Pooling across cells to normalize single-cell RNA sequencing data with many zero counts

By Aaron T. L. Lun, K. Bach, John C. Marioni. Genome Biology, 2016

1,087 citations recorded by the source corpus.

Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts are zero. We present a novel approach where expression values are summed across pools of cells, and the summed values are used for normalization. Pool-based size factors are then deconvolved to yield cell-based factors. Our deconvolution approach outperforms existing methods for accurate normalization of cell-specific biases in simulated data. Similar behavior is observed in real data, where deconvolution improves the relevance of results of downstream analyses.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/24606252810d79f22e06b371b4ce3cde80d4f1d6

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Citations in the corpus

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “Several normalization methods have been proposed for scRNA-seq data, such as SCnorm (Bacher et al., 2017), SAMstrt (Katayama et al., 2013) and a recently introduced deconvolution approach that uses the summed expression values across pools of cells to conduct normalization (Lun et al., 2016).”
Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “A normalization method based on pooling expression values 55 were developed to avoid stochastic zero counts which is robust to differentially expressed genes in the data.”
Comparison and evaluation of statistical error models for scRNA-seq (2021, methodology): “We benchmarked our procedure’s ability to identify differentially expressed genes against log normalization and scran [46].”
Best practices for single-cell analysis across modalities (2023, methodology): “Scran 30 normalization aims to minimize this issue by pooling cells with similar count depth and estimating pool-based size factors using a linear regression over genes.”
Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018, methodology): “Expression data from 4459 human T cells (t_4k) and 68,580 peripheral blood mononuclear cells (PBMCs; pbmc68k) from two separate donors were normalised separately using size factors estimated by the deconvolution method as previously described [28].”
SCENIC: Single-cell regulatory network inference and clustering (2017, methodology): “Statistical techniques and bioinformatics methods have been optimized for single-cell RNA-seq 4 , including methods for expression normalization 5,6 , differential expression analysis 7–12 , clustering 13–16 , dimensionality reduction 17,18 , trajectory inference 19 , and rare cell type…”