Identifying cancerous samples or cells using transcriptomic data is critical for cancer related basic research, early diagnosis, and targeted therapy. However, the high transcriptional heterogeneity of cancers still hinders people from accurately recognizing cancerous transcriptome using bulk, single-cell, or spatial RNA-seq data. Here, we present a novel method named FWP (Feature Weight Pro) that helps measure cancerous transcriptome using transcriptomic data. The workflow of FWP is, first, to calculate feature weights using the training dataset, and then, for each sample in the testing dataset, calculate the feature-weight based final score by combining the cohort-wide and sample-specific information. Those two types of information are utilized through conducting weighted principal component analysis and calculating correlation perturbations. The effectiveness and superiority of FWP over other methods are shown by using bulk, single-cell, and spatial RNA-seq data of multiple cancer types. In addition, the high robustness and efficiency of FWP are also demonstrated by using different numbers of features and cells, respectively. FWP is available at https://github.com/jumphone/fwp .
Keywords: Cancerous transcriptome; Correlation perturbation; Feature weight; Principal component analysis; Single-cell RNA-seq; Spatial RNA-seq.
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