ESCO: single cell expression simulation incorporating gene co-expression

Tian, Jinjin; Wang, Jiebiao; Roeder, Kathryn

doi:10.1093/bioinformatics/btab116

Cited by 28 publications

(20 citation statements)

References 38 publications

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“…Implementation of locCSN requires two key choices: an initialization of the window width for each local test and the thresholding parameter α to derive the zero-one adjacency matrix from the matrix of local test statistics. We simulate coexpression networks using ESCO (17) to evaluate performance of network estimation for various choices of these tuning parameters. We observe that the SD approach for window width recommended by locCSN performs markedly better than the quantile approach utilized by oCSN.…”

Section: Biophysics and Computational Biologymentioning

confidence: 99%

Constructing local cell-specific networks from single-cell data

Wang

Choi

Roeder

2021

Proc. Natl. Acad. Sci. U.S.A.

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Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

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Section: Biophysics and Computational Biologymentioning

confidence: 99%

Constructing local cell-specific networks from single-cell data

Wang

Choi

Roeder

2021

Proc. Natl. Acad. Sci. U.S.A.

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“…Implementation of locCSN requires two key choices: an initialization of the window width for each local test and the thresholding parameter α to derive the 0-1 adjacency matrix from the matrix of local test statistics. We simulate coexpression networks using ESCO (36) to evaluate performance of network estimation for various choices of these tuning parameters. We observe that the standard deviation approach for window width recommended by locCSN performs markedly better than the quantile approach utilized by oCSN.…”

Section: Usagementioning

confidence: 99%

“…To illustrate the importance of local calculations we compare locCSN and oCSN for synthetic data from ESCO (36). The cells are sampled from two populations, one for which a set of genes exhibit pairwise correlation and another for which the genes are independent (Additional file 1: Figure S8a -c).…”

Section: Illustrative Examplesmentioning

confidence: 99%

Constructing local Cell Sepcific Networks from Single Cell Data

Wang

Choi

Roeder

2021

Preprint

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Gene co-expression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach that estimates cell-specific networks (CSN) for each cell using a method inspired by Dai et al. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of network structure, which provide better estimates of gene block structures than traditional measures. The method, called locCSN, is based on a non-parametric investigation of the joint distribution of gene expression, hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. The individual networks preserve information about the heterogeneity of the cells and having repeated estimates of network structure facilitates testing for difference in network structure between groups of cells. The original CSN algorithm showed promise; however, it had shortcomings which locCSN overcomes. Additionally, we propose new downstream analysis methods using CSNs, to utilize more fully the information contained within them. Finally, to further our understanding of autism spectrum disorder we examined the evolution of gene networks in fetal brain cells and compared the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene co-expression changes.

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“…We also investigated the nature of DFC using simulated data that more closely resemble real scRNA-seq data. We used ESCO [15], a simulator that takes drop-out into account, to generate the data. We designed the data with two cell populations and 15 genes.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

et al. 2021

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Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which often involve evaluating differences in the mean expression levels of genes. Differentially expressed gene (DEG)-based analysis has been the most frequently used method of this kind. However, in association with increases in sample size such as in single-cell analysis, DEG-based analysis has faced difficulties associated with the inflation of P-values. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for discriminating a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data and that DFC enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement DEG-based methods for interpreting large data sets. DEG-based analysis uses lists of genes with differences in expression between groups, while DFC, which can be termed a discriminative approach, has potential applications in the task of cell characterization. Upon recent advances in the high-throughput analysis of single cells, methods of cell characterization such as scRNA-seq can be effectively subjected to the discriminative methods.

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ESCO: single cell expression simulation incorporating gene co-expression

Cited by 28 publications

References 38 publications

Constructing local cell-specific networks from single-cell data

Constructing local cell-specific networks from single-cell data

Constructing local Cell Sepcific Networks from Single Cell Data

Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

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