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that classifies tumour and normal cells from the raw count matrix, infer the clonal profile of cancer cells and looks for possible sub-clones in the tumour cell matrix automatically analysing the specific and shared alterations of each subclone and a differential analysis of pathways and genes expressed in each subclone.", "topics": [ "pipelineCNA" ] }, { "page": "plotAllClonalCN", "title": "Title plotAllClonalCN", "topics": [ "plotAllClonalCN" ] }, { "page": "plotAllSubclonalCN", "title": "plotAllSubclonalCN Plot the copy number of each subclone of a sample.", "topics": [ "plotAllSubclonalCN" ] }, { "page": "plotCNA_withAnnotCells", "title": "plotCNA_withAnnotCells allows generating a heatmap of the copy number profile of each cell, adding cell annotations as tracks on the heatmap.", "topics": [ "plotCNA_withAnnotCells" ] }, { "page": "preprocessingMtx", "title": "preprocessingMtx Pre-processing steps: Cells with less than 200 genes and the genes expressed in less than 1 according to genomic coordinates. Highly confident normal cells are sought in the matrix. Genes involved in the cell cycle pathway are removed. Log-Freeman–Tukey transformation to stabilize variance and a polynomial dynamic linear modeling (DLM) to smooth out the outliers.", "topics": [ "preprocessingMtx" ] }, { "page": "removeSyntheticBaseline", "title": "removeSyntheticBaseline Removes a synthetic baseline from a tumour pure matrix", "topics": [ "removeSyntheticBaseline" ] }, { "page": "SCEVAN", "title": "SCEVAN: R package that automatically classifies the cells in the scRNA data by segregating non-malignant cells of tumor microenviroment from the malignant cells. 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