scholarly journals Error estimates for the analysis of differential expression from RNA-seq count data

2014 ◽  
Author(s):  
Conrad Burden ◽  
Sumaira Qureshi ◽  
Susan R Wilson
Keyword(s):  
Differential Expression ◽  
Count Data ◽  
Statistical Models ◽  
Full Range ◽  
Synthetic Data ◽  
Biological Data ◽  
P Value ◽  
Sequencing Data ◽  
False Discovery Rates ◽  
Poisson Data

A number of algorithms exist for analysing RNA-sequencing data to infer profiles of differential gene expression. Problems inherent in building algorithms around statistical models of over dispersed count data are formidable and frequently lead to non-uniform p-value distributions for null-hypothesis data and to inaccurate estimates of false discovery rates (FDRs). This can lead to an inaccurate measure of significance and loss of power to detect differential expression. We use synthetic and real biological data to assess the ability of several available R packages to accurately estimate FDRs. The packages surveyed are based on statistical models of overdispersed Poisson data and include edgeR, DESeq, DESeq2, PoissonSeq and QuasiSeq. Also tested is an add-on package to edgeR and DESeq which we introduce called Polyfit. Polyfit aims to address the problem of a non-uniform null p-value distribution for two-class datasets by adapting the Storey-Tibshirani procedure. We find the best performing package in the sense that it achieves a low FDR which is accurately estimated over the full range of p-values, albeit with a very slow run time, is the QLSpline implementation of QuasiSeq. This finding holds provided the number of biological replicates in each condition is at least 4. The next best performing packages are edgeR and DESeq2. When the number of biological replicates is sufficiently high, and within a range accessible to multiplexed experimental designs, the Polyfit extension improves the performance DESeq (for approximately 6 or more replicates per condition), making its performance comparable with that of edgeR and DESeq2 in our tests with synthetic data.

scholarly journals Error estimates for the analysis of differential expression from RNA-seq count data

2014 ◽  
Author(s):  
Conrad Burden ◽  
Sumaira Qureshi ◽  
Susan R Wilson
Keyword(s):  
Differential Expression ◽  
Count Data ◽  
Statistical Models ◽  
Full Range ◽  
Synthetic Data ◽  
Biological Data ◽  
P Value ◽  
Sequencing Data ◽  
False Discovery Rates ◽  
Poisson Data

A number of algorithms exist for analysing RNA-sequencing data to infer profiles of differential gene expression. Problems inherent in building algorithms around statistical models of over dispersed count data are formidable and frequently lead to non-uniform p-value distributions for null-hypothesis data and to inaccurate estimates of false discovery rates (FDRs). This can lead to an inaccurate measure of significance and loss of power to detect differential expression. We use synthetic and real biological data to assess the ability of several available R packages to accurately estimate FDRs. The packages surveyed are based on statistical models of overdispersed Poisson data and include edgeR, DESeq, DESeq2, PoissonSeq and QuasiSeq. Also tested is an add-on package to edgeR and DESeq which we introduce called Polyfit. Polyfit aims to address the problem of a non-uniform null p-value distribution for two-class datasets by adapting the Storey-Tibshirani procedure. We find the best performing package in the sense that it achieves a low FDR which is accurately estimated over the full range of p-values, albeit with a very slow run time, is the QLSpline implementation of QuasiSeq. This finding holds provided the number of biological replicates in each condition is at least 4. The next best performing packages are edgeR and DESeq2. When the number of biological replicates is sufficiently high, and within a range accessible to multiplexed experimental designs, the Polyfit extension improves the performance DESeq (for approximately 6 or more replicates per condition), making its performance comparable with that of edgeR and DESeq2 in our tests with synthetic data.

scholarly journals Error estimates for the analysis of differential expression from RNA-seq count data

2014 ◽  
Author(s):  
Conrad Burden ◽  
Sumaira Qureshi ◽  
Susan R Wilson
Keyword(s):  
Differential Expression ◽  
Count Data ◽  
Statistical Models ◽  
Full Range ◽  
Synthetic Data ◽  
Biological Data ◽  
P Value ◽  
Sequencing Data ◽  
False Discovery Rates ◽  
Poisson Data

A number of algorithms exist for analysing RNA-sequencing data to infer profiles of differential gene expression. Problems inherent in building algorithms around statistical models of over dispersed count data are formidable and frequently lead to non-uniform p-value distributions for null-hypothesis data and to inaccurate estimates of false discovery rates (FDRs). This can lead to an inaccurate measure of significance and loss of power to detect differential expression. We use synthetic and real biological data to assess the ability of several available R packages to accurately estimate FDRs. The packages surveyed are based on statistical models of overdispersed Poisson data and include edgeR, DESeq, DESeq2, PoissonSeq and QuasiSeq. Also tested is an add-on package to edgeR and DESeq which we introduce called Polyfit. Polyfit aims to address the problem of a non-uniform null p-value distribution for two-class datasets by adapting the Storey-Tibshirani procedure. We find the best performing package in the sense that it achieves a low FDR which is accurately estimated over the full range of p-values to be the QLSpline implementation of QuasiSeq. This finding holds provided the number of biological replicates in each condition is at least 4. The next best performing packages are edgeR and DESeq. When the number of biological replicates is sufficiently high, and within a range accessible to multiplexed experimental designs, the Polyfit extension improves the performance edgeR (for ≤ \le 10 replicates per condition) or DESeq (for ≤ \le 6 replicates per condition) in our tests with synthetic data.

scholarly journals Error estimates for the analysis of differential expression from RNA-seq count data

2014 ◽  
Author(s):  
Conrad Burden ◽  
Sumaira Qureshi ◽  
Susan R Wilson
Keyword(s):  
Differential Expression ◽  
Count Data ◽  
Statistical Models ◽  
Full Range ◽  
Synthetic Data ◽  
Biological Data ◽  
P Value ◽  
Sequencing Data ◽  
False Discovery Rates ◽  
Poisson Data

A number of algorithms exist for analysing RNA-sequencing data to infer profiles of differential gene expression. Problems inherent in building algorithms around statistical models of over dispersed count data are formidable and frequently lead to non-uniform p-value distributions for null-hypothesis data and to inaccurate estimates of false discovery rates (FDRs). This can lead to an inaccurate measure of significance and loss of power to detect differential expression. We use synthetic and real biological data to assess the ability of several available R packages to accurately estimate FDRs. The packages surveyed are based on statistical models of overdispersed Poisson data and include edgeR, DESeq, DESeq2, PoissonSeq and QuasiSeq. Also tested is an add-on package to edgeR and DESeq which we introduce called Polyfit. Polyfit aims to address the problem of a non-uniform null p-value distribution for two-class datasets by adapting the Storey-Tibshirani procedure. We find the best performing package in the sense that it achieves a low FDR which is accurately estimated over the full range of p-values, albeit with a very slow run time, is the QLSpline implementation of QuasiSeq. This finding holds provided the number of biological replicates in each condition is at least 4. The next best performing packages are edgeR and DESeq2. When the number of biological replicates is sufficiently high, and within a range accessible to multiplexed experimental designs, the Polyfit extension improves the performance DESeq (for approximately 6 or more replicates per condition), making its performance comparable with that of edgeR and DESeq2 in our tests with synthetic data.

scholarly journals EBSeq: improving mixing computations for multi-group differential expression analysis

2020 ◽  
Author(s):  
Xiuyu Ma ◽  
Christina Kendziorski ◽  
Michael A. Newton
Keyword(s):  
Differential Expression ◽  
Clustering Algorithms ◽  
Differential Expression Analysis ◽  
Synthetic Data ◽  
Data Sets ◽  
Empirical Bayesian ◽  
False Discovery Rates ◽  
Data Analyst ◽  
False Discovery ◽  
Multiple Sample

ABSTRACTEBSeq is a Bioconductor package designed to calculate empirical-Bayesian inference summaries from sequence-based gene-expression (RNA-Seq) data. It produces gene or isoform-specific scores that measure various patterns of differential expression among a set of sample groups, and is most commonly deployed to measure differential expression between two groups. Its use of local posterior probabilities from a fitted mixture model provides the data analyst a direct way to score the false discovery rate of any reported list of genes, and it is one of the only tools that can address local false discovery rates when analyzing multiple sample groups. Contemporary applications have increasing numbers of sample groups, and the algorithms deployed in EBSeq are neither space nor time efficient in this important case. We describe a version update utilizing code improvements and novel pruning and clustering algorithms in order to reduce the complexity of mixture computations. The algorithms are supported by a theoretical analysis and tested empirically on a variety of benchmark and synthetic data sets.

scholarly journals A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data

2015 ◽  
Author(s):  
Amanda J Lea ◽  
Jenny Tung ◽  
Xiang Zhou
Keyword(s):  
Dna Methylation ◽  
Count Data ◽  
Mixed Model ◽  
Bisulfite Sequencing ◽  
Structured Populations ◽  
P Value ◽  
Sequencing Data ◽  
Data Set ◽  
Age Related ◽  
Bisulfite Sequencing Data

Identifying sources of variation in DNA methylation levels is important for understanding gene regulation. Recently, bisulfite sequencing has become a popular tool for investigating DNA methylation levels. However, modeling bisulfite sequencing data is complicated by dramatic variation in coverage across sites and individual samples, and because of the computational challenges of controlling for genetic covariance in count data. To address these challenges, we present a binomial mixed model and an efficient, sampling-based algorithm (MACAU: Mixed model association for count data via data augmentation) for approximate parameter estimation and p-value computation. This framework allows us to simultaneously account for both the over-dispersed, count-based nature of bisulfite sequencing data, as well as genetic relatedness among individuals. Using simulations and two real data sets (whole genome bisulfite sequencing (WGBS) data from Arabidopsis thaliana and reduced representation bisulfite sequencing (RRBS) data from baboons), we show that our method provides well-calibrated test statistics in the presence of population structure. Further, it improves power to detect differentially methylated sites: in the RRBS data set, MACAU detected 1.6-fold more age-associated CpG sites than a beta-binomial model (the next best approach). Changes in these sites are consistent with known age-related shifts in DNA methylation levels, and are enriched near genes that are differentially expressed with age in the same population. Taken together, our results indicate that MACAU is an efficient, effective tool for analyzing bisulfite sequencing data, with particular salience to analyses of structured populations. MACAU is freely available at www.xzlab.org/software.html.

scholarly journals Analysis of Single-Cell RNA-Sequencing Data: A Step-by-Step Guide

2021 ◽  
Vol 2 (1) ◽  
pp. 43-61
Author(s):  
Aanchal Malhotra ◽  
Samarendra Das ◽  
Shesh N. Rai
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Count Data ◽  
Negative Binomial ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Sequencing Data ◽  
Zero Inflation ◽  
Single Cell Rna Sequencing

Single-cell RNA-sequencing (scRNA-seq) technology provides an excellent platform for measuring the expression profiles of genes in heterogeneous cell populations. Multiple tools for the analysis of scRNA-seq data have been developed over the years. The tools require complicated commands and steps to analyze the underlying data, which are not easy to follow by genome researchers and experimental biologists. Therefore, we describe a step-by-step workflow for processing and analyzing the scRNA-seq unique molecular identifier (UMI) data from Human Lung Adenocarcinoma cell lines. We demonstrate the basic analyses including quality check, mapping and quantification of transcript abundance through suitable real data example to obtain UMI count data. Further, we performed basic statistical analyses, such as zero-inflation, differential expression and clustering analyses on the obtained count data. We studied the effects of excess zero-inflation present in scRNA-seq data on the downstream analyses. Our findings indicate that the zero-inflation associated with UMI data had no or minimal role in clustering, while it had significant effect on identifying differentially expressed genes. We also provide an insight into the comparative analysis for differential expression analysis tools based on zero-inflated negative binomial and negative binomial models on scRNA-seq data. The sensitivity analysis enhanced our findings in that the negative binomial model-based tool did not provide an accurate and efficient way to analyze the scRNA-seq data. This study provides a set of guidelines for the users to handle and analyze real scRNA-seq data more easily.

scholarly journals Differential expression and correlation analysis of miRNA–mRNA profiles in swine testicular cells infected with porcine epidemic diarrhea virus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoqian Zhang ◽  
Chang Li ◽  
Bingzhou Zhang ◽  
Zhonghua Li ◽  
Wei Zeng ◽  
...  
Keyword(s):  
Differential Expression ◽  
Porcine Epidemic Diarrhea Virus ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Bacterial Invasion ◽  
Sequencing Data ◽  
Diarrhea Virus ◽  
Comparison Groups ◽  
Porcine Epidemic Diarrhea

AbstractThe variant virulent porcine epidemic diarrhea virus (PEDV) strain (YN15) can cause severe porcine epidemic diarrhea (PED); however, the attenuated vaccine-like PEDV strain (YN144) can induce immunity in piglets. To investigate the differences in pathogenesis and epigenetic mechanisms between the two strains, differential expression and correlation analyses of the microRNA (miRNA) and mRNA in swine testicular (ST) cells infected with YN15, YN144, and mock were performed on three comparison groups (YN15 vs Control, YN144 vs Control, and YN15 vs YN144). The mRNA and miRNA expression profiles were obtained using next-generation sequencing (NGS), and the differentially expressed (DE) (p-value < 0.05) mRNA and miRNA were obtained using DESeq R package. mRNAs targeted by DE miRNAs were predicted using the miRanda algortithm. 8039, 8631 and 3310 DE mRNAs, and 36, 36, and 22 DE miRNAs were identified in the three comparison groups, respectively. 14,140, 15,367 and 3771 DE miRNA–mRNA (targeted by DE miRNAs) interaction pairs with negatively correlated expression patterns were identified, and interaction networks were constructed using Cytoscape. Six DE miRNAs and six DE mRNAs were randomly selected to verify the sequencing data by real-time relative quantitative reverse transcription polymerase chain reaction (qRT-PCR). Based on bioinformatics analysis, we discovered the differences were mostly involved in host immune responses and viral pathogenicity, including NF-κB signaling pathway and bacterial invasion of epithelial cells, etc. This is the first comprehensive comparison of DE miRNA–mRNA pairs in YN15 and YN144 infection in vitro, which could provide novel strategies for the prevention and control of PED.

A zero-inflated non-negative matrix factorization for the deconvolution of mixed signals of biological data

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yixin Kong ◽  
Ariangela Kozik ◽  
Cindy H. Nakatsu ◽  
Yava L. Jones-Hall ◽  
Hyonho Chun
Keyword(s):  
Matrix Factorization ◽  
Factor Model ◽  
R Package ◽  
Biological Data ◽  
Superior Performance ◽  
Sequencing Data ◽  
Fecal Microbiome ◽  
Brain Gene Expression ◽  
Cell Transcriptome ◽  
Non Negative Matrix Factorization

Abstract A latent factor model for count data is popularly applied in deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the accuracy of the estimates could be much improved. However, the advantage quickly disappears in the presence of excessive zeros. To correctly account for this phenomenon in both mixed and pure samples, we propose a zero-inflated non-negative matrix factorization and derive an effective multiplicative parameter updating rule. In simulation studies, our method yielded the smallest bias. We applied our approach to brain gene expression as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF.

scholarly journals Dynamic model updating (DMU) approach for statistical learning model building with missing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rahi Jain ◽  
Wei Xu
Keyword(s):  
Missing Data ◽  
Dynamic Model ◽  
Statistical Models ◽  
Missing Values ◽  
Model Building ◽  
Model Updating ◽  
Biological Data ◽  
Bayesian Regression ◽  
Biological Research ◽  
Original Dataset

Abstract Background Developing statistical and machine learning methods on studies with missing information is a ubiquitous challenge in real-world biological research. The strategy in literature relies on either removing the samples with missing values like complete case analysis (CCA) or imputing the information in the samples with missing values like predictive mean matching (PMM) such as MICE. Some limitations of these strategies are information loss and closeness of the imputed values with the missing values. Further, in scenarios with piecemeal medical data, these strategies have to wait to complete the data collection process to provide a complete dataset for statistical models. Method and results This study proposes a dynamic model updating (DMU) approach, a different strategy to develop statistical models with missing data. DMU uses only the information available in the dataset to prepare the statistical models. DMU segments the original dataset into small complete datasets. The study uses hierarchical clustering to segment the original dataset into small complete datasets followed by Bayesian regression on each of the small complete datasets. Predictor estimates are updated using the posterior estimates from each dataset. The performance of DMU is evaluated by using both simulated data and real studies and show better results or at par with other approaches like CCA and PMM. Conclusion DMU approach provides an alternative to the existing approaches of information elimination and imputation in processing the datasets with missing values. While the study applied the approach for continuous cross-sectional data, the approach can be applied to longitudinal, categorical and time-to-event biological data.

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