View source: R/classifyTumor.R
classifyTumorCells | R Documentation |
classifyTumorCells Classify tumour and normal cells from the raw count matrix, using normal cells in the matrix or by subtracting a synthetic baseline from the matrix if there are no normal cells in the matrix.
classifyTumorCells( count_mtx, annot_mtx, sample = "", distance = "euclidean", par_cores = 20, ground_truth = NULL, norm_cell_names = NULL, SEGMENTATION_CLASS = TRUE, SMOOTH = TRUE, beta_vega = 0.5 )
count_mtx |
raw count matrix |
annot_mtx |
matrix containing the annotations of the genes (rows: genes, columns: chr start end) |
sample |
sample name (optional) |
distance |
distance used in hierarchical clustering (default euclidean) |
par_cores |
number of cores (default 20) |
norm_cell_names |
confident normal cells (optional) |
SEGMENTATION_CLASS |
Boolean value to perform segmentation before classification (default TRUE) |
SMOOTH |
Boolean value to perform smoothing (default TRUE) |
beta_vega |
specifies beta parameter for segmentation, higher beta for more coarse-grained segmentation. (default 0.5) |
gr_truth |
ground truth of classification (optional) |
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