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Keywords {🔍}

cells, expression, genes, gene, cdr, data, cell, model, pubmed, analysis, article, additional, singlecell, file, mast, scholar, modules, google, mait, figure, treatment, set, methods, expressed, approach, stimulation, cas, continuous, fig, stimulated, module, residuals, component, central, enrichment, shalek, rate, discrete, nonstimulated, observed, cellular, rnaseq, control, gsea, biological, variance, response, linear, experiments, effects,

Topics {✒️}

=\frac{\widehat{\theta}-{\widehat{\theta}}_0}{\sqrt{\widehat{var}\left single-cell rna-seq data live cd3+cd8+ cd4-cd161hivα7 single-cell rna-seq experiments single-cell expression profiles single-cell sequencing identifies rna-seq read counts single-cell rna-seq martin prlic article download pdf single-cell sequencing data future single-cell studies repeated single-cell measurements bulk rna-seq data �t-cell surface signature” raphael gottardo mountain view single-cell expression data gene set-based statistics single-cell residuals produced /rglab/mastdata/archive/v1 shrunken gene-specific precision powerful multi-parametric measurements =1}^{g_0}{\widehat{\beta}}_g} scrna-seq data sets gene-specific inverse variances bystander-activated memory cd8 empirical bayes method transcriptional profile precedes subsequent protein-level single-cell transcriptomics helps improve inference 5em {\displaystyle {\sum}_{ +2{\displaystyle {\sum}_{1\le t-cell signaling module single-cell module scores post-sort experiments transcriptome expression profiling rna-seq experiment myeloid dendritic cells single-read sequencing gene-specific variance estimates rna-seq data accurate transcript quantification providing 2α pseudo-observations bulk rna-seq natural killer t-cells combined z-score summarizes open software development {\widehat{\theta}}_0

Questions {❓}

• The question is essentially ontological: is the CDR a mediator of the treatment effect (is it caused by the treatment and intermediate to expression of the gene of interest), or does it confound the treatment effect (does it happen to co-occur with treatment)?

Schema {🗺️}

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         description:Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST .
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      headline:MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data
      description:Single-cell transcriptomics reveals gene expression heterogeneity but suffers from stochastic dropout and characteristic bimodal expression distributions in which expression is either strongly non-zero or non-detectable. We propose a two-part, generalized linear model for such bimodal data that parameterizes both of these features. We argue that the cellular detection rate, the fraction of genes expressed in a cell, should be adjusted for as a source of nuisance variation. Our model provides gene set enrichment analysis tailored to single-cell data. It provides insights into how networks of co-expressed genes evolve across an experimental treatment. MAST is available at https://github.com/RGLab/MAST .
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         Bioinformatics
         Evolutionary Biology
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