scholarly journals bayNorm: Bayesian gene expression recovery, imputation and normalization for single-cell RNA-sequencing data

2019 ◽  
Author(s):  
Wenhao Tang ◽  
François Bertaux ◽  
Philipp Thomas ◽  
Claire Stefanelli ◽  
Malika Saint ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Empirical Bayes ◽  
Missing Values ◽  
Likelihood Function ◽  
Differential Expression Analysis ◽  
Batch Effect ◽  
Supplementary Information ◽  
Single Cell Rna Sequencing

Abstract Motivation Normalization of single-cell RNA-sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability, high amounts of missing observations and batch effect typical of scRNA-seq datasets make this task particularly challenging. There is a need for an efficient and unified approach for normalization, imputation and batch effect correction. Results Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We first validate our assumptions by showing this model can reproduce different statistics observed in real scRNA-seq data. We demonstrate using publicly available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule fluorescence in situ hybridization measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared with other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalization, imputation and true count recovery of gene expression measurements from scRNA-seq data. Availability and implementation The R package ‘bayNorm’ is publishd on bioconductor at https://bioconductor.org/packages/release/bioc/html/bayNorm.html. The code for analyzing data in this article is available at https://github.com/WT215/bayNorm_papercode. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals bayNorm: Bayesian gene expression recovery, imputation and normalisation for single cell RNA-sequencing data

2018 ◽  
Author(s):  
Wenhao Tang ◽  
François Bertaux ◽  
Philipp Thomas ◽  
Claire Stefanelli ◽  
Malika Saint ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Molecule ◽  
Empirical Bayes ◽  
Missing Values ◽  
Likelihood Function ◽  
Differential Expression Analysis ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing

Normalisation of single cell RNA sequencing (scRNA-seq) data is a prerequisite to their interpretation. The marked technical variability and high amounts of missing observations typical of scRNA-seq datasets make this task particularly challenging. Here, we introduce bayNorm, a novel Bayesian approach for scaling and inference of scRNA-seq counts. The method’s likelihood function follows a binomial model of mRNA capture, while priors are estimated from expression values across cells using an empirical Bayes approach. We demonstrate using publicly-available scRNA-seq datasets and simulated expression data that bayNorm allows robust imputation of missing values generating realistic transcript distributions that match single molecule FISH measurements. Moreover, by using priors informed by dataset structures, bayNorm improves accuracy and sensitivity of differential expression analysis and reduces batch effect compared to other existing methods. Altogether, bayNorm provides an efficient, integrated solution for global scaling normalisation, imputation and true count recovery of gene expression measurements from scRNA-seq data.

scholarly journals CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones

2018 ◽  
Vol 34 (18) ◽  
pp. 3217-3219 ◽  
Author(s):  
Sören Müller ◽  
Ara Cho ◽  
Siyuan J Liu ◽  
Daniel A Lim ◽  
Aaron Diaz
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Analysis ◽  
Copy Number ◽  
Differential Expression Analysis ◽  
Software Tool ◽  
Supplementary Information ◽  
Single Cell Rna Sequencing ◽  
Robust Separation

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) has enabled studies of tissue composition at unprecedented resolution. However, the application of scRNA-seq to clinical cancer samples has been limited, partly due to a lack of scRNA-seq algorithms that integrate genomic mutation data. Results To address this, we present CONICS COpy-Number analysis In single-Cell RNA-Sequencing. CONICS is a software tool for mapping gene expression from scRNA-seq to tumor clones and phylogenies, with routines enabling: the quantitation of copy-number alterations in scRNA-seq, robust separation of neoplastic cells from tumor-infiltrating stroma, inter-clone differential-expression analysis and intra-clone co-expression analysis. Availability and implementation CONICS is written in Python and R, and is available from https://github.com/diazlab/CONICS. Supplementary information Supplementary data are available at Bioinformatics online.

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation

2020 ◽  
Vol 36 (10) ◽  
pp. 3131-3138
Author(s):  
Ke Jin ◽  
Le Ou-Yang ◽  
Xing-Ming Zhao ◽  
Hong Yan ◽  
Xiao-Fei Zhang
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Differential Expression Analysis ◽  
Supplementary Information ◽  
Expression Levels ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis ◽  
Gene Expression Levels

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SPsimSeq: semi-parametric simulation of bulk and single cell RNA sequencing data

2019 ◽  
Author(s):  
Alemu Takele Assefa ◽  
Jo Vandesompele ◽  
Olivier Thas
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Empirical Distribution ◽  
Supplementary Information ◽  
Rna Seq ◽  
Sequencing Data ◽  
Actual Distribution ◽  
Wide Range ◽  
Single Cell Rna Sequencing

SummarySPsimSeq is a semi-parametric simulation method for bulk and single cell RNA sequencing data. It simulates data from a good estimate of the actual distribution of a given real RNA-seq dataset. In contrast to existing approaches that assume a particular data distribution, our method constructs an empirical distribution of gene expression data from a given source RNA-seq experiment to faithfully capture the data characteristics of real data. Importantly, our method can be used to simulate a wide range of scenarios, such as single or multiple biological groups, systematic variations (e.g. confounding batch effects), and different sample sizes. It can also be used to simulate different gene expression units resulting from different library preparation protocols, such as read counts or UMI counts.Availability and implementationThe R package and associated documentation is available from https://github.com/CenterForStatistics-UGent/SPsimSeq.Supplementary informationSupplementary data are available at bioRχiv online.

scholarly journals Stably expressed genes in single-cell RNA-sequencing

2018 ◽  
Author(s):  
Julie M. Deeke ◽  
Johann A. Gagnon-Bartsch
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Stable Expression ◽  
Supplementary Information ◽  
Isolated Cells ◽  
Gene Sets ◽  
Single Cell Rna Sequencing ◽  
Endogenous Genes ◽  
Technical Artifacts

AbstractMotivationIn single-cell RNA-sequencing (scRNA-seq) experiments, RNA transcripts are extracted and measured from isolated cells to understand gene expression at the cellular level. Measurements from this technology are affected by many technical artifacts, including batch effects. In analogous bulk gene expression experiments, external references, e.g., synthetic gene spike-ins often from the External RNA Controls Consortium (ERCC), may be incorporated to the experimental protocol for use in adjusting measurements for technical artifacts. In scRNA-seq experiments, the use of external spike-ins is controversial due to dissimilarities with endogenous genes and uncertainty about sufficient precision of their introduction. Instead, endogenous genes with highly stable expression could be used as references within scRNA-seq to help normalize the data. First, however, a specific notion of stable expression at the single cell level needs to be formulated; genes could be stable in absolute expression, in proportion to cell volume, or in proportion to total gene expression. Different types of stable genes will be useful for different normalizations and will need different methods for discovery.ResultsWe compile gene sets whose products are associated with cellular structures and record these gene sets for future reuse and analysis. We find that genes whose final product are associated with the cytosolic ribosome have expressions that are highly stable with respect to the total RNA content. Notably, these genes appear to be stable in bulk measurements as well.Supplementary informationThe Supplement is available on bioRxiv, and the gene set database is available through [email protected]

Factorization-based Imputation of Expression in Single-cell Transciptomic Analysis (FIESTA) recovers Gene-Cell-State relationships

2021 ◽  
Author(s):  
Elnaz Mirzaei Mehrabad ◽  
Aditya Bhaskara ◽  
Benjamin T. Spike
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Missing Values ◽  
Expression Patterns ◽  
Cellular Systems ◽  
False Negatives ◽  
Data Set ◽  
Link Type ◽  
Single Cell Rna Sequencing

AbstractMotivationSingle cell RNA sequencing (scRNA-seq) is a powerful gene expression profiling technique that is presently revolutionizing the study of complex cellular systems in the biological sciences. Existing single-cell RNA-sequencing methods suffer from sub-optimal target recovery leading to inaccurate measurements including many false negatives. The resulting ‘zero-inflated’ data may confound data interpretation and visualization.ResultsSince cells have coherent phenotypes defined by conserved molecular circuitries (i.e. multiple gene products working together) and since similar cells utilize similar circuits, information about each each expression value or ‘node’ in a multi-cell, multi-gene scRNA-Seq data set is expected to also be predictable from other nodes in the data set. Based on this logic, several approaches have been proposed to impute missing values by extracting information from non-zero measurements in a data set. In this study, we applied non-negative matrix factorization approaches to a selection of published scRNASeq data sets to recommend new values where original measurements are likely to be inaccurate and where ‘zero’ measurements are predicted to be false negatives. The resulting imputed data model predicts novel cell type markers and expression patterns more closely matching gene expression values from orthogonal measurements and/or predicted literature than the values obtained from other previously published imputation [email protected] and implementationFIESTA is written in R and is available at https://github.com/elnazmirzaei/FIESTA and https://github.com/TheSpikeLab/FIESTA.

scholarly journals Stably expressed genes in single-cell RNA sequencing

2020 ◽  
Vol 18 (01) ◽  
pp. 2040004
Author(s):  
Julie M. Deeke ◽  
Johann A. Gagnon-Bartsch
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Stable Expression ◽  
Supplementary Information ◽  
Isolated Cells ◽  
Gene Sets ◽  
Single Cell Rna Sequencing ◽  
Endogenous Genes ◽  
Technical Artifacts

Motivation: In single-cell RNA-sequencing (scRNA-seq) experiments, RNA transcripts are extracted and measured from isolated cells to understand gene expression at the cellular level. Measurements from this technology are affected by many technical artifacts, including batch effects. In analogous bulk gene expression experiments, external references, e.g. synthetic gene spike-ins often from the External RNA Controls Consortium (ERCC), may be incorporated to the experimental protocol for use in adjusting measurements for technical artifacts. In scRNA-seq experiments, the use of external spike-ins is controversial due to dissimilarities with endogenous genes and uncertainty about sufficient precision of their introduction. Instead, endogenous genes with highly stable expression could be used as references within scRNA-seq to help normalize the data. First, however, a specific notion of stable expression at the single-cell level needs to be formulated; genes could be stable in absolute expression, in proportion to cell volume, or in proportion to total gene expression. Different types of stable genes will be useful for different normalizations and will need different methods for discovery. Results: We compile gene sets whose products are associated with cellular structures and record these gene sets for future reuse and analysis. We find that genes whose final products are associated with the cytosolic ribosome have expressions that are highly stable with respect to the total RNA content. Notably, these genes appear to be stable in bulk measurements as well. Supplementary information: Supplementary data are available through GitHub (johanngb/sc-stable).

scholarly journals Accounting for technical noise in single-cell RNA sequencing analysis

2017 ◽  
Author(s):  
Cheng Jia ◽  
Derek Kelly ◽  
Junhyong Kim ◽  
Mingyao Li ◽  
Nancy R. Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Error Control ◽  
Empirical Bayes ◽  
Type I Error ◽  
Differential Expression Analysis ◽  
Type I ◽  
Sequencing Analysis ◽  
Batch Effects ◽  
Single Cell Rna Sequencing

ABSTRACTRecent technological breakthroughs have made it possible to measure RNA expression at the single-cell level, thus paving the way for exploring expression heterogeneity among individual cells. Current single-cell RNA sequencing (scRNA-seq) protocols are complex and introduce technical biases that vary across cells, which can bias downstream analysis without proper adjustment. To account for cell-to-cell technical differences, we propose a statistical framework, TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. More importantly, TASC is able to adjust for covariates to further eliminate confounding that may originate from cell size and cell cycle differences. In simulation and real scRNA-seq data, TASC achieves accurate Type I error control and displays competitive sensitivity and improved robustness to batch effects in differential expression analysis, compared to existing methods. TASC is programmed to be computationally efficient, taking advantage of multi-threaded parallelization. We believe that TASC will provide a robust platform for researchers to leverage the power of scRNA-seq.

scholarly journals scGEApp: a Matlab app for feature selection on single-cell RNA sequencing data

2019 ◽  
Author(s):  
James J. Cai
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Single Cell ◽  
Rna Sequencing ◽  
User Interfaces ◽  
Dropout Rate ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Full Spectrum ◽  
Single Cell Rna Sequencing

AbstractMotivationThe recent development of single-cell technologies, especially single-cell RNA sequencing (scRNA-seq), provides an unprecedented level of resolution to the cell type heterogeneity. It also enables the study of gene expression variability across individual cells within a homogenous cell population. Feature selection algorithms have been used to select biologically meaningful genes while controlling for sampling noise. An easy-to-use application for feature selection on scRNA-seq data requires integration of functions for data filtering, normalization, visualization, and enrichment analyses. Graphic user interfaces (GUIs) are desired for such an application.ResultsWe used native Matlab and App Designer to develop scGEApp for feature selection on singlecell gene expression data. We specifically designed a new feature selection algorithm based on the 3D spline fitting of expression mean (μ), coefficient of variance (CV), and dropout rate (rdrop), making scGEApp a unique tool for feature selection on scRNA-seq data. Our method can be applied to single-sample or two-sample scRNA-seq data, identify feature genes, e.g., those with unexpectedly high CV for given μ and rdrop of those genes, or genes with the most feature changes. Users can operate scGEApp through GUIs to use the full spectrum of functions including normalization, batch effect correction, imputation, visualization, feature selection, and downstream analyses with GSEA and GOrilla.Availabilityhttps://github.com/jamesjcai/scGEAppContact:[email protected] informationSupplementary data are available at Bioinformatics online.

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