We are analyzing https://link.springer.com/article/10.1186/s13059-017-1188-0.

Title:
CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data | Genome Biology
Description:
Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.com/VCCRI/CIDR .
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Keywords {🔍}

cidr, data, clustering, cells, set, cell, dropout, scrnaseq, expression, article, clusters, dropouts, types, analysis, google, scholar, algorithm, number, singlecell, principal, additional, algorithms, file, figure, function, gene, fig, distances, tsne, imputation, index, human, coordinates, neurons, dissimilarity, expected, adjusted, rand, pubmed, single, samples, cluster, output, zifa, sets, brain, dimensionality, reduction, distance, rate,

Topics {✒️}

scrna-seq single-cell rna-seq single-cell rna-seq data single-cell rna-seq full size image scrna-seq data sets scrna-seq data set scrna-seq data analysis bulk rna-seq samples recent scrna-seq studies single-cell transcriptome data article download pdf cell-type molecular signatures scrna-seq data rna-seq experiment calinski–harabasz index versus dropout-affected data set interpretable meta-clustering framework studying cell-type heterogeneity scrna-seq technology single-cell transcriptomes modified bi-clustering algorithm art dimensionality-reduction package /vccri/cidr/releases/tag/0 brain data set iterative expectation-maximization algorithm d_{\text{data}}\left art dimensionality-reduction algorithms full access log-transformed expression values $$d_{\text{true}}\left single-cell genomics =\left\{ \begin{array}{ll} 0 biological data sets data sets transformed figure s9 shows privacy choices/manage cookies simulated data set understanding brain development cell-cycle stage medical research council single cell level creative commons license dropout data set cell-type assignments principal coordinate analysis scrna-seq o_{ki}-o_{kj} human brain [20] human brain simulation data set

Questions {❓}

Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion?

Schema {🗺️}

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         headline:CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
         description:Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.com/VCCRI/CIDR .
         datePublished:2017-03-28T00:00:00Z
         dateModified:2017-03-28T00:00:00Z
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            Plant Genetics and Genomics
            Microbial Genetics and Genomics
            Bioinformatics
            Evolutionary Biology
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      headline:CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data
      description:Most existing dimensionality reduction and clustering packages for single-cell RNA-seq (scRNA-seq) data deal with dropouts by heavy modeling and computational machinery. Here, we introduce CIDR (Clustering through Imputation and Dimensionality Reduction), an ultrafast algorithm that uses a novel yet very simple implicit imputation approach to alleviate the impact of dropouts in scRNA-seq data in a principled manner. Using a range of simulated and real data, we show that CIDR improves the standard principal component analysis and outperforms the state-of-the-art methods, namely t-SNE, ZIFA, and RaceID, in terms of clustering accuracy. CIDR typically completes within seconds when processing a data set of hundreds of cells and minutes for a data set of thousands of cells. CIDR can be downloaded at https://github.com/VCCRI/CIDR .
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         Single-cell
         scRNA-seq
         Dropout
         Imputation
         Dimensionality reduction
         Clustering
         Cell type
         Animal Genetics and Genomics
         Human Genetics
         Plant Genetics and Genomics
         Microbial Genetics and Genomics
         Bioinformatics
         Evolutionary Biology
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            address:
               name:St Vincent’s Clinical School, University of New South Wales, Darlinghurst, Australia
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      affiliation:
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            address:
               name:Victor Chang Cardiac Research Institute, Darlinghurst, Australia
               type:PostalAddress
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            name:Victor Chang Cardiac Research Institute
            address:
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      name:Victor Chang Cardiac Research Institute, Darlinghurst, Australia
      name:St Vincent’s Clinical School, University of New South Wales, Darlinghurst, Australia

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