Project description:When applying deconvolution methods to bulk RNAseq data, a limitation of most prior studies is the lack of paired scRNA-seq and bulk RNA-seq data from the same samples to serve as ground truth for deconvolution. Our UC cohort, containing matched scRNA-seq and bulk RNA-seq data therefore provided a unique opportunity. (The scRNAseq datasets have been published before [PMID: 32111252 PMID: 36129800 PMID: 36099881 PMID: 33837006 ]. Please see linked manuscript for details) We assembled a scRNA-seq dataset of 100,667 cells from 30 UC tissue samples (20 unique patients). Bulk RNA sequencing was performed on a subset of patients(14) in the single-cell RNA sequencing cohort due to tissue availability.
Project description:In this project, two NSCLC cohorts were analyzed by Data Independent Acquisition (DIA-MS). A cohort of early stage NSCLC samples (141) as well as an additional cohort of late stage NSCLC samples (84) with the aim to demonstrate utility of MS for subtyping and treatment prediction in a clinical setting. Further, six identified NSCLC proteome subtypes were investigated in relation to cancer driver pathways and immune phenotypes based on the generated MS-data.