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1. Prioritizing predictive biomarkers for gene essentiality in cancer cells with mRNA expression data and DNA copy number profile

   https://doi.org/10.1093/bioinformatics/bty467  

   Guan, Yuanfang ; Li, Tingyang ; Zhang, Hongjiu ; Zhu, Fan ; Omenn, Gilbert S. ; Valencia, ed., Alfonso ( June 2018 , Bioinformatics)

   Finding driver genes that are responsible for the aberrant proliferation rate of cancer cells is informative for both cancer research and the development of targeted drugs. The established experimental and computational methods are labor-intensive. To make algorithms feasible in real clinical settings, methods that can predict driver genes using less experimental data are urgently needed.

   We designed an effective feature selection method and used Support Vector Machines (SVM) to predict the essentiality of the potential driver genes in cancer cell lines with only 10 genes as features. The accuracy of our predictions was the highest in the Broad-DREAM Gene Essentiality Prediction Challenge. We also found a set of genes whose essentiality could be predicted much more accurately than others, which we called Accurately Predicted (AP) genes. Our method can serve as a new way of assessing the essentiality of genes in cancer cells.

   The raw data that support the findings of this study are available at Synapse. https://www.synapse.org/#! Synapse: syn2384331/wiki/62825. Source code is available at GitHub. https://github.com/GuanLab/DREAM-Gene-Essentiality-Challenge.

   Supplementary data are available at Bioinformatics online.

    

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2. Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes

   https://doi.org/10.1093/bioinformatics/btx227  

   Duong, Dat ; Gai, Lisa ; Snir, Sagi ; Kang, Eun Yong ; Han, Buhm ; Sul, Jae Hoon ; Eskin, Eleazar ( July 2017 , Bioinformatics)

   There is recent interest in using gene expression data to contextualize findings from traditional genome-wide association studies (GWAS). Conditioned on a tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression, and eGenes are genes whose expression levels are associated with genetic variants. eQTLs and eGenes provide great supporting evidence for GWAS hits and important insights into the regulatory pathways involved in many diseases. When a significant variant or a candidate gene identified by GWAS is also an eQTL or eGene, there is strong evidence to further study this variant or gene. Multi-tissue gene expression datasets like the Gene Tissue Expression (GTEx) data are used to find eQTLs and eGenes. Unfortunately, these datasets often have small sample sizes in some tissues. For this reason, there have been many meta-analysis methods designed to combine gene expression data across many tissues to increase power for finding eQTLs and eGenes. However, these existing techniques are not scalable to datasets containing many tissues, like the GTEx data. Furthermore, these methods ignore a biological insight that the same variant may be associated with the same gene across similar tissues.

   We introduce a meta-analysis model that addresses these problems in existing methods. We focus on the problem of finding eGenes in gene expression data from many tissues, and show that our model is better than other types of meta-analyses.

   Source code is at https://github.com/datduong/RECOV.

   Supplementary data are available at Bioinformatics online.

    

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

   https://doi.org/10.1093/bioinformatics/btz414  

   Ma, Anjun ; Sun, Minxuan ; McDermaid, Adam ; Liu, Bingqiang ; Ma, Qin ; Valencia, ed., Alfonso ( May 2019 , Bioinformatics)

   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.

   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.

   The package is freely available at https://github.com/OSU-BMBL/metaqubic.

   Supplementary data are available at Bioinformatics online.

    

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4. Biclustering by sparse canonical correlation analysis

   https://doi.org/10.1007/s40484-017-0127-0  

   Pimentel, Harold ; Hu, Zhiyue ; Huang, Haiyan ( March 2018 , Quantitative Biology)

   Developing appropriate computational tools to distill biological insights from large‐scale gene expression data has been an important part of systems biology. Considering that gene relationships may change or only exist in a subset of collected samples, biclustering that involves clustering both genes and samples has become in‐creasingly important, especially when the samples are pooled from a wide range of experimental conditions.

   In this paper, we introduce a new biclustering algorithm to find subsets of genomic expression features (EFs) (e.g., genes, isoforms, exon inclusion) that show strong “group interactions” under certain subsets of samples. Group interactions are defined by strong partial correlations, or equivalently, conditional dependencies between EFs after removing the influences of a set of other functionally related EFs. Our new biclustering method, named SCCA‐BC, extends an existing method for group interaction inference, which is based on sparse canonical correlation analysis (SCCA) coupled with repeated random partitioning of the gene expression data set.

   SCCA‐BC gives sensible results on real data sets and outperforms most existing methods in simulations. Software is available athttps://github.com/pimentel/scca‐bc.

   SCCA‐BC seems to work in numerous conditions and the results seem promising for future extensions. SCCA‐BC has the ability to find different types of bicluster patterns, and it is especially advantageous in identifying a bicluster whose elements share the same progressive and multivariate normal distribution with a dense covariance matrix.

    

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

   https://doi.org/10.1093/bioinformatics/btaa630  

   Liu, Xiangyu ; Li, Di ; Liu, Juntao ; Su, Zhengchang ; Li, Guojun ( July 2020 , Bioinformatics)

   Cowen, Lenore (Ed.)

   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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