We are analyzing https://link.springer.com/article/10.1186/s13059-016-0970-8.

Title:
Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts | Genome Biology
Description:
Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling, which limits their scope and generality. We propose a novel method that compares and clusters cells based on their transcript-compatibility read counts rather than on the transcript or gene quantifications used in standard analysis pipelines. In the reanalysis of two landmark yet disparate single-cell RNA-seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is directly applicable to data from a wide variety of assays.
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Keywords {🔍}

cells, transcriptcompatibility, cell, counts, clustering, expression, singlecell, reads, article, google, scholar, method, rnaseq, pubmed, gene, data, clusters, based, analysis, dataset, cluster, cas, obtained, figure, quantification, methods, transcripts, results, scrnaseq, read, fig, information, transcript, equivalence, additional, kallisto, file, oligo, tcc, types, propagation, affinity, set, differentiating, distance, nat, genome, model, genes, classes,

Topics {✒️}

single-cell rna-seq reveals single-cell rna-seq experiment quantitative single-cell rna-seq single-cell sequencing-based technologies single-cell-genetic-analysis-product high-dimensional single-cell analysis single-cell rna-seq technology single-cell rna-seq assays single-cell transcriptomics applied single-cell rna-seq single-cell transcriptional landscape highly multiplex rna-seq bulk rna-seq data standard rna-seq model full-length mrna-seq gene expression profile minimum spanning tree assay-specific read-generating model high-dimensional cytometry data single-cell transcriptomic analysis genetic gene-expression heterogeneity mapping rna-seq reads single-cell transcriptomics transcript-compatibility based t-sne spliced-alignment option enabled single-cell data easier quantify rna-seq data van der maaten full size image transcript-compatibility counts matrix pooling single-cell tccs affinity propagation algorithm kallisto rna-seq program bulk rna-seq transcript/gene abundance estimation data-driven phenotypic dissection scrna-seq expression matrices analyzing scrna-seq data transcript-compatibility counts refines transcript-compatibility counts intuitively specific scrna-seq assay clustering single-cell data expected myelin-related genes obtaining transcript-compatability counts multiple single-cell assays microarray data pairwise jensen-shannon distances underlying read-generating model refining transcript-compatibility counting transcript-compatibility counts based

Questions {❓}

What is a gene, post-ENCODE?

Schema {🗺️}

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         description:Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling, which limits their scope and generality. We propose a novel method that compares and clusters cells based on their transcript-compatibility read counts rather than on the transcript or gene quantifications used in standard analysis pipelines. In the reanalysis of two landmark yet disparate single-cell RNA-seq datasets, we show that our method is up to two orders of magnitude faster than previous approaches, provides accurate and in some cases improved results, and is directly applicable to data from a wide variety of assays.
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            Affinity Propagation
            Read Alignment
            Affinity Propagation Algorithm
            Pairwise Distance Matrix
            Animal Genetics and Genomics
            Human Genetics
            Plant Genetics and Genomics
            Microbial Genetics and Genomics
            Bioinformatics
            Evolutionary Biology
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         Minimum Span Tree
         Affinity Propagation
         Read Alignment
         Affinity Propagation Algorithm
         Pairwise Distance Matrix
         Animal Genetics and Genomics
         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
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            address:
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