scholarly journals Differential expression of single-cell RNA-seq data using Tweedie models

2021 ◽  
Author(s):  
Himel Mallick ◽  
Suvo Chatterjee ◽  
Shrabanti Chowdhury ◽  
Saptarshi Chatterjee ◽  
Ali Rahnavard ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Statistical Power ◽  
Heavy Tails ◽  
Linear Models ◽  
Dynamic Range ◽  
Expression Profiles ◽  
R Package ◽  
Tweedie Distribution ◽  
Probability Mass

SummaryThe performance of computational methods and software to identify differentially expressed genes in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq data. Here, we propose to use generalized linear models with the Tweedie distribution that can flexibly capture a large dynamic range of observed scRNA-seq data across experimental platforms induced by heavy tails, sparsity, or different count distributions to model the technological variability in scRNA-seq expression profiles. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we performed a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R package) is available at https://github.com/himelmallick/Tweedieverse.

scholarly journals Hierarchicell: an R-package for estimating power for tests of differential expression with single-cell data

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kip D. Zimmerman ◽  
Carl D. Langefeld
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Study Design ◽  
Statistical Power ◽  
R Package ◽  
Correlation Structure ◽  
Experimental Unit ◽  
Rna Seq ◽  
Sample Correlation ◽  
Number Of Cells

Abstract Background Study design is a critical aspect of any experiment, and sample size calculations for statistical power that are consistent with that study design are central to robust and reproducible results. However, the existing power calculators for tests of differential expression in single-cell RNA-seq data focus on the total number of cells and not the number of independent experimental units, the true unit of interest for power. Thus, current methods grossly overestimate the power. Results Hierarchicell is the first single-cell power calculator to explicitly simulate and account for the hierarchical correlation structure (i.e., within sample correlation) that exists in single-cell RNA-seq data. Hierarchicell, an R-package available on GitHub, estimates the within sample correlation structure from real data to simulate hierarchical single-cell RNA-seq data and estimate power for tests of differential expression. This multi-stage approach models gene dropout rates, intra-individual dispersion, inter-individual variation, variable or fixed number of cells per individual, and the correlation among cells within an individual. Without modeling the within sample correlation structure and without properly accounting for the correlation in downstream analysis, we demonstrate that estimates of power are falsely inflated. Hierarchicell can be used to estimate power for binary and continuous phenotypes based on user-specified number of independent experimental units (e.g., individuals) and cells within the experimental unit. Conclusions Hierarchicell is a user-friendly R-package that provides accurate estimates of power for testing hypotheses of differential expression in single-cell RNA-seq data. This R-package represents an important addition to single-cell RNA analytic tools and will help researchers design experiments with appropriate and accurate power, increasing discovery and improving robustness and reproducibility.

scholarly journals DEsingle for detecting three types of differential expression in single-cell RNA-seq data

2017 ◽  
Author(s):  
Zhun Miao ◽  
Ke Deng ◽  
Xiaowo Wang ◽  
Xuegong Zhang
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Negative Binomial ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Binomial Model ◽  
Supplementary Data ◽  
Rna Seq ◽  
Real Zeros

AbstractSummaryThe excessive amount of zeros in single-cell RNA-seq data include “real” zeros due to the on-off nature of gene transcription in single cells and “dropout” zeros due to technical reasons. Existing differential expression (DE) analysis methods cannot distinguish these two types of zeros. We developed an R package DEsingle which employed Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect 3 types of DE genes in single-cell RNA-seq data with higher accuracy.Availability and ImplementationThe R package DEsingle is freely available at https://github.com/miaozhun/DEsingle and is under Bioconductor’s consideration [email protected] informationSupplementary data are available at bioRxiv online.

scholarly journals DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data

2019 ◽  
Vol 35 (24) ◽  
pp. 5155-5162 ◽  
Author(s):  
Chengzhong Ye ◽  
Terence P Speed ◽  
Agus Salim
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Type I Error ◽  
R Package ◽  
Supplementary Information ◽  
Type I ◽  
Common Phenomenon ◽  
Rna Seq ◽  
Capture Process ◽  
Technological Platforms

Abstract Motivation Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed it affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the process that gives rise to the dropout events. We develop DECENT, a method for DE analysis of scRNA-seq data that explicitly and accurately models the molecule capture process in scRNA-seq experiments. Results We show that DECENT demonstrates improved DE performance over existing DE methods that do not explicitly model dropout. This improvement is consistently observed across several public scRNA-seq datasets generated using different technological platforms. The gain in improvement is especially large when the capture process is overdispersed. DECENT maintains type I error well while achieving better sensitivity. Its performance without spike-ins is almost as good as when spike-ins are used to calibrate the capture model. Availability and implementation The method is implemented as a publicly available R package available from https://github.com/cz-ye/DECENT. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals genesorteR: Feature Ranking in Clustered Single Cell Data

2019 ◽  
Author(s):  
Mahmoud M Ibrahim ◽  
Rafael Kramann
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Large Cell ◽  
R Package ◽  
Marker Genes ◽  
Data Sets ◽  
Cell Type ◽  
Cell Data

ABSTRACTMarker genes identified in single cell experiments are expected to be highly specific to a certain cell type and highly expressed in that cell type. Detecting a gene by differential expression analysis does not necessarily satisfy those two conditions and is typically computationally expensive for large cell numbers.Here we present genesorteR, an R package that ranks features in single cell data in a manner consistent with the expected definition of marker genes in experimental biology research. We benchmark genesorteR using various data sets and show that it is distinctly more accurate in large single cell data sets compared to other methods. genesorteR is orders of magnitude faster than current implementations of differential expression analysis methods, can operate on data containing millions of cells and is applicable to both single cell RNA-Seq and single cell ATAC-Seq data.genesorteR is available at https://github.com/mahmoudibrahim/genesorteR.

scholarly journals A Markov Random Field Model for Network-based Differential Expression Analysis of Single-cell RNA-seq Data

2020 ◽  
Author(s):  
Hongyu Li ◽  
Zhichao Xu ◽  
Taylor Adams ◽  
Naftali Kaminski ◽  
Hongyu Zhao
Keyword(s):  
Random Field ◽  
Single Cell ◽  
Differential Expression ◽  
Markov Random Field ◽  
Statistical Power ◽  
Differential Expression Analysis ◽  
Cell Types ◽  
Cell Type ◽  
Markov Random ◽  
Cell Type Specific

Abstract Background: Recent development of single cell sequencing technologies has made it possible to identify genes with different expression (DE) levels at the cell type level between different groups of samples. However, the often-low sample size of single cell data limits the statistical power to identify DE genes. In this article, we propose to borrow information through known biological networks. Results: We develop MRFscRNAseq, which is based on a Markov Random Field (MRF) model to appropriately accommodate gene network information as well as dependencies among cell types to identify cell-type specific DE genes. We implement an Expectation-Maximization (EM) algorithm with mean field-like approximation to estimate model parameters and a Gibbs sampler to infer DE status. Simulation study shows that our method has better power to detect cell-type specific DE genes than conventional methods while appropriately controlling type I error rate. The usefulness of our method is demonstrated through its application to study the pathogenesis and biological processes of idiopathic pulmonary fibrosis (IPF) using a single-cell RNA-sequencing (scRNA-seq) data set, which contains 18,150 protein-coding genes across 38 cell types on lung tissues from 32 IPF patients and 28 normal controls.Conclusions: The proposed MRF model is implemented in the R package MRFscRNAseq available on GitHub. By utilizing gene-gene and cell-cell networks, our method provides differential expression analysis for scRNA-seq data with increased statistical power.

scholarly journals scRMD: imputation for single cell RNA-seq data via robust matrix decomposition

2020 ◽  
Vol 36 (10) ◽  
pp. 3156-3161 ◽  
Author(s):  
Chong Chen ◽  
Changjing Wu ◽  
Linjie Wu ◽  
Xiaochen Wang ◽  
Minghua Deng ◽  
...  
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Matrix Decomposition ◽  
Transcriptome Profiling ◽  
R Package ◽  
Supplementary Information ◽  
Downstream Analysis

Abstract Motivation Single cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant increase of noises, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values can be beneficial in scRNA-seq data analysis. Results In this article, we model the dropout imputation problem as robust matrix decomposition. This model has minimal assumptions and allows us to develop a computational efficient imputation method called scRMD. Extensive data analysis shows that scRMD can accurately recover the dropout values and help to improve downstream analysis such as differential expression analysis and clustering analysis. Availability and implementation The R package scRMD is available at https://github.com/XiDsLab/scRMD. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals netSmooth: Network-smoothing based imputation for single cell RNA-seq

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 8 ◽  
Author(s):  
Jonathan Ronen ◽  
Altuna Akalin
Keyword(s):  
Single Cell ◽  
Time Course ◽  
Missing Values ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Covariance Structure ◽  
R Package ◽  
Rna Seq ◽  
Distinct Cell

Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https://github.com/BIMSBbioinfo/netSmooth.

scholarly journals glmGamPoi: Fitting Gamma-Poisson Generalized Linear Models on Single Cell Count Data

2020 ◽  
Author(s):  
Constantin Ahlmann-Eltze ◽  
Wolfgang Huber
Keyword(s):  
Single Cell ◽  
Poisson Distribution ◽  
Generalized Linear Models ◽  
Count Data ◽  
Linear Models ◽  
Differential Expression Analysis ◽  
Source Code ◽  
Principal Component ◽  
R Package ◽  
Single Cell Rna Sequencing

Abstract Motivation The Gamma-Poisson distribution is a theoretically and empirically motivated model for the sampling variability of single cell RNA-sequencing counts (Grün et al., 2014; Svensson, 2020; Silverman et al., 2018; Hafemeister and Satija, 2019) and an essential building block for analysis approaches including differential expression analysis (Robinson et al., 2010; McCarthy et al., 2012; Anders and Huber, 2010; Love et al., 2014), principal component analysis (Townes et al., 2019) and factor analysis (Risso et al., 2018). Existing implementations for inferring its parameters from data often struggle with the size of single cell datasets, which can comprise millions of cells; at the same time, they do not take full advantage of the fact that zero and other small numbers are frequent in the data. These limitations have hampered uptake of the model, leaving room for statistically inferior approaches such as logarithm(-like) transformation. Results We present a new R package for fitting the Gamma-Poisson distribution to data with the characteristics of modern single cell datasets more quickly and more accurately than existing methods. The software can work with data on disk without having to load them into RAM simultaneously. Availability The package glmGamPoi is available from Bioconductor for Windows, macOS, and Linux, and source code is available on github.com/const-ae/glmGamPoi under a GPL-3 license.

scholarly journals TMExplorer: A Tumour Microenvironment Single-cell RNAseq Database and Search Tool

2020 ◽  
Author(s):  
Erik Christensen ◽  
Alaine Naidas ◽  
Mia Husic ◽  
Parisa Shooshtari
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
R Package ◽  
Tumour Microenvironment ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Stromal Fibroblasts ◽  
Search Tool ◽  
Cancer Types ◽  
Easy Integration

ABSTRACTTumour microenvironments (TME) contain a variety of cells including but not limited to stromal fibroblasts, endothelial cells, immune cells, malignant cells, and cells of the tissues of origin, whose interactions likely influence tumour behaviour and response to cancer treatment. The specific composition of the TME can be elucidated using single-cell RNA sequencing (scRNA-seq) by measuring expression profiles of individual cells. Several scRNA-seq datasets from multiple cancer types have been published in recent years, yet we still lack a comprehensive database for the collection and presentation of TME data from these studies in an easily accessible format. We have thus built a database of TME scRNA-seq data, containing 21 TME scRNA-seq datasets from 12 different cancer types. We have also created an R package called TMExplorer, which provides an interface to easily search and access all available datasets and their metadata. Data and metadata are kept in a consistent format across all datasets, with multiple expression formats available depending on the use case. Users can view a table of metadata and select individual datasets or filter them by specific characteristics. Users may also select a specific type of cancer and view all published scRNA-seq data for that cancer type available in our database. Users are provided with an option to save the data in multiple formats in order to view or process it outside of R. Thus, the TMExplorer database and search tool allows for thorough examination of the TME using scRNA-seq in a way that is streamlined and allows for easy integration into already existing scRNA-seq analysis pipelines.

scholarly journals Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes

2020 ◽  
Author(s):  
Dustin J. Sokolowski ◽  
Mariela Faykoo-Martinez ◽  
Lauren Erdman ◽  
Huayun Hou ◽  
Cadia Chan ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Cell Types ◽  
R Package ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Kidney Regeneration ◽  
Cell Type ◽  
User Friendly

AbstractRNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by integrating cell-type expression data generated by scRNA-seq and existing deconvolution methods. After benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. We found that scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small proportion of immune cells. While scMappR can work with any user supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its use with bulk RNA-seq data alone. Overall, scMappR is a user-friendly R package that complements traditional differential expression analysis available at CRAN.HighlightsscMappR integrates scRNA-seq and bulk RNA-seq to re-calibrate bulk differentially expressed genes (DEGs).scMappR correctly identified immune-cell expressed DEGs from a bulk RNA-seq analysis of mouse kidney regeneration.scMappR is deployed as a user-friendly R package available at CRAN.

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