scholarly journals Shrinkage of dispersion parameters in the binomial family, with application to differential exon skipping

2014 ◽  
Author(s):  
Sean Ruddy ◽  
Marla Johnson ◽  
Elizabeth Purdom
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Empirical Bayes ◽  
Simulated Data ◽  
Exon Skipping ◽  
Expression Data ◽  
Weighted Likelihood ◽  
Sequencing Data ◽  
Dispersion Parameters ◽  
Per Gene

The prevalence of sequencing experiments in genomics has led to an increased use of methods for count data in analyzing high-throughput genomic data to perform analyses. The importance of shrinkage methods in improving the performance of statistical methods remains. A common example is that of gene expression data, where the counts per gene are often modeled as some form of an over-dispersed Poisson. In this case, shrinkage estimates of the per-gene dispersion parameter have led to improved estimation of dispersion in the case of a small number of samples. We address a different count setting introduced by the use of sequencing data: comparing differential proportional usage via an over-dispersed binomial model. This is motivated by our interest in testing for differential exon skipping in mRNA-Seq experiments. We introduce a novel method that is developed by modeling the dispersion based on the double binomial distribution proposed by Efron (1986). Our method (WEB-Seq) is an empirical bayes strategy for producing a shrunken estimate of dispersion and effectively detects differential proportional usage, and has close ties to the weighted-likelihood strategy of edgeR developed for gene expression data (Robinson and Smyth, 2007; Robinson et al., 2010). We analyze its behavior on simulated data sets as well as real data and show that our method is fast, powerful and gives accurate control of the FDR compared to alternative approaches. We provide implementation of our methods in the R package DoubleExpSeq available on CRAN.

scholarly journals LSTrAP-Crowd: Prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data

2020 ◽  
Author(s):  
Benedict Hew ◽  
Qiao Wen Tan ◽  
William Goh ◽  
Jonathan Wei Xiong Ng ◽  
Kenny Koh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Synthesis ◽  
Rna Sequencing ◽  
Gene Expression Data ◽  
Large Scale ◽  
Bacterial Resistance ◽  
Expression Data ◽  
Sequencing Data ◽  
Novel Proteins ◽  
Novel Antibiotics

AbstractBacterial resistance to antibiotics is a growing problem that is projected to cause more deaths than cancer in 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the bacterial ribosomes, proteins that are involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. The data can be used to identify other vulnerabilities or bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowdsourced.

scholarly journals Probabilistic modeling of bifurcations in single-cell gene expression data using a Bayesian mixture of factor analyzers

2017 ◽  
Vol 2 ◽  
pp. 19 ◽  
Author(s):  
Kieran R Campbell ◽  
Christopher Yau
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Empirical Bayes ◽  
Probabilistic Approach ◽  
Monte Carlo Sampling ◽  
Expression Data ◽  
Bioinformatics Analyses ◽  
Cell Gene Expression ◽  
Cell Gene

Modeling bifurcations in single-cell transcriptomics data has become an increasingly popular field of research. Several methods have been proposed to infer bifurcation structure from such data, but all rely on heuristic non-probabilistic inference. Here we propose the first generative, fully probabilistic model for such inference based on a Bayesian hierarchical mixture of factor analyzers. Our model exhibits competitive performance on large datasets despite implementing full Markov-Chain Monte Carlo sampling, and its unique hierarchical prior structure enables automatic determination of genes driving the bifurcation process. We additionally propose an Empirical-Bayes like extension that deals with the high levels of zero-inflation in single-cell RNA-seq data and quantify when such models are useful. We apply or model to both real and simulated single-cell gene expression data and compare the results to existing pseudotime methods. Finally, we discuss both the merits and weaknesses of such a unified, probabilistic approach in the context practical bioinformatics analyses.

Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Guro Dørum ◽  
Lars Snipen ◽  
Margrete Solheim ◽  
Solve Saebo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Simulated Data ◽  
Real Data ◽  
Biological Knowledge ◽  
Expression Data ◽  
Data Set ◽  
Gene Set ◽  
Network Information

Gene set analysis methods have become a widely used tool for including prior biological knowledge in the statistical analysis of gene expression data. Advantages of these methods include increased sensitivity, easier interpretation and more conformity in the results. However, gene set methods do not employ all the available information about gene relations. Genes are arranged in complex networks where the network distances contain detailed information about inter-gene dependencies. We propose a method that uses gene networks to smooth gene expression data with the aim of reducing the number of false positives and identify important subnetworks. Gene dependencies are extracted from the network topology and are used to smooth genewise test statistics. To find the optimal degree of smoothing, we propose using a criterion that considers the correlation between the network and the data. The network smoothing is shown to improve the ability to identify important genes in simulated data. Applied to a real data set, the smoothing accentuates parts of the network with a high density of differentially expressed genes.

scholarly journals Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data

2020 ◽  
Author(s):  
Shaoheng Liang ◽  
Jinzhuang Dou ◽  
Ramiz Iqbal ◽  
Ken Chen
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Simulated Data ◽  
Batch Effect ◽  
Mouse Retina ◽  
Expression Data ◽  
Retina Development ◽  
Cell Gene Expression ◽  
Cell Gene

AbstractClustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. Batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Batch-Corrected Distance (BCD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate BCD on a simulated data as well as applied it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). BCD achieves more accurate clusters and better visualizations than state-of-the-art batch correction methods on longitudinal datasets. BCD can be directly integrated with most clustering and visualization methods to enable more scientific findings.

scholarly journals graphsim: An R package for simulating gene expression data from graph structures of biological pathways

2020 ◽  
Author(s):  
S. Thomas Kelly ◽  
Michael A. Black
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Simulated Data ◽  
R Package ◽  
Biological Pathways ◽  
Graph Structure ◽  
Expression Data

SummaryTranscriptomic analysis is used to capture the molecular state of a cell or sample in many biological and medical applications. In addition to identifying alterations in activity at the level of individual genes, understanding changes in the gene networks that regulate fundamental biological mechanisms is also an important objective of molecular analysis. As a result, databases that describe biological pathways are increasingly uesad to assist with the interpretation of results from large-scale genomics studies. Incorporating information from biological pathways and gene regulatory networks into a genomic data analysis is a popular strategy, and there are many methods that provide this functionality for gene expression data. When developing or comparing such methods, it is important to gain an accurate assessment of their performance. Simulation-based validation studies are frequently used for this. This necessitates the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This procedure has been released as the graphsim R package on CRAN and GitHub (https://github.com/TomKellyGenetics/graphsim) and is compatible with any graph structure that can be described using the igraph package. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression.

scholarly journals A Robust Procedure for Machine Learning Algorithms Using Gene Expression Data

2021 ◽  
Vol 12 (2) ◽  
pp. 2422-2439
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Gene Expression Data ◽  
Learning Algorithms ◽  
Simulated Data ◽  
Cancer Classification ◽  
Machine Learning Algorithms ◽  
Expression Data ◽  
Traditional Procedure

Cancer classification is one of the main objectives for analyzing big biological datasets. Machine learning algorithms (MLAs) have been extensively used to accomplish this task. Several popular MLAs are available in the literature to classify new samples into normal or cancer populations. Nevertheless, most of them often yield lower accuracies in the presence of outliers, which leads to incorrect classification of samples. Hence, in this study, we present a robust approach for the efficient and precise classification of samples using noisy GEDs. We examine the performance of the proposed procedure in a comparison of the five popular traditional MLAs (SVM, LDA, KNN, Naïve Bayes, Random forest) using both simulated and real gene expression data analysis. We also considered several rates of outliers (10%, 20%, and 50%). The results obtained from simulated data confirm that the traditional MLAs produce better results through our proposed procedure in the presence of outliers using the proposed modified datasets. The further transcriptome analysis found the significant involvement of these extra features in cancer diseases. The results indicated the performance improvement of the traditional MLAs with our proposed procedure. Hence, we propose to apply the proposed procedure instead of the traditional procedure for cancer classification.

scholarly journals An empirical Bayes approach for analysis of diverse periodic trends in time-course gene expression data

2012 ◽  
Vol 29 (2) ◽  
pp. 182-188 ◽  
Author(s):  
Mehmet Kocak ◽  
E. Olusegun George ◽  
Saumyadipta Pyne ◽  
Stanley Pounds
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Empirical Bayes ◽  
Time Course ◽  
Expression Data ◽  
Empirical Bayes Approach ◽  
Bayes Approach

NCMP-04. BRAIN-UMAP: THE GENETIC INTERSECTION BETWEEN NEUROSCIENCE, NEUROLOGY, PSYCHIATRY, AND ONCOLOGY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi148-vi148
Author(s):  
Sonali Arora ◽  
Nicholas Nuechterlein ◽  
Siobhan Pattwell ◽  
Eric Holland
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Brain Regions ◽  
Human Diseases ◽  
Next Generation Sequencing Data ◽  
Normal Brain ◽  
Expression Data ◽  
Sequencing Data ◽  
Insight Into ◽  
Genome Atlas

Abstract Whole transcriptome sequencing (RNA-seq) is an important tool for understanding genetic mechanisms underlying human diseases and gaining a better insight into complex human diseases. Several ground-breaking projects have uniformly processed RNASeq data from publicly available studies to enable cross-comparison. One noteworthy study is the recount2 pipeline, which in 2017, has reprocessed ~70,0000 samples from Short Read Archive(SRA), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx). This vast dataset also includes gene expression data for GTEx-defined brain regions, neurological and psychiatric disorders (such as Parkinson's, Alzheimer’s, Huntington’s) and gliomas (such as TCGA, Chinese Glioma Genome Atlas (CGGA)). We apply uniform manifold approximation and projection (UMAP), a non-linear dimension reduction tool, to bulk gene expression data from brain-related diseases to build a BRAIN-UMAP, which allows for visualization of gene expression profiles across datasets. This UMAP shows that while gliomas form a distinct cluster, the neurological and psychiatric diseases are similar to GTEX-defined normal brain regions which exhibit tissue-specific profiles and patterns. Incorporating gliomas from various publicly available datasets also allows for the ability to observe unique clustering of particular subtypes, which can increase our genetic understanding of the disease. We also present a resource where researchers interested in mechanisms, can easily compare, and contrast the expression of a given gene and/or pathway of interest across various diseases, gliomas, and normal brain regions. Our current study, focusing on brain related diseases, offers insight into what may be possible for the broader neuroscientific community if we continually reprocess newly available brain related RNASeq samples using recount2. Additionally, if we build similar uniformly processing pipelines for other kinds of next-generation sequencing data, we would be able to use multi-omic sequencing data to find novel associations between biological entities and increase our mechanistic knowledge of the disease.

scholarly journals A comparative study of biomarker gene selection methods in presence of outliers

2018 ◽  
Vol 25 ◽  
pp. 9-16
Author(s):  
M Shahjaman ◽  
N Kumar ◽  
AA Begum ◽  
SMS Islam ◽  
MNH Mollah
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Simulated Data ◽  
Small Sample ◽  
Data Sets ◽  
Expression Data ◽  
Selection Methods ◽  
Cancer Data

The main purpose of gene expression data analysis is to identify the biomarker genes by comparing the gene expression levels between two different groups or conditions. There are several methods to select biomarker genes and many comparative studies have been performed to select the appropriate method. However, they did not consider the problems of outliers in their data sets though it is very essential to select the method from robustness point of view due to outliers may occur in the different steps of the gene expression data generating process. In this paper, it is evaluated the performance among five popular statistical biomarker gene selection methods viz. T-test, SAM, LIMMA, KW and FCROS using both simulated and real gene expression data sets in absence and presence of outliers. In the simulated data analysis, it was demonstrated the performance of these methods in terms of different performance measures such as TPR, TNR, FPR, FNR and AUC and based on these measures, it was found that in absence of outliers, for both small-and-large sample cases all the methods perform almost similar. Whereas, in presence of outliers, for small-sample case only the FCROS method perform well than other methods. From a real colon cancer data analysis, it was elucidated that FCROS method identified additional 59 genes that were not detected by the other methods and most of them belongs to the different cancer related pathways.J. bio-sci. 25: 9-16, 2017

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