bb_cellchat | R Documentation |
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.
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
)
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 |
see github::sqjin/CellChat
A CellChat object
normalized_counts
mutate-joins
,pull
tibble
createCellChat
,CellChatDB.human
,CellChatDB.mouse
,character(0)
,subsetData
,identifyOverExpressedGenes
,identifyOverExpressedInteractions
,projectData
,computeCommunProb
,filterCommunication
,computeCommunProbPathway
,aggregateNet
character(0)
plan
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