Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
Open accessPreprint — published as 10.1093/biostatistics/kxx053 · Paper page
Abstract
Until recently, high-throughput gene expression technology, such as RNA-Sequencing (RNA-seq) required hundreds of thousands of cells to produce reliable measurements. Recent technical advances permit genome-wide gene expression measurement at the single-cell level. Single-cell RNA-Seq (scRNA-seq) is the most widely used and numerous publications are based on data produced with this technology. However, RNA-Seq and scRNA-seq data are markedly different. In particular, unlike RNA-Seq, the majority of reported expression levels in scRNA-seq are zeros, which could be either biologically-driven, genes not expressing RNA at the time of measurement, or technically-driven, gene expressing RNA, but not at a sufficient level to detected by sequencing technology. Another difference is that the proportion of genes reporting the expression level to be zero varies substantially across single cells compared to RNA-seq samples. However, it remains unclear to what extent this cell-to-cell variation is being driven by technical rather than biological variation. Furthermore, while systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies, these issues have received minimal attention in published studies based on scRNA-seq technology. Here, we use an assessment experiment to examine data from published studies and demonstrate that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we present evidence that some of these reported zeros are driven by technical variation by demonstrating that scRNA-seq produces more zeros than expected and that this bias is greater for lower expressed genes. In addition, this missing data problem is exacerbated by the fact that this technical variation varies cell-to-cell. Then, we show how this technical cell-to-cell variability can be confused with novel biological results. Finally, we demonstrate and discuss how batch-effects and confounded experiments can intensify the problem.
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One of the biggest limitations of the C1 system is that single cell capture and preparation from different conditions are fully independent.
“One of the biggest limitations of using the C1 system, however, is that single cell capture and preparation from different conditions are fully independent23.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [23]
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Chen, J., Cheung, F., Shi, R., Zhou, H., Lu, W., Candia, J., Kotliarov, Y., Stagliano, K. R., & Tsang, J. S. (2018) · Journal of Translational Medicine · doi:10.1186/s12967-018-1578-4 · Cited by 157 · see citing works
“When multiple samples are analyzed, technical variation is commonly found in high throughput data [8].”
BackgroundSingle Cell RNA Sequencing of Rare Immune Cell Populations
Nguyen, A., Khoo, W. H., Moran, I., Croucher, P. I., & Phan, T. G. (2018) · Frontiers in Immunology · doi:10.3389/fimmu.2018.01553 · Cited by 123 · see citing works
“Thus, preparing different experimental groups on library preparation plates and sequencing lanes and, if possible, minimizing batching of experiments is important in decreasing technical bias (19).”
BackgroundAn Introduction to the Analysis of Single-Cell RNA-Sequencing Data
Aljanahi, A. A., Danielsen, M., & Dunbar, C. E. (2018) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2018.07.003 · Cited by 116 · see citing works
“The DGE matrix is expected to be mostly filled with zeros because of these dropout events.(48) Therefore, normalization and scaling are vital prior to interpreting the data.”
Tang, W., Bertaux, F., Thomas, P., Stefanelli, C., Saint, M., Marguerat, S., & Shahrezaei, V. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz726 · Cited by 113 · see citing works
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Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
Zhu, X., Ching, T., Pan, X., Weissman, S. M., & Garmire, L. (2017) · PeerJ · doi:10.7717/peerj.2888 · Cited by 86 · see citing works
Exploiting single-cell expression to characterize co-expression replicability
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2016) · Genome Biology · doi:10.1186/s13059-016-0964-6 · Cited by 76 · see citing works
scAlign: a tool for alignment, integration, and rare cell identification from scRNA-seq data
Johansen, N., & Quon, G. (2019) · Genome biology · doi:10.1186/s13059-019-1766-4 · Cited by 101 · see citing works
Owen, R. P., White, M. J., Severson, D. T., Braden, B., Bailey, A. A., Goldin, R. D., Wang, L. M., Ruiz‐Puig, C., Maynard, N. D., Green, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06796-9 · Cited by 94 · see citing works
Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysisPreprint
Hsiao, C. J., Tung, P., Blischak, J., Burnett, J. E., Barr, K., Dey, K. K., Stephens, M., & Gilad, Y. (2020) · Genome Research · doi:10.1101/gr.247759.118 · Cited by 85 · see citing works
“R-squared=0.39 for PC1; 0.25 for PC2), consistent with results from previous studies (Hicks et al. 2018).”
MethodResultInfluentialWhat are the applications of single-cell RNA sequencing in cancer research: a systematic review
Li, L., Xiong, F., Wang, Y., Zhang, S., Gong, Z., Li, X., He, Y., Shi, L., Wang, F., Liao, Q., et al. (2021) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-021-01955-1 · Cited by 86 · see citing works
“During the scRNA-seq experimental procedure, when cells subject to different conditions are cultured, captured, and sequenced separately, batch effects will be evident [20].”
MethodLeukocyte Heterogeneity in Adipose Tissue, Including in Obesity
Weinstock, A., Silva, H. M., Moore, K. J., Schmidt, A. M., & Fisher, E. A. A. (2020) · Circulation Research · doi:10.1161/circresaha.120.316203 · Cited by 67 · see citing works
A large-scale benchmark for network inference from single-cell perturbation data
Chevalley, M., Roohani, Y. H., Mehrjou, A., Leskovec, J., & Schwab, P. (2025) · Communications Biology · doi:10.1038/s42003-025-07764-y · Cited by 26 · see citing works
“Single-celldataoftenhasmuchhighertechnicalnoiseand variability than bulk RNA-seq, due to factors like ampli fi cation noise, low mRNA capture ef fi ciency, and sequencing depth 40 .”
How many markers are needed to robustly determine a cell's type?
Fischer, S., & Gillis, J. A. (2021) · iScience · doi:10.1016/j.isci.2021.103292 · Cited by 43 · see citing works
“For many of these applications, the strength of individual markers is limited by the lack of conservation (Bakken et al., 2021) and the sporadic expression in individual cells (Kharchenko et al., 2014; Risso et al., 2018; Hicks et al., 2018; Chen and Zhou 2018).”
BackgroundChen, W., Zhang, S., Williams, J., Ju, B., Shaner, B., Easton, J., Wu, G., & Chen, X. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.03.026 · Cited by 44 · see citing works
“Although the batch-effect concern is universal for all scRNA-seq analyses (as recently reviewed in [7]), it is probably more prominent for differential expression (DE) analysis of scRNA-seq data, because cells from different experimental groups/conditions are typically captured separately, and this produces large coll…”
BackgroundEmbedding to reference t-SNE space addresses batch effects in single-cell classification
Poličar, P. G., Stražar, M., & Zupan, B. (2021) · Machine Learning · doi:10.1007/s10994-021-06043-1 · Cited by 38 · see citing works
“It has been well documented that even small differences in the experimental setup of cell-dissociation, handling protocols, librarypreparation technologies, or sequencing platforms can significantly affect the resulting gene-expression measurements (Tung et al., 2017; Hicks et al., 2018).”
BackgroundComparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing
Xie, Y., Chen, H., Chellamuthu, V. R., Lajam, A. B. M., Albani, S., Low, A. H. L., Petretto, E. G., & Behmoaras, J. V. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25073828 · Cited by 35 · see citing works
Zhang, Z., Zhao, X., Bindra, M., Qiu, P., & Zhang, X. (2024) · Nature Communications · doi:10.1038/s41467-024-45227-w · Cited by 36 · see citing works
“Measuring data from multiple conditions in the same batch can largely ameliorate this problem, which echoes recommendationsonwet-labexperimentaldesignthatbatchesshould be distributed across biological conditions 25,26 .”
BackgroundAdvances in single-cell sequencing: insights from organ transplantation
Wang, Y., Wang, J., Schnieke, A., & Fischer, K. (2021) · Military Medical Research · doi:10.1186/s40779-021-00336-1 · Cited by 31 · see citing works
“Batch effects are caused by large-scale scRNA-seq datasets, samples prepared in different laboratories, even those based on the same protocol, and large data generated from separate technicians or different time points [49, 50].”
BackgroundTuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis
Raimundo, F., Vallot, C., & Vert, J. (2020) · Genome biology · doi:10.1186/s13059-020-02128-7 · Cited by 31 · see citing works
“Due to the small amount of RNA available in each individual cell, and to the technical difficulty to analyze thousands (or millions) of cells in parallel, raw scRNA-seq data have been found to be subject to a number of biases including low sequencing depth, over-dispersion and zero inflation of read counts, or sensiti…”
BackgroundFrom multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis
Carangelo, G., Magi, A., & Semeraro, R. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.994069 · Cited by 36 · see citing works
“In single cell settings, the variables at stake increase as the number of cells under examination increases, due to cell-to-cell variability and biases such as dropout (Vallejos et al., 2017; Hicks et al., 2018).”
BackgroundHigh-Dimensional Separability for One- and Few-Shot Learning
Gorban, A. N., Grechuk, B., Mirkes, E. M., et al. (2021) · Entropy · doi:10.3390/e23081090 · Cited by 26 · see citing works
“First principal components are frequently considered to be associated 309 with technical artifacts in the analysis of omics datasets in bioinformatics, and their removal might 310 improve the downstream analyses [46,47].”
BackgroundNoise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing
Zhang, R., Atwal, G. S., & Lim, W. K. (2021) · Patterns · doi:10.1016/j.patter.2021.100211 · Cited by 21 · see citing works
St‐Pierre, M., VanderZwaag, J., Loewen, S. M., & Tremblay, M. (2022) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2022.932572 · Cited by 27 · see citing works
“…of technical and biological difficulties in scRNA-seq have been recently reported, from batch effect to incorrectly reported data and differing statistical approaches (Tung et al., 2017; Hicks et al., 2018; Squair et al., 2021), as well as artifacts caused by cell preparation (Nguyen et al., 2018).”
An efficient scRNA-seq dropout imputation method using graph attention network
Xu, C., Cai, L., & Gao, J. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04493-x · Cited by 30 · see citing works
“However, dropout events will seriously affect the calculation of the distance between expression profiles, which leads to downstream results [16].”
BackgroundUsing deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data
Bahl, E., Chatterjee, S., Mukherjee, U., Elsadany, M., Vanrobaeys, Y. S., Lin, L., McDonough, M. C., Resch, J. M., Giese, K.-P., Abel, T. G., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44503-5 · Cited by 29 · see citing works
Hsu, L. L., & Culhane, A. C. (2023) · Scientific Reports · doi:10.1038/s41598-022-26434-1 · Cited by 22 · see citing works
Mirkes, E. M., Bac, J., Fouché, A., Викторович, С. С., Zinovyev, A. Y., & Gorban, A. N. (2022) · Entropy · doi:10.3390/e25010033 · Cited by 21 · see citing works
“For example, the first principal components are frequently associated with technical artifacts in the analysis of omics datasets in bioinformatics, and removing of such components might improve the downstream analyses [25, 26].”
BackgroundDetecting hidden batch factors through data-adaptive adjustment for biological effects
Yi, H., Raman, A. T., Zhang, H., Allen, G. I., & Liu, Z. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx635 · Cited by 23 · see citing works
Identification of cell types in a mouse brain single-cell atlas using low sampling coverage
Bhaduri, A., Nowakowski, T. J., Pollen, A. A., & Kriegstein, A. R. (2018) · BMC Biology · doi:10.1186/s12915-018-0580-x · Cited by 15 · see citing works
“To account for potentially pervasive batch effects in single-cell datasets [11, 12], each subset was randomly selected from the libraries represented in the original dataset, and Louvain-Jaccard clustering generated clusters that each represented many of these libraries [7] (Additional file 1: Figure S1b–d).”
MethodDeciphering deep-sea chemosynthetic symbiosis by single-nucleus RNA-sequencing
Wang, H., He, K., Zhang, H., Zhang, Q., Cao, L., Li, J., Zhong, Z., Chen, H., Zhou, L., Lian, C., et al. (2023) · eLife · doi:10.7554/elife.88294 · Cited by 7 · see citing works
Mitigating the adverse impact of batch effects in sample pattern detection
Fei, T., Zhang, T., Shi, W., & Yu, T. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty117 · Cited by 19 · see citing works
One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data
Wang, C. X., Zhang, L., & Wang, B. (2022) · Genome biology · doi:10.1186/s13059-022-02659-1 · Cited by 14 · see citing works
“Datasets generated by different sequencing platforms and technologies have inherent technical differences [14, 15], posing greater challenge to the integration task as the distributions of gene expressions vary significantly across the five datasets.”
BackgroundLin, Z., Zamanighomi, M., Daley, T., Ma, S., & Wong, W. H. (2020) · Statistical Science · doi:10.1214/19-sts714 · Cited by 17 · see citing works
Zeng, P., & Lin, Z. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009064 · Cited by 20 · see citing works
Zhang, A. W., O’Flanagan, C. H., Chavez, E. A., Lim, J. L., McPherson, A., Wiens, M., Walters, P., Chan, T., Hewitson, B., Lai, D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/521914 · Cited by 9 · see citing works
“Given the technical effects associated with differences in experimental design and processing, expression profiles for reference populations may not be directly comparable to those for other single-cell RNA-seq experiments [11].”
BackgroundA CRISPR/Cas9-based enhancement of high-throughput single-cell transcriptomics
Pandey, A. C., Bezney, J., DeAscanis, D., Kirsch, E., Ahmed, F., Crinklaw, A., Choudhary, K. S., Mandala, T., Deason, J., Hamidi, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59880-2 · Cited by 13 · see citing works
Chong, Z. X., Ho, W. Y., Yeap, S.-K., Wang, M., Chien, Y., Verusingam, N. D., & Ong, H. K. (2021) · Journal of the Chinese Medical Association · doi:10.1097/jcma.0000000000000535 · Cited by 14 · see citing works
Stochastic models for single‐cell data: Current challenges and the way forward
Hsu, I. S., & Moses, A. M. (2021) · FEBS Journal · doi:10.1111/febs.15760 · Cited by 13 · see citing works
Liu, C., Wang, L., & Liu, Z. (2023) · BMC Bioinformatics · doi:10.1186/s12859-022-05126-7 · Cited by 11 · see citing works
“First, to account for the high dropout rate and noise [36] in single-cell data, we built functions to smooth [37] the data.”
MethodExploring the optimization of autoencoder design for imputing single-cell RNA sequencing data
Xi, N. M., & Li, J. J. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.07.041 · Cited by 6 · see citing works
“expressed genes have more zeros than highly expressed genes have [15, 55].”
BackgroundWeinstein, E. N., & Miller, J. W. (2021) · PubMed · doi:10.48550/arxiv.2109.02712
Molecular Heterogeneity in Large-Scale Biological Data: Techniques and Applications
Deng, C., Daley, T., Brandine, G. D. S., & Smith, A. D. (2019) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-072018-021339 · Cited by 7 · see citing works
“The latter are known as dropout genes and tend to be rare and lowly expressed genes (123).”
BackgroundMethodBatch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment
Fei, T., & Yu, T. (2019) · bioRxiv · doi:10.1101/669739 · Cited by 9
“Hicks et al. (2017) discussed the coexistence of biological signal and technical variation, which may still compromise the downstream analysis.”
BackgroundMethodInfluentialStecher, C., Bischl, R., Schmid-Böse, A., Ferstl, S., Potzmann, E., Frank, M., Braun, N., Farlik, M., Flavell, R. A., & Herndler‐Brandstetter, D. (2025) · Nature Communications · doi:10.1038/s41467-025-61231-0 · Cited by 8 · see citing works
“Sincethe abundance of Il15 + stromalcellsin the BM is low and scRNA-seq is biased toward underestimating weakly expressed genes 47 , we generated our scRNA-seq dataset from sorted Il15 GFP+ stromalcells.”
Normalizing need not be the norm: count-based math for analyzing single-cell data
Church, S. H., Mah, J. L., Wagner, G. P., & Dunn, C. W. (2023) · Theory in Biosciences · doi:10.1007/s12064-023-00408-x · Cited by 7 · see citing works
“However, because the total counts per cell are much smaller than the counts per sample in bulk experiments, there are problems with interpreting counts as measurements of proportional abundances (Vallejos et al. 2017; Townes et al. 2019).”
InfluentialBoosting scRNA-seq data clustering by cluster-aware feature weighting
Li, R., Guan, J., & Zhou, S. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04033-7 · Cited by 8 · see citing works
“The generation of these zero values is due to that these genes are not expressed in these cells, or their genetic products are not detected (also called “dropout events” [17]).”
BackgroundOsório, T. G., Pavesi, E., Abou‐El‐Ardat, K., Qureshi, N., Cassidy, L., John, T. L. S., Sirotin, N., & Piechowski‐Jóźwiak, B. (2025) · Frontiers in Aging · doi:10.3389/fragi.2025.1656727 · Cited by 10 · see citing works
Partially characterized topology guides reliable anchor-free scRNA-integration
He, C., Filippidis, P., Kleinstein, S. H., & Guan, L. (2025) · Communications Biology · doi:10.1038/s42003-025-07988-y · Cited by 4 · see citing works
“The second category is mixing-loss-based integration approaches, which pair reconstruction loss together with a batch-mixing loss to encourage well-mixed batches if possible.”
SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data
Gan, D., & Li, J. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac819 · Cited by 6 · see citing works
The differential impacts of dataset imbalance in single-cell data integrationPreprint
Maan, H., Zhang, L., Yu, C., Geuenich, M. J., Campbell, K. R., & Wang, B. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.06.511156 · Cited by 6 · see citing works
“These factors, combined with measurements 54 often conducted across separate experimental groups without balanced designs 55 [7], leads to a higher susceptibility of scRNA-seq data to batch effects.”
BackgroundXia, Q., Thompson, J. A., & Koestler, D. C. (2021) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2021-0020 · Cited by 8 · see citing works
“2010), single-cell sequencing (Hicks et al. 2018) and DNA methylation (Sun et al.”
BackgroundVerrillo, L., Mangano, E., Drongitis, D., Ivan Merelli, A., Pischedda, F., Piccoli, G., Consolandi, C., Bordoni, R., & Miano, M. G. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108960 · Cited by 6 · see citing works
“To cope with potential biases due to cell sampling, differences in the mRNA molecule number, library size and clustering, we carried out a global bioinformatic normalization prior to analyzing biological signals (Hicks et al., 2018; Yuan et al., 2017).”
MethodVarying-Censoring Aware Matrix Factorization for Single Cell RNA-SequencingPreprint
Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/166736 · Cited by 6 · see citing works
“Furthermore, Hicks et al. [2017] provide evidence that differences in the proportion of zeros is frequently driven by technical artifacts rather than biological factors. We demonstrate that failing to address these features of the data can lead to identification of spurious clusters even when the underlying data are e…”
BackgroundMethodInfluentialSelf-citationIntegration of single‐cell proteomic datasets through distinctive proteins in cell clusters
Koca, M. B., & Sevilgen, F. E. (2023) · PROTEOMICS · doi:10.1002/pmic.202300282 · Cited by 5 · see citing works
“Additionally, the use of different technologies to perform measurements can also lead to batch effects [3].”
BackgroundTran, A., Yang, P., Yang, J. Y. H., & Ormerod, J. T. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac008 · Cited by 7 · see citing works
“In the sequencing protocol for scRNAseq, lowly expressed genes may not be captured in the amplification step causing them to be completely unrepresented in the final data [44, 45].”
BackgroundJoint Gene Network Construction by Single-Cell RNA Sequencing Data
Dong, M., He, Y., Jiang, Y., & Zou, F. (2022) · Biometrics · doi:10.1111/biom.13645 · Cited by 5 · see citing works
“Though both are count data, scRNA-seq data are inherently sparser, with an excessive amount of zeros due to either technical artifacts (false zeros), known as “dropouts” (Hicks et al., 2018), or biological absence of expression (true zeros) (Jiang et al., 2017; Dong and Jiang, 2019).”
BackgroundGeometry of Similarity ComparisonsPreprint
Tabaghi, P., Peng, J., Milenković, O., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.09858 · Cited by 1 · see citing works
“[53] Stephanie C Hicks, F William Townes, Mingxiang Teng, and Rafael A Irizarry.”
BackgroundDeep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysisPreprint
Li, X., Lyu, Y., Park, J., et al. (2019) · bioRxiv · doi:10.1101/530378 · Cited by 5 · see citing works
Addressing the looming identity crisis in single cell RNA-seq
(2017) · doi:10.1101/150524 · Cited by 365 · see citing works
Noureen, N., Wang, X., & Zheng, S. (2022) · STAR Protocols · doi:10.1016/j.xpro.2022.101877 · Cited by 4 · see citing works
“Dropouts are the main reason for scRNAseq data sparsity.(24) Dropouts arise from a low amount of input materials in sequencing and stochastic transcription.”
BackgroundCutting-edge advances in endocrine therapy for breast cancer (Review)
Dong, Y.-M., Shao, L.-H., Li, Q., et al. (2026) · Oncology Letters · doi:10.3892/ol.2026.15569 · Cited by 3
“To advance clinical‑grade implementation, the development of high‑quality reference atlases, harmonized quality‑control (QC) standards and consensus reporting systems will be essential to ensure reproducibility, cross‑platform compara‑ bility and clinically meaningful interpretation (90,94).”
COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection
Luo, Q., Chen, Y., & Lan, X. (2024) · BMC Biology · doi:10.1186/s12915-024-01963-5 · Cited by 5 · see citing works
Limitations and optimizations of cellular lineages tracking
Leibovich, N., & Goyal, S. (2023) · bioRxiv · doi:10.1101/2023.03.15.532767 · Cited by 3
Chen, A. A., Clark, K. A., Dewey, B. E., DuVal, A. L., Pellegrini, N., Nair, G., Jalkh, Y., Khalil, S., Zurawski, J., Calabresi, P. A., et al. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012241 · Cited by 2 · see citing works
“However, in their current form, these methods do not account for covariates and are known to be substantially influenced by confounders such as batch [3].”
BackgroundMeta-analytic markers reveal a generalizable description of cortical cell typesPreprint
Fischer, S., & Gillis, J. (2021) · bioRxiv · doi:10.1101/2021.04.16.439807 · Cited by 2 · see citing works
“2020) and the sporadic expression in individual 57 cells (Kharchenko et al. 2014; Risso et al. 2018; Hicks et al. 2018; Chen and Zhou 2018).”
Das, J., & Lanier, L. L. (2019) · Journal of Leukocyte Biology · doi:10.1002/jlb.6mr1218-505r · Cited by 3 · see citing works
RESCUE: recovery of unattributed expression patterns in spatial transcriptomics
Lee, Y. J., Yeo, S. Y., Schrader, A. W., Lee, J., Traniello, I. M., Asadian, M., Ahmed, A. C., Robinson, G. E., Han, H., & Zhao, S. D. (2026) · Nature Communications · doi:10.1038/s41467-026-71720-5
“Second, log-transformation requires adding pseudo-counts, which can introduce signi fi cant arti fi cial distortion between zeros and non-zeros 84,85 .”
Zhang, Y., Yang, B., Yu, Y., Wang, X., Niu, C., Zhang, Y., Liu, Y., Li, J., Zhang, C., Yang, J., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-70582-1 · Cited by 1 · see citing works
“The fi eld has relied on retrospective computationalnormalization 25,26 , post-hoc methods that treat statistical symptoms, not the underlying measurement problem, andriskcon fl atingartifactswithbiologicalsignals 27 .”
Liu, W., & Zhao, Z. (2024) · bioRxiv · doi:10.1101/2024.08.15.608093 · Cited by 1
Points of Significance: Principal Component Analysis for Biocentric Data Visualization
Arbatsky, M., Tyurin-Kuzmin, P., Kulebyakin, K., Chechekhin, V., Kalinina, N., Sysoeva, V., Semina, E., & Rubina, K. (2022) · BioNanoScience · doi:10.1007/s12668-022-01021-w · Cited by 2 · see citing works
“In other words, there are inevitable limits to using a standard analysis algorithm, which can result in a potential loss of large amounts of data that otherwise can be fruitful in formulating a hypothesis [8].”
BackgroundDimension Reduction for Data with Heterogeneous Missingness
Ling, Y., Liu, Z., & Xue, J.-H. (2021) · Conference on Uncertainty in Artificial Intelligence · doi:10.48550/arxiv.2109.11765 · Cited by 1
“Some zeros indicate the true biological non-expression while others are due to the corresponding missing values, which are called dropouts [Hicks et al., 2017, Li and Li, 2018].”
BackgroundChan, M. E., Bhamidipati, P. S., Goldsby, H., et al. (2019) · bioRxiv · doi:10.1101/840801 · Cited by 1 · see citing works
Nonparametric statistical inference and imputation for incomplete categorical data
Wang, C.-J., Shen, L.-H., Li, H., et al. (2017) · doi:10.4310/sii.2020.v13.n1.a2 · Cited by 1
“In biological experiments, data may be incomplete for either biologically-driven or technically-driven reasons [7].”
BackgroundOdaka, M., Magnin, M., & Inoue, K. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1250545 · Cited by 1 · see citing works
“Dimensionality reduction and clustering The step after quality control was the imputation of zeros, representing either technically missing data or biologically absent genes, within a sparse matrix of single-cell omics data [49].”
MethodSingh, A. N. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.24.445309
t-SNE transformation: a normalization method for local features of single-cell RNA-seq dataPreprint
Chengrui, A., Bacardit, J., Zhang, N., & Wu, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/799288
“It underestimated 7 the means of low expressed genes [15] as well as their derivations [7, 10].”
Background
- Authors
- Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A.
- Affiliations
- Harvard University (US)
- Corresponding author
- Hicks, Stephanie C.
- Venue
- Biostatistics
- Publisher
- openRxiv
- Published
- 2015-08-25
- Volume
- 19
- Issue
- 4
- Pages
- 562-578
- ISSN
- 1465-4644 (ISSN-L)
- PMC
- PMC6215955
- OpenAlex
- W2794891507
- Cited by
- 625
- References
- 76
- Citations by year
- 2026: 63 · 2025: 70 · 2024: 57 · 2023: 64 · 2022: 67 · 2021: 86 · 2020: 65 · 2019: 54 · 2018: 43 · 2017: 34 · 2016: 19 · 2015: 2
- FWCI
- 21.88
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Biostatistics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (NIH-R01)
- National Human Genome Research Institute (K99HG009007, HG005220, R01HG005220)
- National Institute of General Medical Sciences (R01GM083084, R01GM103552)
- U.S. National Library of Medicine (T32LM012411)
- National Center for Research Resources (R01RR021967)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell RNA sequencing, missing data, technical variability, systematic errors, batch effects, confounding, low-expressed genes
- MeSH terms
- Animals, Humans, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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