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 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.

Usage

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,
  FIXED_NORMAL_CELLS = FALSE,
  output_dir = "./output"
)

Arguments

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)

FIXED_NORMAL_CELLS

TRUE if vector of norm_cell to be used as reference fixed, if you are interested only in clonal structure e non nella classificazione normal/tumor (default FALSE)

gr_truth

ground truth of classification (optional)