Statistics

Overview of PCTfuncDB.

We collected 62 single-cell RNA-sequencing (scRNA-seq) and 12 whole exome sequencing (WES) datasets across 6 different platforms from 7 public databases. For scRNA-seq data, after removing low-quality cells and samples, a total of 4,972,145 cells and 1,352 patient samples from 13 tissues with 46 different disease states were retained in PCTanno. To identify malignant transformation related genes, we first catalog epithelial, immune and stromal cell types for each scRNA-seq dataset. Then, we integrate corresponding single-cell transcriptomes for three major cell types above within each tissue, respectively and run batch correction. We also carried out functional impact prediction of mutations in our identified malignant transformation related genes based on bulk DNA and scRNA-seq samples.

The PCTanno database includes 62 datasets from 57 single cell RNA sequencing (scRNA-seq) studies and 12 whole exome sequencing (WES) datasets that across 13 tissues from healthy to premalignant lesions to cancer. scRNA-seq data was integrated to perform transcriptional dynamics analysis based on stem-like cells, and processed with differentiation trajectory inference, cell-cell communication, gene regulatory network inference, gene ontology and pathway enrichment analysis. We carried out functional impact prediction of mutations based on WES data. The application of PCTanno will help to identify novel biomarkers across transitions from healthy to diseased tissues, and dissect the sequential molecular and cellular events that promote oncogenesis leading to precision prevention and interception strategies.

Figure 1. Overview of PCTanno functional annotation.

Function of PCTanno.

By looking at malignancies through the lens of their originating lesions provide, PCTanno can provide comprehensive characterizations for genes involved in malignant transformation across diverse tissue types in human, and chart cell composition and cell state changes that occur during the transformation of healthy tissues to precancer to cancer.

Summary of data design and collection for malignant transformation analysis.

Summary of scRNA-seq datasets for malignant transformation analysis

Summary of the number of cells, samples collected by tissue (left) and their tissue compositions (right).

Summary of platforms of the included scRNA-seq datasets.