Cited by 894 · see citing works

An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics

Angelidis, I., Simon, L. M., Fernandez, I. E., Strunz, M., Mayr, C. H., Greiffo, F. R., Tsitsiridis, G., Ansari, M., Graf, E., Strom, T.-M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08831-9 · Cited by 637 · see citing works

Comparison and evaluation of statistical error models for scRNA-seq

Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634 · see citing works

The Elimination form of the Inverse and its Application to Linear Programming

Markowitz, H. M. (1957) · Management Science · doi:10.1287/mnsc.3.3.255 · Cited by 515 · see citing works

muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515 · see citing works

Modular, efficient and constant-memory single-cell RNA-seq preprocessing

Melsted, P., Booeshaghi, A. S., Liu, L., Gao, F., Moses, L., Min, K. H., Beltrame, E. D. V., Hjörleifsson, K. E., Gehring, J., & Pachter, L. S. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00870-2 · Cited by 478 · see citing works

Normalizing single-cell RNA sequencing data: challenges and opportunities

Vallejos, C. A., Risso, D., Scialdone, A., Dudoit, S., & Marioni, J. C. (2017) · Nature Methods · doi:10.1038/nmeth.4292 · Cited by 436 · see citing works

The Specious Art of Single-Cell Genomics

Chari, T., & Pachter, L. (2021) · doi:10.1101/2021.08.25.457696 · Cited by 379 · see citing works

Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions

Evans, C., Hardin, J., & Stoebel, D. M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx008 · Cited by 368 · see citing works

Droplet scRNA-seq is not zero-inflated

Svensson, V. (2019) · doi:10.1101/582064 · Cited by 356 · see citing works

The Square Root Transformation in Analysis of Variance

Bartlett, M. S. (1936) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.2307/2983678 · Cited by 349 · see citing works

Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments

Tian, L., Dong, X., Freytag, S., Cao, K. L., Su, S., JalalAbadi, A., Amann‐Zalcenstein, D., Weber, T. S., Seidi, A., Jabbari, J. S., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0425-8 · Cited by 342 · see citing works

Solving the chemical master equation for monomolecular reaction systems analytically

Jahnke, T., & Huisinga, W. (2006) · Journal of Mathematical Biology · doi:10.1007/s00285-006-0034-x · Cited by 314 · see citing works

Dictionary learning for integrative, multimodal, and scalable single-cell analysisPreprint

Hao, Y., Stuart, T., Kowalski, M. H., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.24.481684 · Cited by 283 · see citing works

SCnorm: robust normalization of single-cell RNA-seq data

Bacher, R., Chu, L.-F., Leng, N., Gasch, A. P., Thomson, J. A., Stewart, R. M., Newton, M., & Kendziorski, C. (2017) · Nature Methods · doi:10.1038/nmeth.4263 · Cited by 282 · see citing works

Ten quick tips for effective dimensionality reduction

Nguyen, L. H., & Holmes, S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006907 · Cited by 269 · see citing works

Analytic Pearson residuals for normalization of single-cell RNA-seq UMI data

Lause, J., Berens, P., & Kobak, D. (2021) · Genome Biology · doi:10.1186/s13059-021-02451-7 · Cited by 195 · see citing works

Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq

Cole, M. B., Risso, D., Wagner, A., DeTomaso, D., Ngai, J., Purdom, E., Dudoit, S., & Yosef, N. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.010 · Cited by 173 · see citing works

Comparison of Transformations for Single-Cell RNA-Seq Data

Ahlmann-Eltze, C., & Huber, W. (2021) · doi:10.1101/2021.06.24.449781 · Cited by 148 · see citing works

A multicenter study benchmarking single-cell RNA sequencing technologies using reference samples

Chen, W., Zhao, Y., Chen, X., Yang, Z., Xu, X., Bi, Y., Chen, V., Li, J., Choi, H., Ernest, B., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00748-9 · Cited by 114 · see citing works

The Limiting Distributions of Certain Statistics

Doob, J. L. (1935) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177732594 · Cited by 109 · see citing works

Optimal marker gene selection for cell type discrimination in single cell analyses

Dumitrascu, B., Villar, S., Mixon, D. G., & Engelhardt, B. E. (2021) · Nature Communications · doi:10.1038/s41467-021-21453-4 · Cited by 105 · see citing works

Normalization Methods on Single-Cell RNA-seq Data: An Empirical Survey

Lytal, N., Ran, D., & An, L. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00041 · Cited by 89 · see citing works

Bayesian inference of gene expression states from single-cell RNA-seq data

Breda, J., Zavolan, M., & van Nimwegen, E. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00875-x · Cited by 87 · see citing works

Isoform cell-type specificity in the mouse primary motor cortex

Booeshaghi, A. S., Yao, Z., van Velthoven, C. T. J., Smith, K. A., Tasic, B., Zeng, H., & Pachter, L. S. (2021) · Nature · doi:10.1038/s41586-021-03969-3 · Cited by 81 · see citing works

Normalization of single-cell RNA-seq counts by log( x + 1) or log(1 + x )

Booeshaghi, A. S., & Pachter, L. S. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab085 · Cited by 78 · see citing works

Benchmarking UMI-based single-cell RNA-seq preprocessing workflows

You, Y., Tian, L., Su, S., Dong, X., Jabbari, J. S., Hickey, P. F., & Ritchie, M. E. (2021) · Genome biology · doi:10.1186/s13059-021-02552-3 · Cited by 72 · see citing works

Why You Cannot Transform Your Way out of Trouble for Small Counts

Warton, D. I. (2017) · Biometrics · doi:10.1111/biom.12728 · Cited by 57 · see citing works

Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing dataPreprint

Lun, A. T. L. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/404962 · Cited by 53 · see citing works

A mechanistic model for the negative binomial distribution of single-cell mRNA countsPreprint

Amrhein, L., Harsha, K., & Fuchs, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/657619 · Cited by 29 · see citing works

Length biases in single-cell RNA sequencing of pre-mRNA

Gorin, G., & Pachter, L. S. (2022) · Biophysical Reports · doi:10.1016/j.bpr.2022.100097 · Cited by 28 · see citing works

Single-cell transcriptomics from human pancreatic islets: sample preparation matters

Bonnycastle, L. L., Gildea, D. E., Yan, T., Narisu, N., Swift, A. J., Wolfsberg, T. G., Erdos, M. R., & Collins, F. S. (2019) · Biology Methods and Protocols · doi:10.1093/biomethods/bpz019 · Cited by 27 · see citing works

PsiNorm: a scalable normalization for single-cell RNA-seq data

Borella, M., Martello, G., Risso, D., & Romualdi, C. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab641 · Cited by 19 · see citing works

Normalization by distributional resampling of high throughput single-cell RNA-sequencing data

Brown, J., Ni, Z., Mohanty, C., Bacher, R. L., & Kendziorski, C. M. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab450 · Cited by 17 · see citing works

An efficient form of inverse for sparse matrices

Orchard-Eays, W. (1956) · ACM National Meeting · doi:10.1145/800258.808973 · Cited by 1 · see citing works

Best practices for single-cell analysis across modalities

Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works

Comparison of Transformations for Single-Cell RNA-Seq Data

Ahlmann-Eltze, C., & Huber, W. (2021) · doi:10.1101/2021.06.24.449781 · Cited by 148 · see citing works

A Novel, Improved, Application for the Normalization of RNA-seq Expression Data in Complex PolyploidsPreprint

Evans, D. L. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.22541/au.162740437.78291655/v1

Data Normalization

Priddy, K. L., & Keller, P. E. (2009) · SPIE eBooks · doi:10.1117/3.633187.ch3

NORMA-Gene: A simple and robust method for qPCR normalization based on target gene data

Heckmann, L., Sørensen, P. B., Krogh, P. H., & Sørensen, J. G. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-250 · Cited by 151 · see citing works

Normalization of Biological Expression Data Based on Selection of a Stable Element Set

Bouki, Y., Yoshihiro, T., Inoue, E., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23854-3_17

Normalization and Selecting Non-Differentially Expressed Genes Improve Machine Learning Modelling of Cross-Platform Transcriptomic Data

Deng, F., Feng, C. H., Gao, N., & Zhang, L. (2025) · Transactions on Artificial Intelligence · doi:10.53941/tai.2025.100005 · Cited by 4 · see citing works