The rise of the distributions: why non-normality is important for understanding the transcriptome and beyond

Mar, Jessica C.

doi:10.1007/s12551-018-0494-4

Cited by 44 publications

(55 citation statements)

References 65 publications

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“…Exponential scaling of scRNA-seq has made it feasible to study scEV across thousands of cells [25] and quantify scEV based on measures of statistical dispersion such as the coefficient of variation (CV) [26,27]. The sheer number of cells sequenced in a "typical" droplet-based scRNA-seq experiment allows us to filter out for a sizable number of highly homogeneous cells, based on the similarity between their global transcriptional profiles.…”

Section: Introductionmentioning

confidence: 99%

Single-Cell Expression Variability Implies Cell Function

Osorio

Zhong

et al. 2019

Cells

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As single-cell RNA sequencing (scRNA-seq) data becomes widely available, cell-to-cell variability in gene expression, or single-cell expression variability (scEV), has been increasingly appreciated. However, it remains unclear whether this variability is functionally important and, if so, what are its implications for multi-cellular organisms. Here, we analyzed multiple scRNA-seq data sets from lymphoblastoid cell lines (LCLs), lung airway epithelial cells (LAECs), and dermal fibroblasts (DFs) and, for each cell type, selected a group of homogenous cells with highly similar expression profiles. We estimated the scEV levels for genes after correcting the mean-variance dependency in that data and identified 465, 466, and 364 highly variable genes (HVGs) in LCLs, LAECs, and DFs, respectively. Functions of these HVGs were found to be enriched with those biological processes precisely relevant to the corresponding cell type’s function, from which the scRNA-seq data used to identify HVGs were generated—e.g., cytokine signaling pathways were enriched in HVGs identified in LCLs, collagen formation in LAECs, and keratinization in DFs. We repeated the same analysis with scRNA-seq data from induced pluripotent stem cells (iPSCs) and identified only 79 HVGs with no statistically significant enriched functions; the overall scEV in iPSCs was of negligible magnitude. Our results support the “variation is function” hypothesis, arguing that scEV is required for cell type-specific, higher-level system function. Thus, quantifying and characterizing scEV are of importance for our understating of normal and pathological cellular processes.

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Section: Introductionmentioning

confidence: 99%

Single-Cell Expression Variability Implies Cell Function

Osorio

Zhong

et al. 2019

Cells

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“…For the entire transcriptome, this means that genes with expression profiles that more closely resemble a Normal distribution will be more easily detectable by standard statistical methods. This kind of bias means many genes may be being overlooked or down-weighted because we are not stopping to first evaluate the prevalence of different distributions [16].…”

Section: Discussionmentioning

confidence: 99%

The shape of gene expression distributions matter: how incorporating distribution shape improves the interpretation of cancer transcriptomic data

Torrenté

Zimmerman

Suzuki

et al. 2019

Preprint

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In genomics, we often impose the assumption that gene expression data follows a specific distribution. However, rarely do we stop to question this assumption or consider its applicability to all genes in the transcriptome. Our study investigated the prevalence of genes with expression distributions that are non-Normal in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, less than 50% of all genes were Normally-distributed, with other distributions including Gamma, Bimodal, Cauchy, and Lognormal were represented. Relevant information about cancer biology was captured by the genes with non-Normal gene expression. When used for classification, the set of non-Normal genes were able to discriminate between cancer patients with poor versus good survival status. Our results highlight the value of studying a gene's distribution shape to model heterogeneity of transcriptomic data. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of distribution in a patient cohort.

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“…Edited by Assoc. Prof. Joshua Ho and Dr. Eleni Giannoulatou (Ho and Giannoulatou 2019) the Big Data Issue contained 16 contributions dealing with subjects as diverse as Bayesian statistical analysis (Yau and Campbell 2019), the dangers of using statistical tests rooted in the assumption of a normal distribution (Mar 2019), Hi-C analysis of interactions between genomic loci (Pal et al 2019), bioinformatics-based discovery of cancer causing mutations (Nussinov et al 2019), and machine learning in predicting cancer patient treatment outcomes (Mehreen and Aittokallio 2019).…”

Section: -A Year In Reviewmentioning

confidence: 99%