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1. scCAN: single-cell clustering using autoencoder and network fusion

   https://doi.org/10.1038/s41598-022-14218-6  

   Tran, Bang ; Tran, Duc ; Nguyen, Hung ; Ro, Seungil ; Nguyen, Tin ( June 2022 , Scientific Reports)

   Unsupervised clustering of single-cell RNA sequencing data (scRNA-seq) is important because it allows us to identify putative cell types. However, the large number of cells (up to millions), the high-dimensionality of the data (tens of thousands of genes), and the high dropout rates all present substantial challenges in single-cell analysis. Here we introduce a new method, named single-cell Clustering using Autoencoder and Network fusion (scCAN), that can overcome these challenges to accurately segregate different cell types in large and sparse scRNA-seq data. In an extensive analysis using 28 real scRNA-seq datasets (more than three million cells) and 243 simulated datasets, we validate that scCAN: (1) correctly estimates the number of true cell types, (2) accurately segregates cells of different types, (3) is robust against dropouts, and (4) is fast and memory efficient. We also compare scCAN with CIDR, SEURAT3, Monocle3, SHARP, and SCANPY. scCAN outperforms these state-of-the-art methods in terms of both accuracy and scalability. The scCAN package is available athttps://cran.r-project.org/package=scCAN. Data and R scripts are available athttp://sccan.tinnguyen-lab.com/

    

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2. Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells

   https://doi.org/10.3390/ijms22189684  

   Sun, Jiao ; Fahmi, Naima Ahmed ; Nassereddeen, Heba ; Cheng, Sze ; Martinez, Irene ; Fan, Deliang ; Yong, Jeongsik ; Zhang, Wei ( September 2021 , International Journal of Molecular Sciences)

   null (Ed.)

   Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional regulation is very limited. In this study, computational methods developed by our lab were applied to RNA-seq data to detect transcript variants (i.e., alternative splicing (AS) and alternative polyadenylation (APA) events). The RNA-seq data were obtained from a publicly available source, and they consist of mock-treated and SARS-CoV-2 infected (COVID-19) lung alveolar (A549) cells. Data analysis results show that more AS events are found in SARS-CoV-2 infected cells than in mock-treated cells, whereas fewer APA events are detected in SARS-CoV-2 infected cells. A combination of conventional differential gene expression analysis and transcript variants analysis revealed that most of the genes with transcript variants are not differentially expressed. This indicates that no strong correlation exists between differential gene expression and the AS/APA events in the mock-treated or SARS-CoV-2 infected samples. These genes with transcript variants can be applied as another layer of molecular signatures for COVID-19 studies. In addition, the transcript variants are enriched in important biological pathways that were not detected in the studies that only focused on differential gene expression analysis. Therefore, the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis. 

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3. Microbiome composition and function within the Kellet’s whelk perivitelline fluid

   https://doi.org/10.1128/spectrum.03514-23  

   Daniels, Benjamin N. ; Nurge, Jenna ; De Smet, Chanel ; Sleeper, Olivia ; White, Crow ; Davidson, Jean M. ; Fidopiastis, Pat ( March 2024 , Microbiology Spectrum)

   Hom, Erik F. (Ed.)

   This study provides novel insight to an encapsulated system with strong evidence of symbiosis between the microbial inhabitants and developing host embryos. The Kellet’s whelk perivitelline fluid (PVF) contains microbial organisms of interest that may be providing symbiotic functions and potential antimicrobial properties during this vulnerable life history stage. This study, the first to utilize a comprehensive approach to investigating Kellet’s whelk PVF microbiome, couples 16S rRNA gene long-read sequencing with RNA-seq. This research contributes to and expands our knowledge on the roles of beneficial host-associated microbes.

    

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4. HyPR-seq: Single-cell quantification of chosen RNAs via hybridization and sequencing of DNA probes

   https://doi.org/10.1073/pnas.2010738117  

   Marshall, Jamie L. ; Doughty, Benjamin R. ; Subramanian, Vidya ; Guckelberger, Philine ; Wang, Qingbo ; Chen, Linlin M. ; Rodriques, Samuel G. ; Zhang, Kaite ; Fulco, Charles P. ; Nasser, Joseph ; et al ( December 2020 , Proceedings of the National Academy of Sciences)

   Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present Hybridization of Probes to RNA for sequencing (HyPR-seq), a method to sensitively quantify the expression of hundreds of chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects nonpolyadenylated and low-abundance transcripts, and can profile more than 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell-type frequencies in tissue using low-abundance marker genes. By directing sequencing power to genes of interest and sensitively quantifying individual transcripts, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome single-cell RNA-sequencing, making HyPR-seq a powerful method for targeted RNA profiling in single cells.

    

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5. 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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