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1. CorDiffViz: an R package for visualizing multi-omics differential correlation networks

   https://doi.org/10.1186/s12859-021-04383-2  

   Yu, Shiqing ; Drton, Mathias ; Promislow, Daniel_E_L ; Shojaie, Ali ( October 2021 , BMC Bioinformatics)

   Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression.

   We present a new R package, , that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in , and , and is available athttps://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available athttps://diffcornet.github.io/CorDiffViz/demo.html.

   Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets.

    

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2. 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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3. PCA outperforms popular hidden variable inference methods for molecular QTL mapping

   https://doi.org/10.1186/s13059-022-02761-4  

   Zhou, Heather J. ; Li, Lei ; Li, Yumei ; Li, Wei ; Li, Jingyi Jessica ( October 2022 , Genome Biology)

   Estimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth “QTL”) analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose.

   Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)—a well-established dimension reduction and factor discovery method—via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use.

   To help researchers use PCA in their QTL analysis, we provide an R package along with a detailed guide, both of which are freely available athttps://github.com/heatherjzhou/PCAForQTL. We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research.

    

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4. Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods

   https://doi.org/10.1186/s13059-024-03292-w  

   Hu, Mengying ; Chikina, Maria ( December 2024 , Genome Biology)

   Computational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions.

   In our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers.

   Our heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly packagehttps://github.com/humengying0907/deconvBenchmarkingandhttps://doi.org/10.5281/zenodo.8206516, enabling further developments in deconvolution methods.

    

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5. Reference-agnostic representation and visualization of pan-genomes

   https://doi.org/10.1186/s12859-021-04424-w  

   Liang, Qihua ; Lonardi, Stefano ( October 2021 , BMC Bioinformatics)

   The pan-genome of a species is the union of the genes and non-coding sequences present in all individuals (cultivar, accessions, or strains) within that species.

   Here we introduce PGV, a reference-agnostic representation of the pan-genome of a species based on the notion of consensus ordering. Our experimental results demonstrate that PGV enables an intuitive, effective and interactive visualization of a pan-genome by providing a genome browser that can elucidate complex structural genomic variations.

   The PGV software can be installed via conda or downloaded fromhttps://github.com/ucrbioinfo/PGV. The companion PGV browser athttp://pgv.cs.ucr.educan be tested using example bed tracks available from the GitHub page.

    

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