Additional file 3: Table S3. of Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold

细胞类型特异性差异表达

An example of LRCDE and csSAM analysis results of simulated data with target cell proportion standard deviation of 0.15. Sample size – 14, target condition number (kappa) – 100, target mean squared er

DataCite Commons2024-12-13 更新120

Additional file 4 of Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes

基因表达去卷积

单细胞转录组学

Additional file 4: Table S3. RNA-seq and scRNA-seq estimated expression profiles for cell mixtures and cell lines.

Figshare2023-08-02 更新50

Computational de novo discovery of distinguishing genes for biological processes and cell types in complex tissues

基因表达去卷积

细胞类型识别

Bulk tissue samples examined by gene expression studies are usually heterogeneous. The data gained from these samples display the confounding patterns of mixtures consisting of multiple cell types or

Figshare2018-03-02 更新50

Additional file 4: Table S4. of Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold

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Additional file 4: Table S4. of Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold

资源简介：

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