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mc2 algorithm’s efficient graph partition algorithm  (MetaCell Inc)

 
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    MetaCell Inc mc2 algorithm’s efficient graph partition algorithm
    Robustness of the divide-and-conquer metacell algorithm. A Distribution of metacell normalized inner variance for the PBMC dataset, using the Baran et al. algorithm (orange) vs. <t>MC2</t> two-sided stability score optimization, working on the entire data in a single pile (i.e., no divide and conquer, green). B Distribution of normalized inner variance for the PMBC dataset using the full MC2 <t>algorithms</t> (blue) vs. the single-pile algorithm (green). C Metacell graph derived by MC2 on the PMBC dataset. Annotation as in Baran et al. D Distribution of metacell normalized inner variance for HSC and MPP cells, when using full MC2 on the HCA bone marrow data set (orange) or when restricting analysis to MPP/HSC cells alone (blue). E Metacell graph for the full HCA BM data set and for metacells computed on the zoomed-in HSC/MPP subset
    Mc2 Algorithm’s Efficient Graph Partition Algorithm, supplied by MetaCell Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/mc2 algorithm’s efficient graph partition algorithm/product/MetaCell Inc
    Average 90 stars, based on 1 article reviews
    mc2 algorithm’s efficient graph partition algorithm - by Bioz Stars, 2026-05
    90/100 stars

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    1) Product Images from "Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis"

    Article Title: Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis

    Journal: Genome Biology

    doi: 10.1186/s13059-022-02667-1

    Robustness of the divide-and-conquer metacell algorithm. A Distribution of metacell normalized inner variance for the PBMC dataset, using the Baran et al. algorithm (orange) vs. MC2 two-sided stability score optimization, working on the entire data in a single pile (i.e., no divide and conquer, green). B Distribution of normalized inner variance for the PMBC dataset using the full MC2 algorithms (blue) vs. the single-pile algorithm (green). C Metacell graph derived by MC2 on the PMBC dataset. Annotation as in Baran et al. D Distribution of metacell normalized inner variance for HSC and MPP cells, when using full MC2 on the HCA bone marrow data set (orange) or when restricting analysis to MPP/HSC cells alone (blue). E Metacell graph for the full HCA BM data set and for metacells computed on the zoomed-in HSC/MPP subset
    Figure Legend Snippet: Robustness of the divide-and-conquer metacell algorithm. A Distribution of metacell normalized inner variance for the PBMC dataset, using the Baran et al. algorithm (orange) vs. MC2 two-sided stability score optimization, working on the entire data in a single pile (i.e., no divide and conquer, green). B Distribution of normalized inner variance for the PMBC dataset using the full MC2 algorithms (blue) vs. the single-pile algorithm (green). C Metacell graph derived by MC2 on the PMBC dataset. Annotation as in Baran et al. D Distribution of metacell normalized inner variance for HSC and MPP cells, when using full MC2 on the HCA bone marrow data set (orange) or when restricting analysis to MPP/HSC cells alone (blue). E Metacell graph for the full HCA BM data set and for metacells computed on the zoomed-in HSC/MPP subset

    Techniques Used: Derivative Assay

    Scaling MC2 to millions of cells. A Graphs show scaling of MC2 (multi-pile) compared to a naïve metacell on a single pile or a PCA+2-Phase Louvain clustering implementation in Seurat, using the PMBC 160K cell data (resampled to datasets of increasing sizes— X -axis). B Comparison of MC2 and two-phase clustering performance for the organogenesis datasets (MOCA). C Effects of scaling the pile sizes on the normalized inner variance for MC2 on the organogenesis data. D Distribution of normalized inner variance for MC2 and PCA+Louvain original sub-clusters on the organogenesis data. E Marker heat map and metacell graph projection of the organogenesis data. Clustering of metacells is used for coloring and cross-reference purpose, in support of, but not in place of supervised annotation. F Distribution of metacells linkage with different embryonic time points over the metacell graph. Color coding is based on metacell clustering as in D . To compensate for differences in the number of cells, we randomly sampled 2000 points for each time point and weighted by the fraction of the cells of each age in each metacells
    Figure Legend Snippet: Scaling MC2 to millions of cells. A Graphs show scaling of MC2 (multi-pile) compared to a naïve metacell on a single pile or a PCA+2-Phase Louvain clustering implementation in Seurat, using the PMBC 160K cell data (resampled to datasets of increasing sizes— X -axis). B Comparison of MC2 and two-phase clustering performance for the organogenesis datasets (MOCA). C Effects of scaling the pile sizes on the normalized inner variance for MC2 on the organogenesis data. D Distribution of normalized inner variance for MC2 and PCA+Louvain original sub-clusters on the organogenesis data. E Marker heat map and metacell graph projection of the organogenesis data. Clustering of metacells is used for coloring and cross-reference purpose, in support of, but not in place of supervised annotation. F Distribution of metacells linkage with different embryonic time points over the metacell graph. Color coding is based on metacell clustering as in D . To compensate for differences in the number of cells, we randomly sampled 2000 points for each time point and weighted by the fraction of the cells of each age in each metacells

    Techniques Used: Comparison, Marker

    MC2-sensitive detection of rare behavior. A Correlation matrix between log gene expression frequency of 260 genes with rare expression signatures (see text). We highlight several gene clusters at the left. B Each bar graph show specificity (left) and fold change enrichment (right) of top genes separating three exemplified rare transcriptional behaviors. Also shown for each rare behavior are the distribution of total gene expression of rare genes per single cell within the top-enriched metacell(s) (shades of green) and within the top enriched PCA+Louvain subcluster (orange). C Shown are single-cell gene expression for rare behavior marker genes and for genes correlated and anticorrelated with them, plotted for cells within the most strongly enriched PCA+Louvain sub-cluster for the observed behavior
    Figure Legend Snippet: MC2-sensitive detection of rare behavior. A Correlation matrix between log gene expression frequency of 260 genes with rare expression signatures (see text). We highlight several gene clusters at the left. B Each bar graph show specificity (left) and fold change enrichment (right) of top genes separating three exemplified rare transcriptional behaviors. Also shown for each rare behavior are the distribution of total gene expression of rare genes per single cell within the top-enriched metacell(s) (shades of green) and within the top enriched PCA+Louvain subcluster (orange). C Shown are single-cell gene expression for rare behavior marker genes and for genes correlated and anticorrelated with them, plotted for cells within the most strongly enriched PCA+Louvain sub-cluster for the observed behavior

    Techniques Used: Gene Expression, Expressing, Marker



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    mc2 algorithm’s efficient graph partition algorithm  (MetaCell Inc)
    90
    MetaCell Inc mc2 algorithm’s efficient graph partition algorithm
    Robustness of the divide-and-conquer metacell algorithm. A Distribution of metacell normalized inner variance for the PBMC dataset, using the Baran et al. algorithm (orange) vs. <t>MC2</t> two-sided stability score optimization, working on the entire data in a single pile (i.e., no divide and conquer, green). B Distribution of normalized inner variance for the PMBC dataset using the full MC2 <t>algorithms</t> (blue) vs. the single-pile algorithm (green). C Metacell graph derived by MC2 on the PMBC dataset. Annotation as in Baran et al. D Distribution of metacell normalized inner variance for HSC and MPP cells, when using full MC2 on the HCA bone marrow data set (orange) or when restricting analysis to MPP/HSC cells alone (blue). E Metacell graph for the full HCA BM data set and for metacells computed on the zoomed-in HSC/MPP subset
    Mc2 Algorithm’s Efficient Graph Partition Algorithm, supplied by MetaCell Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/mc2 algorithm’s efficient graph partition algorithm/product/MetaCell Inc
    Average 90 stars, based on 1 article reviews
    mc2 algorithm’s efficient graph partition algorithm - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Robustness of the divide-and-conquer metacell algorithm. A Distribution of metacell normalized inner variance for the PBMC dataset, using the Baran et al. algorithm (orange) vs. MC2 two-sided stability score optimization, working on the entire data in a single pile (i.e., no divide and conquer, green). B Distribution of normalized inner variance for the PMBC dataset using the full MC2 algorithms (blue) vs. the single-pile algorithm (green). C Metacell graph derived by MC2 on the PMBC dataset. Annotation as in Baran et al. D Distribution of metacell normalized inner variance for HSC and MPP cells, when using full MC2 on the HCA bone marrow data set (orange) or when restricting analysis to MPP/HSC cells alone (blue). E Metacell graph for the full HCA BM data set and for metacells computed on the zoomed-in HSC/MPP subset

    Journal: Genome Biology

    Article Title: Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis

    doi: 10.1186/s13059-022-02667-1

    Figure Lengend Snippet: Robustness of the divide-and-conquer metacell algorithm. A Distribution of metacell normalized inner variance for the PBMC dataset, using the Baran et al. algorithm (orange) vs. MC2 two-sided stability score optimization, working on the entire data in a single pile (i.e., no divide and conquer, green). B Distribution of normalized inner variance for the PMBC dataset using the full MC2 algorithms (blue) vs. the single-pile algorithm (green). C Metacell graph derived by MC2 on the PMBC dataset. Annotation as in Baran et al. D Distribution of metacell normalized inner variance for HSC and MPP cells, when using full MC2 on the HCA bone marrow data set (orange) or when restricting analysis to MPP/HSC cells alone (blue). E Metacell graph for the full HCA BM data set and for metacells computed on the zoomed-in HSC/MPP subset

    Article Snippet: We first wished to ensure that the MC2 algorithm’s efficient graph partition algorithm is not losing significant quality compared to the original, resampling-based Metacell implementation (MC1) [ ].

    Techniques: Derivative Assay

    Scaling MC2 to millions of cells. A Graphs show scaling of MC2 (multi-pile) compared to a naïve metacell on a single pile or a PCA+2-Phase Louvain clustering implementation in Seurat, using the PMBC 160K cell data (resampled to datasets of increasing sizes— X -axis). B Comparison of MC2 and two-phase clustering performance for the organogenesis datasets (MOCA). C Effects of scaling the pile sizes on the normalized inner variance for MC2 on the organogenesis data. D Distribution of normalized inner variance for MC2 and PCA+Louvain original sub-clusters on the organogenesis data. E Marker heat map and metacell graph projection of the organogenesis data. Clustering of metacells is used for coloring and cross-reference purpose, in support of, but not in place of supervised annotation. F Distribution of metacells linkage with different embryonic time points over the metacell graph. Color coding is based on metacell clustering as in D . To compensate for differences in the number of cells, we randomly sampled 2000 points for each time point and weighted by the fraction of the cells of each age in each metacells

    Journal: Genome Biology

    Article Title: Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis

    doi: 10.1186/s13059-022-02667-1

    Figure Lengend Snippet: Scaling MC2 to millions of cells. A Graphs show scaling of MC2 (multi-pile) compared to a naïve metacell on a single pile or a PCA+2-Phase Louvain clustering implementation in Seurat, using the PMBC 160K cell data (resampled to datasets of increasing sizes— X -axis). B Comparison of MC2 and two-phase clustering performance for the organogenesis datasets (MOCA). C Effects of scaling the pile sizes on the normalized inner variance for MC2 on the organogenesis data. D Distribution of normalized inner variance for MC2 and PCA+Louvain original sub-clusters on the organogenesis data. E Marker heat map and metacell graph projection of the organogenesis data. Clustering of metacells is used for coloring and cross-reference purpose, in support of, but not in place of supervised annotation. F Distribution of metacells linkage with different embryonic time points over the metacell graph. Color coding is based on metacell clustering as in D . To compensate for differences in the number of cells, we randomly sampled 2000 points for each time point and weighted by the fraction of the cells of each age in each metacells

    Article Snippet: We first wished to ensure that the MC2 algorithm’s efficient graph partition algorithm is not losing significant quality compared to the original, resampling-based Metacell implementation (MC1) [ ].

    Techniques: Comparison, Marker

    MC2-sensitive detection of rare behavior. A Correlation matrix between log gene expression frequency of 260 genes with rare expression signatures (see text). We highlight several gene clusters at the left. B Each bar graph show specificity (left) and fold change enrichment (right) of top genes separating three exemplified rare transcriptional behaviors. Also shown for each rare behavior are the distribution of total gene expression of rare genes per single cell within the top-enriched metacell(s) (shades of green) and within the top enriched PCA+Louvain subcluster (orange). C Shown are single-cell gene expression for rare behavior marker genes and for genes correlated and anticorrelated with them, plotted for cells within the most strongly enriched PCA+Louvain sub-cluster for the observed behavior

    Journal: Genome Biology

    Article Title: Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis

    doi: 10.1186/s13059-022-02667-1

    Figure Lengend Snippet: MC2-sensitive detection of rare behavior. A Correlation matrix between log gene expression frequency of 260 genes with rare expression signatures (see text). We highlight several gene clusters at the left. B Each bar graph show specificity (left) and fold change enrichment (right) of top genes separating three exemplified rare transcriptional behaviors. Also shown for each rare behavior are the distribution of total gene expression of rare genes per single cell within the top-enriched metacell(s) (shades of green) and within the top enriched PCA+Louvain subcluster (orange). C Shown are single-cell gene expression for rare behavior marker genes and for genes correlated and anticorrelated with them, plotted for cells within the most strongly enriched PCA+Louvain sub-cluster for the observed behavior

    Article Snippet: We first wished to ensure that the MC2 algorithm’s efficient graph partition algorithm is not losing significant quality compared to the original, resampling-based Metacell implementation (MC1) [ ].

    Techniques: Gene Expression, Expressing, Marker