We are analyzing https://link.springer.com/article/10.1186/s13059-016-0930-3.

Title:
Beyond comparisons of means: understanding changes in gene expression at the single-cell level | Genome Biology
Description:
Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to-cell heterogeneity but whose overall expression remains unchanged. Control experiments validate our method’s performance and a case study suggests that novel biological insights can be revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS .
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Keywords {🔍}

expression, cells, genes, biological, data, overdispersion, gene, article, cell, analysis, model, technical, additional, posterior, fig, pubmed, samples, populations, parameters, google, scholar, file, estimates, groups, singlecell, content, group, methods, cellcycle, sets, differences, population, set, scrnaseq, mrna, method, approach, heterogeneity, rnaseq, variability, counts, expressed, patterns, number, lfc, control, based, delta, cas, basics,

Topics {✒️}

} \stackrel{\text{iid}}{\sim} \log\text{ } \stackrel{\text{iid}}{\sim} {\log\text{ single-cell rna-seq experiments quantitative single-cell rna-seq n_{1}}{\text{median}} \left\{ \frac{\sum_{ single-cell rna sequencing single cell rna-sequencing single-cell rna-seq }}{\text{median}} \left\{ \frac{\sum_{ single-cell sequencing data multifactor rna-seq experiments bulk rna-seq literature scrna-seq data sets scrna-seq experiments involve single-cell transcriptomics applied $$ {\begin{aligned} \text{cv}^{2}\left article download pdf = \left\{ \begin{array}{ll} cell-specific normalizing constants count-based data sets bulk rna-seq cell-cycle data set rna-seq data stringent regulatory control lfc log-fold change rna-seq experiments $$ {\begin{aligned} \lambda'_{ log-transformed expression counts scrna-seq analyses comparing gene-specific parameters european economic area single-cell transcriptomics scrna-seq data gene-specific model parameters upper triangular panels lower triangular panels full access mccarthy dj single-cell level cell-cycle phases g1 privacy choices/manage cookies unique molecular identifiers false discovery rate $$ \begin{aligned} \tilde{\mu}^{ differential expression analyses = 2 \max\left\{\tilde{\pi}^{ comparative analysis $$ \text{efdr}_{\alpha_{_{ probability cut-offs defined lower panels illustrate

Schema {🗺️}

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         description:Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to-cell heterogeneity but whose overall expression remains unchanged. Control experiments validate our method’s performance and a case study suggests that novel biological insights can be revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS .
         datePublished:2016-04-15T00:00:00Z
         dateModified:2016-04-15T00:00:00Z
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            Cellular heterogeneity
            Animal Genetics and Genomics
            Human Genetics
            Plant Genetics and Genomics
            Microbial Genetics and Genomics
            Bioinformatics
            Evolutionary Biology
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      headline:Beyond comparisons of means: understanding changes in gene expression at the single-cell level
      description:Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarchical model that builds upon BASiCS to study changes that lie beyond comparisons of means, incorporating built-in normalization and quantifying technical artifacts by borrowing information from spike-in genes. Using a probabilistic approach, we highlight genes undergoing changes in cell-to-cell heterogeneity but whose overall expression remains unchanged. Control experiments validate our method’s performance and a case study suggests that novel biological insights can be revealed. Our method is implemented in R and available at https://github.com/catavallejos/BASiCS .
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      dateModified:2016-04-15T00:00:00Z
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         Single-cell RNA-seq
         Differential expression
         Cellular heterogeneity
         Animal Genetics and Genomics
         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
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            type:Organization
            name:Wellcome Trust Genome Campus
            address:
               name:EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK
               type:PostalAddress
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      name:Sylvia Richardson
      affiliation:
            name:Cambridge Institute of Public Health
            address:
               name:MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:John C. Marioni
      affiliation:
            name:Wellcome Trust Genome Campus
            address:
               name:EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK
               type:PostalAddress
            type:Organization
            name:University of Cambridge
            address:
               name:Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
      name:EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK
      name:MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK
      name:EMBL European Bioinformatics Institute, Wellcome Trust Genome Campus, Cambridge, UK
      name:Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK

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