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1. Bayesian negative binomial regression for differential expression with confounding factors

   https://doi.org/10.1093/bioinformatics/bty330  

   Dadaneh, Siamak Zamani ; Zhou, Mingyuan ; Qian, Xiaoning ; Berger, ed., Bonnie ( April 2018 , Bioinformatics)

   Rapid adoption of high-throughput sequencing technologies has enabled better understanding of genome-wide molecular profile changes associated with phenotypic differences in biomedical studies. Often, these changes are due to multiple interacting factors. Existing methods are mostly considering differential expression across two conditions studying one main factor without considering other confounding factors. In addition, they are often coupled with essential sophisticated ad-hoc pre-processing steps such as normalization, restricting their adaptability to general experimental setups. Complex multi-factor experimental design to accurately decipher genotype-phenotype relationships signifies the need for developing effective statistical tools for genome-scale sequencing data profiled under multi-factor conditions.

   We have developed a novel Bayesian negative binomial regression (BNB-R) method for the analysis of RNA sequencing (RNA-seq) count data. In particular, the natural model parameterization removes the needs for the normalization step, while the method is capable of tackling complex experimental design involving multi-variate dependence structures. Efficient Bayesian inference of model parameters is obtained by exploiting conditional conjugacy via novel data augmentation techniques. Comprehensive studies on both synthetic and real-world RNA-seq data demonstrate the superior performance of BNB-R in terms of the areas under both the receiver operating characteristic and precision-recall curves.

   BNB-R is implemented in R language and is available at https://github.com/siamakz/BNBR.

   Supplementary data are available at Bioinformatics online.

    

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2. QUBIC2: a novel and robust biclustering algorithm for analyses and interpretation of large-scale RNA-Seq data

   https://doi.org/10.1093/bioinformatics/btz692  

   Xie, Juan ; Ma, Anjun ; Zhang, Yu ; Liu, Bingqiang ; Cao, Sha ; Wang, Cankun ; Xu, Jennifer ; Zhang, Chi ; Ma, Qin ; Birol, ed., Inanc ( September 2019 , Bioinformatics)

   The biclustering of large-scale gene expression data holds promising potential for detecting condition-specific functional gene modules (i.e. biclusters). However, existing methods do not adequately address a comprehensive detection of all significant bicluster structures and have limited power when applied to expression data generated by RNA-Sequencing (RNA-Seq), especially single-cell RNA-Seq (scRNA-Seq) data, where massive zero and low expression values are observed.

   We present a new biclustering algorithm, QUalitative BIClustering algorithm Version 2 (QUBIC2), which is empowered by: (i) a novel left-truncated mixture of Gaussian model for an accurate assessment of multimodality in zero-enriched expression data, (ii) a fast and efficient dropouts-saving expansion strategy for functional gene modules optimization using information divergency and (iii) a rigorous statistical test for the significance of all the identified biclusters in any organism, including those without substantial functional annotations. QUBIC2 demonstrated considerably improved performance in detecting biclusters compared to other five widely used algorithms on various benchmark datasets from E.coli, Human and simulated data. QUBIC2 also showcased robust and superior performance on gene expression data generated by microarray, bulk RNA-Seq and scRNA-Seq.

   The source code of QUBIC2 is freely available at https://github.com/OSU-BMBL/QUBIC2.

   czhang87@iu.edu or qin.ma@osumc.edu

   Supplementary data are available at Bioinformatics online.

    

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3. Molecular insight into cotton leaf curl geminivirus disease resistance in cultivated cotton ( Gossypium hirsutum )

   https://doi.org/10.1111/pbi.13236  

   Zaidi, Syed Shan‐e‐Ali ; Naqvi, Rubab Zahra ; Asif, Muhammad ; Strickler, Susan ; Shakir, Sara ; Shafiq, Muhammad ; Khan, Abdul Manan ; Amin, Imran ; Mishra, Bharat ; Mukhtar, M. Shahid ; et al ( September 2019 , Plant Biotechnology Journal)

   Cultivated cotton (Gossypium hirsutum) is the most important fibre crop in the world. Cotton leaf curl disease (CLCuD) is the major limiting factor and a threat to textile industry in India and Pakistan. All the local cotton cultivars exhibit moderate to no resistance againstCLCuD. In this study, we evaluated an exotic cotton accession Mac7 as a resistance source toCLCuD by challenging it with viruliferous whiteflies and performingqPCRto evaluate the presence/absence and relative titre ofCLCuD‐associated geminiviruses/betasatellites. The results indicated that replication of pathogenicity determinant betasatellite is significantly attenuated in Mac7 and probably responsible for resistance phenotype. Afterwards, to decipher the genetic basis ofCLCuD resistance in Mac7, we performedRNAsequencing onCLCuD‐infested Mac7 and validatedRNA‐Seq data withqPCRon 24 independent genes. We performed co‐expression network and pathway analysis for regulation of geminivirus/betasatellite‐interacting genes. We identified nine novel modules with 52 hubs of highly connected genes in network topology within the co‐expression network. Analysis of these hubs indicated the differential regulation of auxin stimulus and cellular localization pathways in response toCLCuD. We also analysed the differential regulation of geminivirus/betasatellite‐interacting genes in Mac7. We further performed the functional validation of selected candidate genes via virus‐induced gene silencing (VIGS). Finally, we evaluated the genomic context of resistance responsive genes and found that these genes are not specific to A or D sub‐genomes ofG. hirsutum. These results have important implications in understandingCLCuD resistance mechanism and developing a durable resistance in cultivated cotton.

    

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4. Two-phase differential expression analysis for single cell RNA-seq

   https://doi.org/10.1093/bioinformatics/bty329  

   Wu, Zhijin ; Zhang, Yi ; Stitzel, Michael L. ; Wu, Hao ; Berger, ed., Bonnie ( April 2018 , Bioinformatics)

   Single-cell RNA-sequencing (scRNA-seq) has brought the study of the transcriptome to higher resolution and makes it possible for scientists to provide answers with more clarity to the question of ‘differential expression’. However, most computational methods still stick with the old mentality of viewing differential expression as a simple ‘up or down’ phenomenon. We advocate that we should fully embrace the features of single cell data, which allows us to observe binary (from Off to On) as well as continuous (the amount of expression) regulations.

   We develop a method, termed SC2P, that first identifies the phase of expression a gene is in, by taking into account of both cell- and gene-specific contexts, in a model-based and data-driven fashion. We then identify two forms of transcription regulation: phase transition, and magnitude tuning. We demonstrate that compared with existing methods, SC2P provides substantial improvement in sensitivity without sacrificing the control of false discovery, as well as better robustness. Furthermore, the analysis provides better interpretation of the nature of regulation types in different genes.

   SC2P is implemented as an open source R package publicly available at https://github.com/haowulab/SC2P.

   Supplementary data are available at Bioinformatics online.

    

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5. A new Bayesian factor analysis method improves detection of genes and biological processes affected by perturbations in single-cell CRISPR screening

   https://doi.org/10.1038/s41592-023-02017-4  

   Zhou, Yifan ; Luo, Kaixuan ; Liang, Lifan ; Chen, Mengjie ; He, Xin ( September 2023 , Nature Methods)

   Clustered regularly interspaced short palindromic repeats (CRISPR) screening coupled with single-cell RNA sequencing has emerged as a powerful tool to characterize the effects of genetic perturbations on the whole transcriptome at a single-cell level. However, due to its sparsity and complex structure, analysis of single-cell CRISPR screening data is challenging. In particular, standard differential expression analysis methods are often underpowered to detect genes affected by CRISPR perturbations. We developed a statistical method for such data, called guided sparse factor analysis (GSFA). GSFA infers latent factors that represent coregulated genes or gene modules; by borrowing information from these factors, it infers the effects of genetic perturbations on individual genes. We demonstrated through extensive simulation studies that GSFA detects perturbation effects with much higher power than state-of-the-art methods. Using single-cell CRISPR data from human CD8⁺T cells and neural progenitor cells, we showed that GSFA identified biologically relevant gene modules and specific genes affected by CRISPR perturbations, many of which were missed by existing methods, providing new insights into the functions of genes involved in T cell activation and neurodevelopment.

    

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