SAVER: gene expression recovery for single-cell RNA sequencing

Kang

et al. 2019

Preprint

ABSTRACTFast, robust and technology-independent computational methods are needed for supervised cell type annotation of single-cell RNA sequencing data. We present SciBet, a Bayesian classifier that accurately predicts cell identity for newly sequenced cells or cell clusters. We enable web client deployment of SciBet for rapid local computation without uploading local data to the server. This user-friendly and cross-platform tool can be widely useful for single cell type identification.

Section: Parameterization Of Gene Expression Distribution Of Single Cmentioning

confidence: 99%

SciBet: a portable and fast single cell type identifier

Kang

et al. 2019

Preprint

“…For droplet datasets, the observed UMI count can be modeled as a NB random variable, which also arises as a Poisson-gamma mixture 35 :…”

Section: Expression Entropy Modelmentioning

confidence: 99%

“…The and are shape parameter and rate parameter respectively. Given the assumption that the shape parameter is a constant across cells and genes, and that the rate parameter is a constant of gene across cells 35,36 , and can be expressed as and , respectively. Then the distributions can be recognized as:…”

Section: Expression Entropy Modelmentioning

confidence: 99%

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ROGUE: an entropy-based universal metric for assessing the purity of single cell population

et al. 2019

Preprint

Single-cell RNA sequencing (scRNA-seq) is a versatile tool for discovering and annotating cell types and states, but the determination and annotation of cell subtypes is often subjective and arbitrary. Often, it is not even clear whether a given cluster is uniform. Here we present an entropy-based statistic, ROGUE, to accurately quantify the purity of identified cell clusters. We demonstrated that our ROGUE metric is generalizable across datasets, and enables accurate, sensitive and robust assessment of cluster purity on a wide range of simulated and real datasets. Applying this metric to fibroblast and B cell datasets, we identified additional subtypes and demonstrated the application of ROGUE-guided analyses to detect true signals in specific subpopulations. ROGUE can be applied to all tested scRNA-seq datasets, and has important implications for evaluating the quality of putative clusters, discovering pure cell subtypes and constructing comprehensive, detailed and standardized single cell atlas. Z.Z. conceived this study. C.L. and B.L. designed the -model. B.L. introduced the algorithm of ROGUE, performed the benchmark testing, analyzed the data and developed the R package. Z.L. and X.R. assisted with method development. B.L., C.L. and Z.Z. wrote the manuscript with all the authors' inputs.

“…Evidence has shown that batch effects account for a substantial percentage of dropout variability at the cell level [1]. Many imputation methods have been proposed in recent years to recover dropouts [18,[21][22][23][24] in scRNA-seq data. However, among these methods, only BUSseq [18] takes both batch effects and dropout events into consideration.…”

Section: Introductionmentioning

confidence: 99%

SCRIBE: a new approach to dropout imputation and batch effects correction for single-cell RNA-seq data

Zhang

Kexuan

et al. 2019

Preprint

Single-cell RNA sequencing technologies are widely used in recent years as a powerful tool allowing the observation of gene expression at the resolution of single cells. Two of the major challenges in scRNA-seq data analysis are dropout events and batch effects. The inflation of zero(dropout rate) varies substantially across single cells. Evidence has shown that technical noise, including batch effects, explains a notable proportion of this cell-to-cell variation. To capture biological variation, it is necessary to quantify and remove technical variation. Here, we introduce SCRIBE (Single-Cell Recovery Imputation with Batch Effects), a principled framework that imputes dropout events and corrects batch effects simultaneously. We demonstrate, through real examples, that SCRIBE outperforms existing scRNA-seq data analysis tools in recovering cell-specific gene expression patterns, removing batch effects and retaining biological variation across cells.