Enhancing scientific discoveries in molecular biology with deep generative models
Generative models provide a well‐established statistical framework for evaluating
uncertainty and deriving conclusions from large data sets especially in the presence of …
uncertainty and deriving conclusions from large data sets especially in the presence of …
An empirical Bayes method for differential expression analysis of single cells with deep generative models
Detecting differentially expressed genes is important for characterizing subpopulations of
cells. In scRNA-seq data, however, nuisance variation due to technical factors like …
cells. In scRNA-seq data, however, nuisance variation due to technical factors like …
Decision-making with auto-encoding variational Bayes
To make decisions based on a model fit with auto-encoding variational Bayes (AEVB),
practitioners often let the variational distribution serve as a surrogate for the posterior …
practitioners often let the variational distribution serve as a surrogate for the posterior …
Time-dependence of graph theory metrics in functional connectivity analysis
Brain graphs provide a useful way to computationally model the network structure of the
connectome, and this has led to increasing interest in the use of graph theory to quantitate …
connectome, and this has led to increasing interest in the use of graph theory to quantitate …
BICOSS: Bayesian iterative conditional stochastic search for GWAS
Background Single marker analysis (SMA) with linear mixed models for genome wide
association studies has uncovered the contribution of genetic variants to many observed …
association studies has uncovered the contribution of genetic variants to many observed …
Transcriptome-wide spatial RNA profiling maps the cellular architecture of the developing human neocortex
Spatial genomic technologies can map gene expression in tissues, but provide limited
potential for transcriptome-wide discovery approaches and application to fixed tissue …
potential for transcriptome-wide discovery approaches and application to fixed tissue …
BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data
Background Genome-wide association studies (GWASes) aim to identify single nucleotide
polymorphisms (SNPs) associated with a given phenotype. A common approach for the …
polymorphisms (SNPs) associated with a given phenotype. A common approach for the …
BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies
Background Genome-wide association studies (GWAS) seek to identify single nucleotide
polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated …
polymorphisms (SNPs) that cause observed phenotypes. However, with highly correlated …
Deep generative models for detecting differential expression in single cells
Detecting differentially expressed genes is important for characterizing subpopulations of
cells. However, in scRNA-seq data, nuisance variation due to technical factors like …
cells. However, in scRNA-seq data, nuisance variation due to technical factors like …
Modeling allele-specific expression at the gene and SNP levels simultaneously by a Bayesian logistic mixed regression model
Background High-throughput sequencing experiments, which can determine allele origins,
have been used to assess genome-wide allele-specific expression. Despite the amount of …
have been used to assess genome-wide allele-specific expression. Despite the amount of …