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Clustering benchmark results for <t>the</t> <t>Stereo-seq</t> dataset across three gene selection levels (2000 genes, 5000 genes, and all genes). Eight methods—including the base (no imputation) and seven imputation approaches—are evaluated using (a) ARI, (b) NMI, (c) AMI, and (d) HOMO scores. MAGIC method consistently performs well across all metrics
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Clustering benchmark results for <t>the</t> <t>Stereo-seq</t> dataset across three gene selection levels (2000 genes, 5000 genes, and all genes). Eight methods—including the base (no imputation) and seven imputation approaches—are evaluated using (a) ARI, (b) NMI, (c) AMI, and (d) HOMO scores. MAGIC method consistently performs well across all metrics
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Clustering benchmark results for <t>the</t> <t>Stereo-seq</t> dataset across three gene selection levels (2000 genes, 5000 genes, and all genes). Eight methods—including the base (no imputation) and seven imputation approaches—are evaluated using (a) ARI, (b) NMI, (c) AMI, and (d) HOMO scores. MAGIC method consistently performs well across all metrics
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Clustering benchmark results for <t>the</t> <t>Stereo-seq</t> dataset across three gene selection levels (2000 genes, 5000 genes, and all genes). Eight methods—including the base (no imputation) and seven imputation approaches—are evaluated using (a) ARI, (b) NMI, (c) AMI, and (d) HOMO scores. MAGIC method consistently performs well across all metrics
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Clustering benchmark results for the Stereo-seq dataset across three gene selection levels (2000 genes, 5000 genes, and all genes). Eight methods—including the base (no imputation) and seven imputation approaches—are evaluated using (a) ARI, (b) NMI, (c) AMI, and (d) HOMO scores. MAGIC method consistently performs well across all metrics

Journal: Briefings in Bioinformatics

Article Title: Spatial information matters: are traditional imputation methods effective for spatial transcriptomics data?

doi: 10.1093/bib/bbag027

Figure Lengend Snippet: Clustering benchmark results for the Stereo-seq dataset across three gene selection levels (2000 genes, 5000 genes, and all genes). Eight methods—including the base (no imputation) and seven imputation approaches—are evaluated using (a) ARI, (b) NMI, (c) AMI, and (d) HOMO scores. MAGIC method consistently performs well across all metrics

Article Snippet: Stereo-seq (Spatiotemporal Enhanced Resolution Omics-sequencing) is a high-resolution spatial transcriptomics technology that captures genome-wide gene expression with spatial fidelity using DNA nanoball (DNB)-patterned arrays [ ].

Techniques: Selection