Project description:Single-cell transcriptomics allows the identification of cellular types, subtypes and states through cell clustering. In this process, similar cells are grouped before determining co-expressed marker genes for phenotype inference. The performance of computational tools is directly associated to their marker identification accuracy, but the lack of an optimal solution challenges a systematic method comparison. Moreover, phenotypes from different studies are challenging to integrate, due to varying resolution, methodology and experimental design. In this work we introduce matchSCore (https://github.com/elimereu/matchSCore), a measure to fastly match cell populations across tools, experiments and technologies. We compared 14 computational methods and evaluated their accuracy in clustering and gene marker identification in simulated data sets. Further, we used matchSCore to project cell type identities across mouse or human cell atlas projects. Despite originated from different technologies, cell populations could be matched across datasets, allowing the assignment of clusters to reference maps and their annotation.
Project description:Analysis of expression changes between colon tumors (Duke's stage II) and matching colon mucosa tissues using Affymetrix GeneChip® Human Gene 2.0 ST arrays. All experiments were performed simultaneously for matching tissue samples.
Project description:We used NimbleGen human CpG/promoter microarrays for profiling epigenetic changes (DNA methylation, H3K27me3 and H3K4me3 marks) in colon tumors (Duke's stage II) and matching mucosa samples from patients. Analysis of DNA methylation was done by using the methylated CpG island recovery assay (MIRA) technique with further hybridization versus input on NimbleGen human CpG/promoter microarrays. For profiling of H3K27me3 and H3K4me3 marks, we performed chromatin immunoprecipitation with H3K27me3 (#07-449, Millipore) and H3K4me3 (#39159, Active Motif) antibodies and further hybridized versus input on NimbleGen human CpG/promoter microarrays. All experiments were performed simultaneously for matching tissue samples.
Project description:Matching sets of RfxCasR and shRNAs targeting ANXA4 and B4GALNT1 plus non-targeting (NT) controls were profiled by mRNA sequencing to compare non-specific transcriptome perturbations for both shRNA and RfxCasR technologies.
Project description:Glioblastomas (GBM) are the most aggressive type of brain tumors with a dismal prognosis. The effectiveness of anti-tumor drugs is thwarted by brain endothelial cells (ECs) in the tumor microenvironment. However, the characteristics of ECs remains poorly inventoried at the single-cell level. We performed single-cell RNA sequencing (scRNA-seq) for ECs from 4 GBMs and matching non-malignant brain samples, and identified distinct EC phenotypes in GBM.