Main functionality 1: To detect dubious metacells for a given metacell partition

Usage

mcRigor_DETECT(
  obj_singlecell,
  cell_membership = NULL,
  tgamma = NULL,
  assay_type = c("RNA", "ATAC"),
  aggregate_method = c("mean", "sum", "geom"),
  output_file = NULL,
  Nrep = 1,
  gene_filter = 0.1,
  feature_use = 2000,
  cor_method = c("pearson", "spearman"),
  prePro = T,
  test_cutoff = 0.01,
  thre_smooth = T,
  thre_bw = 1/6,
  draw = T,
  pur_metric = NULL,
  check_purity = T,
  fields = NULL,
  step_save = T
)

Arguments

obj_singlecell: Seurat object of the original single cells.
cell_membership: A dataframe, each column of which contains the metacell membership of single cells under a given gamma (granularity level). The column names should be the corresponding gamma's (in the character type). The row names should be the single cell id's.
tgamma: The target gamma value — the gamma (in the character type) corresponding to the metacell partition where dubious metacells detection will be performed. tgamma has to be in the column names of cell_membership. If tgamma = NULL, dubious metacells detection will be performed on the metacell partition represented by the first column of cell_membership.
assay_type: The type of data assay yuo are using, depending on which different normalization would be used.
aggregate_method: The method to aggregate single cell profiles into metacell profiles
output_file: The directory for output saving
Nrep: The number of permutation repetitions we use when deriving the null.
gene_filter: A proportion. Genes expressed lower than this proportion will be filtered out.
feature_use: The number of genes to use in metacell testing.
cor_method: The method for gene correlation calculation description
prePro: A boolean indicating whether to normalize obj_singlecell for preprocessing.
test_cutoff: The test size for dubious metacell detection testing
thre_smooth: A boolean indicating whether to smooth the threshold function
thre_bw: If thre_smooth is True, thre_bw specifies the bandwidth for smoothing
draw: A boolean indicating whether to visualize the mcRigor results
pur_metric: Can be NULL or a metadata variable name, ex. cell type.
check_purity: A boolean indicating whether to calculate the metacell purity of specific fields or not.
fields: A vector of the fields of interest, ex. celltype. It should be a subset of obj_singlecell's meta.data.
step_save: A boolean indicating whether to save the outputs step by step

Value

A list containing the following fields:

mc_res: A named vector indicating whether each metacll is dubious or trustworthy
obj_metacell: The metacell Seurat object with the mcRigor detection results in the metadata
thre: The thresholds for dubious metacell detection
TabMC: A dataframe containing the permutation results, elements to calculate the test statistics mcDiv and mcDiv null
test_plot: The scatter plots demonstrating the mcDiv values and the obtained thresholds for dubious metacell detection
purity_plot: A violin plot showing the purity distribution of the pur_metric covariate in dubious metacells and trustworthy metacells