scholarly journals A comparative study of deconvolution methods for RNA-seq data under a dynamic testing landscape

2020 ◽  
Author(s):  
Haijing Jin ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Dynamic Testing ◽  
Cell Types ◽  
Future Application ◽  
Expression Data ◽  
Rna Seq ◽  
Biological Factors ◽  
The Impact ◽  
Unit Component

AbstractDeconvolution analyses have been widely used to track compositional alternations of cell-types in gene expression data. Even though numerous novel methods have been developed in recent years, researchers are still having difficulty selecting optimal deconvolution methods due to the lack of comprehensive benchmarks relative to the newly developed methods. To systematically reveal the pitfalls and challenges of deconvolution analyses, we studied the impact of several technical and biological factors such as simulation model, quantification unit, component number, weight matrix, and unknown content by constructing three benchmarking frameworks that cover comparative analysis of 11 popular deconvolution methods under 1,766 conditions. We hope this study can provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.

scholarly journals A benchmark for RNA-seq deconvolution analysis under dynamic testing environments

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Haijing Jin ◽  
Zhandong Liu
Keyword(s):  
Dynamic Testing ◽  
Cell Types ◽  
Future Application ◽  
Expression Data ◽  
Rna Seq ◽  
Deconvolution Analysis ◽  
Tuning Parameters ◽  
Testing Environments ◽  
The Impact ◽  
Unit Component

Abstract Background Deconvolution analyses have been widely used to track compositional alterations of cell types in gene expression data. Although a large number of novel methods have been developed, due to a lack of understanding of the effects of modeling assumptions and tuning parameters, it is challenging for researchers to select an optimal deconvolution method suitable for the targeted biological conditions. Results To systematically reveal the pitfalls and challenges of deconvolution analyses, we investigate the impact of several technical and biological factors including simulation model, quantification unit, component number, weight matrix, and unknown content by constructing three benchmarking frameworks. These frameworks cover comparative analysis of 11 popular deconvolution methods under 1766 conditions. Conclusions We provide new insights to researchers for future application, standardization, and development of deconvolution tools on RNA-seq data.

scholarly journals Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Kayla A. Johnson ◽  
Arjun Krishnan
Keyword(s):  
Gene Expression ◽  
Expression Data ◽  
Rna Seq ◽  
Functional Relationships ◽  
Gene Coexpression ◽  
Transformation Methods ◽  
Network Transformation ◽  
Almost All ◽  
Coexpression Networks ◽  
The Impact

Abstract Background Constructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks, including good choices for data pre-processing, normalization, and network transformation, have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing and normalization methods for RNA-seq focus on the end goal of determining differential gene expression. Results Here, we present a comprehensive benchmarking and analysis of 36 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We test these workflows on both large, homogenous datasets and small, heterogeneous datasets from various labs. We analyze the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with counts adjusted by size factors producing networks that most accurately recapitulate known tissue-naive and tissue-aware gene functional relationships. Conclusions Based on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/RNAseq_coexpression to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

scholarly journals CDSeqR: fast complete deconvolution for gene expression data from bulk tissues

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kai Kang ◽  
Caizhi Huang ◽  
Yuanyuan Li ◽  
David M. Umbach ◽  
Leping Li
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Biological Tissues ◽  
Specific Gene ◽  
Specific Cell ◽  
Specific Information ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Type ◽  
Cell Type Specific

Abstract Background Biological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and an added new function to aid cell type annotation. The R package would be of interest for the broader R community. Result We developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. Conclusions The existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell–cell interactions in the tissue microenvironment. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.

scholarly journals Integrative single-cell and bulk RNA-seq analysis in human retina identified cell type-specific composition and gene expression changes for age-related macular degeneration

2019 ◽  
Author(s):  
Yafei Lyu ◽  
Randy Zauhar ◽  
Nico Dana ◽  
Christianne E. Strang ◽  
Kui Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Macular Degeneration ◽  
Single Cell ◽  
Cell Types ◽  
Age Related Macular Degeneration ◽  
Peripheral Retina ◽  
Rna Seq ◽  
Cell Type ◽  
Age Related ◽  
The Impact

Age-related macular degeneration (AMD) preferentially affects distinct cell types and topographic regions in retina. To characterize the impact of AMD on gene expression changes across retinal cell types and regions, we generated both single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data from macular and peripheral retina in postmortem human donors with and without AMD. The scRNA-seq data revealed 11 major cell types with many previously reported AMD risk genes showing substantial cell type and region specificity. Cell type proportional changes with advancing AMD stage were significant for Müller glia, rods, astrocytes, microglia and endothelium.

scholarly journals Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

2020 ◽  
Author(s):  
Kayla A Johnson ◽  
Arjun Krishnan
Keyword(s):  
Gene Expression ◽  
Tissue Expression ◽  
Specific Gene ◽  
Expression Data ◽  
Rna Seq ◽  
Functional Relationships ◽  
Gene Coexpression ◽  
Network Transformation ◽  
Coexpression Networks ◽  
The Impact

AbstractBackgroundConstructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks – including good choices for data pre-processing, normalization, and network transformation – have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing/normalization methods for RNA-seq focus on the end goal of determining differential gene expression.ResultsHere, we present a comprehensive benchmarking and analysis of 30 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We tested these workflows on both large, homogenous datasets (Genotype-Tissue Expression project) and small, heterogeneous datasets from various labs (submitted to the Sequence Read Archive). We analyzed the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with trimmed mean of M-values or upper quartile normalization producing networks that most accurately recapitulate known tissue-naive and tissue-specific gene functional relationships.ConclusionsBased on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/norm_for_RNAseq_coexp to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

scholarly journals The Gene Expression Deconvolution Interactive Tool (GEDIT): Accurate Cell Type Quantification from Gene Expression Data

2019 ◽  
Author(s):  
Brian B. Nadel ◽  
David Lopez ◽  
Dennis J. Montoya ◽  
Feiyang Ma ◽  
Hannah Waddel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Reference Data ◽  
Cell Types ◽  
Expression Data ◽  
Rna Seq ◽  
Cell Type ◽  
Tissue Samples ◽  
Interactive Tool ◽  
Human And Mouse

AbstractThe cell type composition of heterogeneous tissue samples can be a critical variable in both clinical and laboratory settings. However, current experimental methods of cell type quantification (e.g. cell flow cytometry) are costly, time consuming, and can introduce bias. Computational approaches that infer cell type abundance from expression data offer an alternate solution. While these methods have gained popularity, most are limited to predicting hematopoietic cell types and do not produce accurate predictions for stromal cell types. Many of these methods are also limited to particular platforms, whether RNA-seq or specific microarrays. We present the Gene Expression Deconvolution Interactive Tool (GEDIT), a tool that overcomes these limitations, compares favorably with existing methods, and provides superior versatility. Using both simulated and experimental data, we extensively evaluate the performance of GEDIT and demonstrate that it returns robust results under a wide variety of conditions. These conditions include a variety of platforms (microarray and RNA-seq), tissue types (blood and stromal), and species (human and mouse). Finally, we provide reference data from eight sources spanning a wide variety of stromal and hematopoietic types in both human and mouse. This reference database allows the user to obtain estimates for a wide variety of tissue samples without having to provide their own data. GEDIT also accepts user submitted reference data, thus allowing the estimation of any cell type or subtype, provided that reference data is available.Author SummaryThe Gene Expression Deconvolution Interactive Tool (GEDIT) is a robust and accurate tool that uses gene expression data to estimate cell type abundances. Extensive testing on a variety of tissue types and technological platforms demonstrates that GEDIT provides greater versatility than other cell type deconvolution tools. GEDIT utilizes reference data describing the expression profile of purified cell types, and we provide in the software package a library of reference matrices from various sources. GEDIT is also flexible and allows the user to supply custom reference matrices. A GUI interface for GEDIT is available at http://webtools.mcdb.ucla.edu/, and source code and reference matrices are available at https://github.com/BNadel/GEDIT.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Li Tong ◽  
◽  
Po-Yen Wu ◽  
John H. Phan ◽  
Hamid R. Hassazadeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Disease Outcome ◽  
Rna Seq ◽  
Next Generation Sequencing Technology ◽  
Normalization Methods ◽  
The Us ◽  
Sequencing Quality ◽  
Improved Accuracy ◽  
The Impact

Abstract To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline’s performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

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