classifyTumorCells: classifyTumorCells Classify tumour and normal cells from the...

In AntonioDeFalco/SCEVAN: SCEVAN

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.

Description

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
)

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)

AntonioDeFalco/SCEVAN documentation built on April 14, 2025, 6:33 a.m.