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1. Covariate-dependent negative binomial factor analysis of RNA sequencing data

   https://doi.org/10.1093/bioinformatics/bty237  

   Zamani Dadaneh, Siamak ; Zhou, Mingyuan ; Qian, Xiaoning ( June 2018 , Bioinformatics)

   High-throughput sequencing technologies, in particular RNA sequencing (RNA-seq), have become the basic practice for genomic studies in biomedical research. In addition to studying genes individually, for example, through differential expression analysis, investigating co-ordinated expression variations of genes may help reveal the underlying cellular mechanisms to derive better understanding and more effective prognosis and intervention strategies. Although there exists a variety of co-expression network based methods to analyze microarray data for this purpose, instead of blindly extending these methods for microarray data that may introduce unnecessary bias, it is crucial to develop methods well adapted to RNA-seq data to identify the functional modules of genes with similar expression patterns.

   We have developed a fully Bayesian covariate-dependent negative binomial factor analysis (dNBFA) method—dNBFA—for RNA-seq count data, to capture coordinated gene expression changes, while considering effects from covariates reflecting different influencing factors. Unlike existing co-expression network based methods, our proposed model does not require multiple ad-hoc choices on data processing, transformation, as well as co-expression measures and can be directly applied to RNA-seq data. Furthermore, being capable of incorporating covariate information, the proposed method can tackle setups with complex confounding factors in different experiment designs. Finally, the natural model parameterization removes the need for a normalization preprocessing step, as commonly adopted to compensate for the effect of sequencing-depth variations. Efficient Bayesian inference of model parameters is derived by exploiting conditional conjugacy via novel data augmentation techniques. Experimental results on several real-world RNA-seq datasets on complex diseases suggest dNBFA as a powerful tool for discovering the gene modules with significant differential expression and meaningful biological insight.

   dNBFA is implemented in R language and is available at https://github.com/siamakz/dNBFA.

    

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2. A weighted FDR procedure under discrete and heterogeneous null distributions

   https://doi.org/10.1002/bimj.201900216  

   Chen, Xiongzhi ; Doerge, R. W. ; Sarkar, Sanat K. ( May 2020 , Biometrical Journal)

   Multiple testing (MT) with false discovery rate (FDR) control has been widely conducted in the “discrete paradigm” wherep‐values have discrete and heterogeneous null distributions. However, in this scenario existing FDR procedures often lose some power and may yield unreliable inference, and for this scenario there does not seem to be an FDR procedure that partitions hypotheses into groups, employs data‐adaptive weights and is nonasymptotically conservative. We propose a weightedp‐value‐based FDR procedure, “weighted FDR (wFDR) procedure” for short, for MT in the discrete paradigm that efficiently adapts to both heterogeneity and discreteness ofp‐value distributions. We theoretically justify the nonasymptotic conservativeness of the wFDR procedure under independence, and show via simulation studies that, for MT based onp‐values of binomial test or Fisher's exact test, it is more powerful than six other procedures. The wFDR procedure is applied to two examples based on discrete data, a drug safety study, and a differential methylation study, where it makes more discoveries than two existing methods.

    

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3. Identification of cell-type specific alternative transcripts in the multicellular alga Volvox carteri

   https://doi.org/10.1186/s12864-023-09558-0  

   Balasubramanian, Ravi Nicholas ; Gao, Minglu ; Umen, James ( October 2023 , BMC Genomics)

   Cell type specialization is a hallmark of complex multicellular organisms and is usually established through implementation of cell-type-specific gene expression programs. The multicellular green algaVolvox carterihas just two cell types, germ and soma, that have previously been shown to have very different transcriptome compositions which match their specialized roles. Here we interrogated another potential mechanism for differentiation inV. carteri, cell type specific alternative transcript isoforms (CTSAI).

   We used pre-existing predictions of alternative transcripts and de novo transcript assembly with HISAT2 and Ballgown software to compile a list of loci with two or more transcript isoforms, identified a small subset that were candidates for CTSAI, and manually curated this subset of genes to remove false positives. We experimentally verified three candidates using semi-quantitative RT-PCR to assess relative isoform abundance in each cell type.

   Of the 1978 loci with two or more predicted transcript isoforms 67 of these also showed cell type isoform expression biases. After curation 15 strong candidates for CTSAI were identified, three of which were experimentally verified, and their predicted gene product functions were evaluated in light of potential cell type specific roles. A comparison of genes with predicted alternative splicing fromChlamydomonas reinhardtii, a unicellular relative ofV. carteri, identified little overlap between ortholog pairs with alternative splicing in both species. Finally, we interrogated cell type expression patterns of 126 V. carteripredicted RNA binding protein (RBP) encoding genes and found 40 that showed either somatic or germ cell expression bias. These RBPs are potential mediators of CTSAI inV. carteriand suggest possible pre-adaptation for cell type specific RNA processing and a potential path for generating CTSAI in the early ancestors of metazoans and plants.

   We predicted numerous instances of alternative transcript isoforms in Volvox, only a small subset of which showed cell type specific isoform expression bias. However, the validated examples of CTSAI supported existing hypotheses about cell type specialization inV. carteri,and also suggested new hypotheses about mechanisms of functional specialization for their gene products. Our data imply that CTSAI operates as a minor but important component ofV. cartericellular differentiation and could be used as a model for how alternative isoforms emerge and co-evolve with cell type specialization.

    

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4. Comparing Large Covariance Matrices under Weak Conditions on the Dependence Structure and its Application to Gene Clustering

   https://doi.org/10.1111/biom.12552  

   Chang, Jinyuan ; Zhou, Wen ; Zhou, Wen-Xin ; Wang, Lan ( July 2016 , Biometrics)

   Comparing large covariance matrices has important applications in modern genomics, where scientists are often interested in understanding whether relationships (e.g., dependencies or co-regulations) among a large number of genes vary between different biological states. We propose a computationally fast procedure for testing the equality of two large covariance matrices when the dimensions of the covariance matrices are much larger than the sample sizes. A distinguishing feature of the new procedure is that it imposes no structural assumptions on the unknown covariance matrices. Hence, the test is robust with respect to various complex dependence structures that frequently arise in genomics. We prove that the proposed procedure is asymptotically valid under weak moment conditions. As an interesting application, we derive a new gene clustering algorithm which shares the same nice property of avoiding restrictive structural assumptions for high-dimensional genomics data. Using an asthma gene expression dataset, we illustrate how the new test helps compare the covariance matrices of the genes across different gene sets/pathways between the disease group and the control group, and how the gene clustering algorithm provides new insights on the way gene clustering patterns differ between the two groups. The proposed methods have been implemented in an R-package HDtest and are available on CRAN.

    

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5. Nonparametric expression analysis using inferential replicate counts

   https://doi.org/10.1093/nar/gkz622  

   Zhu, Anqi ; Srivastava, Avi ; Ibrahim, Joseph G ; Patro, Rob ; Love, Michael I ( August 2019 , Nucleic Acids Research)

   Abstract A primary challenge in the analysis of RNA-seq data is to identify differentially expressed genes or transcripts while controlling for technical biases. Ideally, a statistical testing procedure should incorporate the inherent uncertainty of the abundance estimates arising from the quantification step. Most popular methods for RNA-seq differential expression analysis fit a parametric model to the counts for each gene or transcript, and a subset of methods can incorporate uncertainty. Previous work has shown that nonparametric models for RNA-seq differential expression may have better control of the false discovery rate, and adapt well to new data types without requiring reformulation of a parametric model. Existing nonparametric models do not take into account inferential uncertainty, leading to an inflated false discovery rate, in particular at the transcript level. We propose a nonparametric model for differential expression analysis using inferential replicate counts, extending the existing SAMseq method to account for inferential uncertainty. We compare our method, Swish, with popular differential expression analysis methods. Swish has improved control of the false discovery rate, in particular for transcripts with high inferential uncertainty. We apply Swish to a single-cell RNA-seq dataset, assessing differential expression between sub-populations of cells, and compare its performance to the Wilcoxon test. 

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