Infer Cell-Cell Communication

Use this function to identify ligand/receptor pairs expressed by cell clusters in human, mouse or zebrafish single cell data. A CellChat object is generated which can be used to visualize these connections using bb_cellchat_heatmap or other tools from package CellChat.

Usage

bb_cellchat(
  cds,
  group_var,
  n_cores = 12,
  species = c("human", "mouse", "zebrafish"),
  min_cells = 10,
  prob_type = c("triMean", "truncatedMean", "median"),
  prob_trim = NULL,
  project = TRUE,
  pop_size_arg = TRUE,
  ask = TRUE
)

Arguments

cds

The cell data set object. It should usually be pre-filtered to conatin a single biological sample.

group_var

The cell metadata column identifying cell groups for cell-cell communication inference.

n_cores

Number of cores for the analysis, Default: 12

species

Species for the assay, Default: c("human", "mouse", "zebrafish")

min_cells

Cell clusters smaller than this value will be ignored., Default: 10

prob_type

Methods for computing the average gene expression per cell group. By default = "triMean", producing fewer but stronger interactions; When setting ‘type = "truncatedMean"', a value should be assigned to ’trim', producing more interactions, Default: c("triMean", "truncatedMean", "median")

prob_trim

the fraction (0 to 0.25) of observations to be trimmed from each end of x before the mean is computed if using truncatedMean, Default: NULL

project

Whether or not to smooth gene expression, Default: TRUE

pop_size_arg

Whether consider the proportion of cells in each group across all sequenced cells. Set population.size = FALSE if analyzing sorting-enriched single cells, to remove the potential artifact of population size. Set population.size = TRUE if analyzing unsorted single-cell transcriptomes, with the reason that abundant cell populations tend to send collectively stronger signals than the rare cell populations., Default: TRUE

Value

A CellChat object

Details

see github::sqjin/CellChat

See also

normalized_counts mutate-joins,pull tibble createCellChat,CellChatDB.human,CellChatDB.mouse,character(0),subsetData,identifyOverExpressedGenes,identifyOverExpressedInteractions,projectData,computeCommunProb,filterCommunication,computeCommunProbPathway,aggregateNet character(0) plan