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fluidigm c1 scrna seq data  (fluidigm)


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    fluidigm fluidigm c1 scrna seq data
    Fluidigm C1 Scrna Seq Data, supplied by fluidigm, used in various techniques. Bioz Stars score: 96/100, based on 3404 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Average 96 stars, based on 3404 article reviews
    fluidigm c1 scrna seq data - by Bioz Stars, 2026-03
    96/100 stars

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    Study design of the scCNV benchmark analysis. The top panel (A) illustrates the evaluation scheme for sensitivity and specificity of scCNV detection using the scRNA-seq datasets (10x, C1-HT, C1, and <t>ICELL8</t> full-length) of a breast cancer cell line vs. the paired B-cell line derived from the same donor, which was generated from our previous multicenter benchmarking study. The middle panel (B) illustrates the evaluation scheme for accuracy of subclone identification using the mixed scRNA-seq data from the Tian et al. study derived from a mixture including either three or five human lung adenocarcinoma cell lines. Drop-seq_3cl, scRNA-seq data from the mixed three human lung adenocarcinoma cell lines; CEL-seq2_3cl, scRNA-seq data from the mixed three human lung adenocarcinoma cell lines; 10x_3cl, 10x scRNA-seq data from the mixed three human lung adenocarcinoma cell lines; and 10x_5cl, 10x scRNA-seq data from the mixed five human lung adenocarcinoma cell lines. The lower panel (C) illustrates the application of scCNV methods to a human small cell lung cancer (SCLC) scRNA-seq dataset (20M read/each cell, full-length transcript, SMART-seq2) including 92 primary SCLC single cells and 39 relapse SCLC single cells, plus scWES and bulk cell WGS from primary SCLC and relapsed tumoral tissues as well as peri-tumoral normal tissues.
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    Study design of the scCNV benchmark analysis. The top panel (A) illustrates the evaluation scheme for sensitivity and specificity of scCNV detection using the scRNA-seq datasets (10x, C1-HT, C1, and ICELL8 full-length) of a breast cancer cell line vs. the paired B-cell line derived from the same donor, which was generated from our previous multicenter benchmarking study. The middle panel (B) illustrates the evaluation scheme for accuracy of subclone identification using the mixed scRNA-seq data from the Tian et al. study derived from a mixture including either three or five human lung adenocarcinoma cell lines. Drop-seq_3cl, scRNA-seq data from the mixed three human lung adenocarcinoma cell lines; CEL-seq2_3cl, scRNA-seq data from the mixed three human lung adenocarcinoma cell lines; 10x_3cl, 10x scRNA-seq data from the mixed three human lung adenocarcinoma cell lines; and 10x_5cl, 10x scRNA-seq data from the mixed five human lung adenocarcinoma cell lines. The lower panel (C) illustrates the application of scCNV methods to a human small cell lung cancer (SCLC) scRNA-seq dataset (20M read/each cell, full-length transcript, SMART-seq2) including 92 primary SCLC single cells and 39 relapse SCLC single cells, plus scWES and bulk cell WGS from primary SCLC and relapsed tumoral tissues as well as peri-tumoral normal tissues.

    Journal: Precision Clinical Medicine

    Article Title: A benchmarking study of copy number variation inference methods using single-cell RNA-sequencing data

    doi: 10.1093/pcmedi/pbaf011

    Figure Lengend Snippet: Study design of the scCNV benchmark analysis. The top panel (A) illustrates the evaluation scheme for sensitivity and specificity of scCNV detection using the scRNA-seq datasets (10x, C1-HT, C1, and ICELL8 full-length) of a breast cancer cell line vs. the paired B-cell line derived from the same donor, which was generated from our previous multicenter benchmarking study. The middle panel (B) illustrates the evaluation scheme for accuracy of subclone identification using the mixed scRNA-seq data from the Tian et al. study derived from a mixture including either three or five human lung adenocarcinoma cell lines. Drop-seq_3cl, scRNA-seq data from the mixed three human lung adenocarcinoma cell lines; CEL-seq2_3cl, scRNA-seq data from the mixed three human lung adenocarcinoma cell lines; 10x_3cl, 10x scRNA-seq data from the mixed three human lung adenocarcinoma cell lines; and 10x_5cl, 10x scRNA-seq data from the mixed five human lung adenocarcinoma cell lines. The lower panel (C) illustrates the application of scCNV methods to a human small cell lung cancer (SCLC) scRNA-seq dataset (20M read/each cell, full-length transcript, SMART-seq2) including 92 primary SCLC single cells and 39 relapse SCLC single cells, plus scWES and bulk cell WGS from primary SCLC and relapsed tumoral tissues as well as peri-tumoral normal tissues.

    Article Snippet: CaSpER outperformed CopyKAT in both sensitivity and specificity with scRNA-seq data derived from Fluidigm C1 and 10x platforms, whereas CopyKAT showed better specificity with the ICELL8 scRNA-seq data (Fig. ).

    Techniques: Derivative Assay, Generated

    CAFs subtypes in colorectal cancer.

    Journal: Frontiers in Cell and Developmental Biology

    Article Title: The role of cancer-associated fibroblasts in the invasion and metastasis of colorectal cancer

    doi: 10.3389/fcell.2024.1375543

    Figure Lengend Snippet: CAFs subtypes in colorectal cancer.

    Article Snippet: ● Patient samples ● scRNA-seq with Fluidigm C1 Platform , CAF-A , MMP2 DCN COL1A2 , ECM remodeling , Intermediate state? , .

    Techniques: Expressing