RecBic: a fast and accurate algorithm recognizing trend-preserving biclusters

Abstract Motivation Biclustering has emerged as a powerful approach to identifying functional patterns in complex biological data. However, existing tools are limited by their accuracy and efficiency to recognize various kinds of complex biclusters submerged in ever large datasets. We introduce a novel fast and highly accurate algorithm RecBic to identify various forms of complex biclusters in gene expression datasets. Results We designed RecBic to identify various trend-preserving biclusters, particularly, those with narrow shapes, i.e. clusters where the number of genes is larger than the number of conditions/samples. Given a gene expression matrix, RecBic starts with a column seed, and grows it into a full-sized bicluster by simply repetitively comparing real numbers. When tested on simulated datasets in which the elements of implanted trend-preserving biclusters and those of the background matrix have the same distribution, RecBic was able to identify the implanted biclusters in a nearly perfect manner, outperforming all the compared salient tools in terms of accuracy and robustness to noise and overlaps between the clusters. Moreover, RecBic also showed superiority in identifying functionally related genes in real gene expression datasets. Availability and implementation Code, sample input data and usage instructions are available at the following websites. Code: https://github.com/holyzews/RecBic/tree/master/RecBic/. Data: http://doi.org/10.5281/zenodo.3842717. Supplementary information Supplementary data are available at Bioinformatics online.

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The gene expression matrix: Towards the extraction of genetic network architectures

Nonlinear Analysis ◽

10.1016/s0362-546x(97)00217-4 ◽

1997 ◽

Vol 30 (3) ◽

pp. 1815-1824 ◽

Cited By ~ 22

Author(s):

Roland Somogyi ◽

Stefanie Fuhrman ◽

Manor Askenazi ◽

Andy Wuensche

Keyword(s):

Gene Expression ◽

Genetic Network ◽

Network Architectures ◽

Gene Expression Matrix ◽

Expression Matrix

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Mining the Gene Expression Matrix: Inferring Gene Relationships from Large Scale Gene Expression Data

Information Processing in Cells and Tissues ◽

10.1007/978-1-4615-5345-8_22 ◽

1998 ◽

pp. 203-212 ◽

Cited By ~ 35

Author(s):

Patrik D’haeseleer ◽

Xiling Wen ◽

Stefanie Fuhrman ◽

Roland Somogyi

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Large Scale ◽

Expression Data ◽

Gene Expression Matrix ◽

Expression Matrix

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Properties of blood-contacting surfaces of clinically implanted cardiac assist devices: Gene expression, matrix composition, and ultrastructural characterization of cellular linings

Journal of Cellular Biochemistry ◽

10.1002/jcb.240570320 ◽

1995 ◽

Vol 57 (3) ◽

pp. 557-573 ◽

Cited By ~ 33

Author(s):

Michael J. Menconi ◽

Shirwin Pockwinse ◽

Thomas A. Owen ◽

Kurt A. Dasse ◽

Gary S. Stein ◽

...

Keyword(s):

Gene Expression ◽

Matrix Composition ◽

Assist Devices ◽

Cardiac Assist ◽

Gene Expression Matrix ◽

Cardiac Assist Devices ◽

Ultrastructural Characterization ◽

Expression Matrix

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Single-cell RNA-seq data reveals TNBC tumor heterogeneity through characterizing subclone compositions and proportions

10.1101/858290 ◽

2019 ◽

Author(s):

Weida Wang ◽

Jinyuan Xu ◽

Shuyuan Wang ◽

Peng Xia ◽

Li Zhang ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Tumor Heterogeneity ◽

Single Cells ◽

Rna Seq ◽

Biological Functions ◽

Gene Markers ◽

Gene Expression Matrix ◽

Deconvolution Algorithm ◽

Expression Matrix

AbstractUnderstanding subclonal architecture and their biological functions poses one of the key challenges to deeply portray and investigative the cause of triple-negative breast cancer (TNBC). Here we combine single-cell and bulk sequencing data to analyze tumor heterogeneity through characterizing subclone compositions and proportions. Based on sing-cell RNA-seq data (GSE118389) we identified five distinct cell subpopulations and characterized their biological functions based on their gene markers. According to the results of functional annotation, we found that C1 and C2 are related to immune functions, while C5 is related to programmed cell death. Then based on subclonal basis gene expression matrix, we applied deconvolution algorithm on TCGA tissue RNA-seq data and observed that microenvironment is diverse among TNBC subclones, especially C1 is closely related to T cells. What’s more, we also found that high C5 proportions would led to poor survival outcome, log-rank test p-value and HR [95%CI] for five years overall survival in GSE96058 dataset were 0.0158 and 2.557 [1.160-5.636]. Collectively, our analysis reveals both intra-tumor and inter-tumor heterogeneity and their association with subclonal microenvironment in TNBC (subclone compositions and proportions), and uncovers the organic combination of subclones dictating poor outcomes in this disease.HighlightsWe applied deconvolution algorithm on subclonal basis gene expression matrix to link single cells and bulk tissue together.

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BMDx: a graphical Shiny application to perform Benchmark Dose analysis for transcriptomics data

Bioinformatics ◽

10.1093/bioinformatics/btaa030 ◽

2020 ◽

Vol 36 (9) ◽

pp. 2932-2933 ◽

Cited By ~ 3

Author(s):

Angela Serra ◽

Laura Aliisa Saarimäki ◽

Michele Fratello ◽

Veer Singh Marwah ◽

Dario Greco

Keyword(s):

Gene Expression ◽

Information Criterion ◽

Benchmark Dose ◽

Functional Enrichment ◽

Supplementary Information ◽

Gene Expression Matrix ◽

R Shiny ◽

Transcriptomics Data ◽

Dose Dependent ◽

Shiny Application

Abstract Motivation The analysis of dose-dependent effects on the gene expression is gaining attention in the field of toxicogenomics. Currently available computational methods are usually limited to specific omics platforms or biological annotations and are able to analyse only one experiment at a time. Results We developed the software BMDx with a graphical user interface for the Benchmark Dose (BMD) analysis of transcriptomics data. We implemented an approach based on the fitting of multiple models and the selection of the optimal model based on the Akaike Information Criterion. The BMDx tool takes as an input a gene expression matrix and a phenotype table, computes the BMD, its related values, and IC50/EC50 estimations. It reports interactive tables and plots that the user can investigate for further details of the fitting, dose effects and functional enrichment. BMDx allows a fast and convenient comparison of the BMD values of a transcriptomics experiment at different time points and an effortless way to interpret the results. Furthermore, BMDx allows to analyse and to compare multiple experiments at once. Availability and implementation BMDx is implemented as an R/Shiny software and is available at https://github.com/Greco-Lab/BMDx/. Supplementary information Supplementary data are available at Bioinformatics online.

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Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data

Bioinformatics ◽

10.1093/bioinformatics/btaa947 ◽

2020 ◽

Author(s):

Cynthia Z Ma ◽

Michael R Brent

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Large Scale ◽

Supplementary Information ◽

Activity Levels ◽

Expression Data ◽

Gene Expression Matrix ◽

Perturbation Data ◽

Carry Over ◽

Necessary And Sufficient

Abstract Motivation The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now. Results We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2. Availability and implementation Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

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Bfimpute: A Bayesian factorization method to recover single-cell RNA sequencing data

10.1101/2021.02.10.430649 ◽

2021 ◽

Author(s):

Zi-Hang Wen ◽

Jeremy L. Langsam ◽

Lu Zhang ◽

Wenjun Shen ◽

Xin Zhou

Keyword(s):

Gene Expression ◽

Single Cell ◽

Single Cells ◽

Cell Group ◽

Specific Gene ◽

Cell Type ◽

Sequencing Data ◽

Gene Expression Matrix ◽

Study Gene Expression ◽

Expression Matrix

AbstractSingle-cell RNA-seq (scRNA-seq) offers opportunities to study gene expression of tens of thousands of single cells simultaneously, to investigate cell-to-cell variation, and to reconstruct cell-type-specific gene regulatory networks. Recovering dropout events in a sparse gene expression matrix for scRNA-seq data is a long-standing matrix completion problem. We introduce Bfimpute, a Bayesian factorization imputation algorithm that reconstructs two latent gene and cell matrices to impute final gene expression matrix within each cell group, with or without the aid of cell type labels or bulk data. Bfimpute achieves better accuracy than other six publicly notable scRNA-seq imputation methods on simulated and real scRNA-seq data, as measured by several different evaluation metrics. Bfimpute can also flexibly integrate any gene or cell related information that users provide to increase the performance. Availability: Bfimpute is implemented in R and is freely available at https://github.com/maiziezhoulab/Bfimpute.

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A dynamic programing approach to integrate gene expression data and network information for pathway model generation

Bioinformatics ◽

10.1093/bioinformatics/btz467 ◽

2019 ◽

Vol 36 (1) ◽

pp. 169-176 ◽

Cited By ~ 3

Author(s):

Yuexu Jiang ◽

Yanchun Liang ◽

Duolin Wang ◽

Dong Xu ◽

Trupti Joshi

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Gene Expression Data ◽

Biological Data ◽

Human Lung Cancer ◽

Supplementary Information ◽

Model Generation ◽

Expression Data ◽

Pathway Model ◽

Dynamic Programing

Abstract Motivation As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed toward integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results toward pathway model generation and testing. Results To address this gap, we have developed the IMPRes algorithm, a new step-wise active pathway detection method using a dynamic programing approach. IMPRes takes advantage of the existing pathway interaction knowledge in Kyoto Encyclopedia of Genes and Genomes. Omics data are then used to assign penalties to genes, interactions and pathways. Finally, starting from one or multiple seed genes, a shortest path algorithm is applied to detect downstream pathways that best explain the gene expression data. Since dynamic programing enables the detection one step at a time, it is easy for researchers to trace the pathways, which may lead to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on three yeast datasets have shown that IMPRes can achieve competitive or better performance than other state-of-the-art methods. Furthermore, a case study on human lung cancer dataset was performed and we provided several insights on genes and mechanisms involved in lung cancer, which had not been discovered before. Availability and implementation IMPRes visualization tool is available via web server at http://digbio.missouri.edu/impres. Supplementary information Supplementary data are available at Bioinformatics online.

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MetaQUBIC: a computational pipeline for gene-level functional profiling of metagenome and metatranscriptome

Bioinformatics ◽

10.1093/bioinformatics/btz414 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4474-4477 ◽

Cited By ~ 1

Author(s):

Anjun Ma ◽

Minxuan Sun ◽

Adam McDermaid ◽

Bingqiang Liu ◽

Qin Ma

Keyword(s):

Supplementary Information ◽

Computational Pipeline ◽

Data Types ◽

Human Gut ◽

Dna And Rna ◽

Gene Expression Matrix ◽

Environmental Interactions ◽

Gene Level ◽

Functional Profiling ◽

Expression Matrix

Abstract Motivation Metagenomic and metatranscriptomic analyses can provide an abundance of information related to microbial communities. However, straightforward analysis of this data does not provide optimal results, with a required integration of data types being needed to thoroughly investigate these microbiomes and their environmental interactions. Results Here, we present MetaQUBIC, an integrated biclustering-based computational pipeline for gene module detection that integrates both metagenomic and metatranscriptomic data. Additionally, we used this pipeline to investigate 735 paired DNA and RNA human gut microbiome samples, resulting in a comprehensive hybrid gene expression matrix of 2.3 million cross-species genes in the 735 human fecal samples and 155 functional enriched gene modules. We believe both the MetaQUBIC pipeline and the generated comprehensive human gut hybrid expression matrix will facilitate further investigations into multiple levels of microbiome studies. Availability and implementation The package is freely available at https://github.com/OSU-BMBL/metaqubic. Supplementary information Supplementary data are available at Bioinformatics online.

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