NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
The increasing availability of single-cell data revolutionizes the understanding of biological
mechanisms at cellular resolution. For differential expression analysis in multi-subject single …
mechanisms at cellular resolution. For differential expression analysis in multi-subject single …
bigSCale: an analytical framework for big-scale single-cell data
Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into
complex tissues, with the latest techniques capable of processing tens of thousands of cells …
complex tissues, with the latest techniques capable of processing tens of thousands of cells …
ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis
Single-cell RNA-seq data allows insight into normal cellular function and various disease
states through molecular characterization of gene expression on the single cell level …
states through molecular characterization of gene expression on the single cell level …
Mixture models for single-cell assays with applications to vaccine studies
Blood and tissue are composed of many functionally distinct cell subsets. In immunological
studies, these can be measured accurately only using single-cell assays. The …
studies, these can be measured accurately only using single-cell assays. The …
Fast zero-inflated negative binomial mixed modeling approach for analyzing longitudinal metagenomics data
X Zhang, N Yi - Bioinformatics, 2020 - academic.oup.com
Motivation Longitudinal metagenomics data, including both 16S rRNA and whole-
metagenome shotgun sequencing data, enhanced our abilities to understand the dynamic …
metagenome shotgun sequencing data, enhanced our abilities to understand the dynamic …
Machine intelligence in single-cell data analysis: advances and new challenges
The rapid development of single-cell technologies allows for dissecting cellular
heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of …
heterogeneity at different omics layers with an unprecedented resolution. In-dep analysis of …
[HTML][HTML] Deep learning applications in single-cell genomics and transcriptomics data analysis
Traditional bulk sequencing methods are limited to measuring the average signal in a group
of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution …
of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution …
NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis
X Zhang, N Yi - BMC bioinformatics, 2020 - Springer
Background Microbiome/metagenomic data have specific characteristics, including varying
total sequence reads, over-dispersion, and zero-inflation, which require tailored analytic …
total sequence reads, over-dispersion, and zero-inflation, which require tailored analytic …
Deep generative modeling for single-cell transcriptomics
Single-cell transcriptome measurements can reveal unexplored biological diversity, but they
suffer from technical noise and bias that must be modeled to account for the resulting …
suffer from technical noise and bias that must be modeled to account for the resulting …
Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases
In silico quantification of cell proportions from mixed-cell transcriptomics data
(deconvolution) requires a reference expression matrix, called basis matrix. We hypothesize …
(deconvolution) requires a reference expression matrix, called basis matrix. We hypothesize …