Project description:Compositional statistics and random gene-sets were used to assign the tumor site of origin and histopathology of 18 epithelial ovarian cancer cell lines
Project description:An expert-pathologist-reviewed epithelial ovarian cancer reference library (n = 50) used to assign the histopathology of epithelial ovarian cell lines using compositional statistics and random gene-sets
Project description:We propose a statistical algorithm MethylPurify that uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. With purity estimate, MethylPurify can identify differentially methylated regions (DMRs) from individual tumor samples without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. On real patient data where tumor to normal comparison were used as golden standard, MethylPurify called DMR from tumor samples alone at over 57% sensitivity and 91% specificity.
Project description:We propose a statistical algorithm MethylPurify that uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. With purity estimate, MethylPurify can identify differentially methylated regions (DMRs) from individual tumor samples without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. On real patient data where tumor to normal comparison were used as golden standard, MethylPurify called DMR from tumor samples alone at over 57% sensitivity and 91% specificity. Lung adenocarcinoma cancer and normal tissues from 5 patients were captured by Agilent SureSelect Methyl-Seq system, followed by bisulfite sequencing.
Project description:The identity and functions of specialized cell types are dependent on the complex interplay between signaling and transcriptional networks. Recently single-cell technologies such as CITE-seq have been developed that enable simultaneous quantitative analysis of cell-surface receptor expression with transcriptional states. To date, these datasets have not been used to systematically develop cell-context-specific maps of the interface between signaling and transcriptional regulators orchestrating cellular identity and function. We present SPaRTAN (Single-cell Proteomic and RNA based Transcription factor Activity Network), a computational method to link cell-surface receptors to transcription factors (TFs) by exploiting cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) datasets with cis-regulatory information. SPaRTAN is applied to immune cell types in the blood to predict the coupling of signaling receptors with cell context-specific TFs. The predictions are validated by prior knowledge and flow cytometry analyses. SPaRTAN is then used to predict the signaling coupled TF states of tumor infiltrating CD8+ T cells in malignant peritoneal and pleural mesotheliomas. SPaRTAN greatly enhances the utility of CITE-seq datasets to uncover TF and cell-surface receptor relationships in diverse cellular states.
Project description:Identification of carcinogenesis and tumor progression processes in pancreatic ductal adenocarcinoma using high-throughput proteomics
Project description:Precision mapping of glycans at the site-specific level using mass spectrometry data has emerged as a crucial approach for glycan discovery in modern glycoproteomics and glycobiology. However, the extensive diversity of glycan compositions within and across species far surpasses the capacity of existing software databases. Consequently, the identification of glycans not included in the database or lacking prior compositional knowledge during large-scale glycoproteomic analyses poses a significant challenge. Here, we present pGlycoNovo, a software platform for analyzing intact glycopeptides featuring rare glycan attachments within the pGlyco3 software environment.
