Summarise the connectivity of a set of neurons grouping by type

Usage

cf_partner_summary(
  ids,
  threshold = 1L,
  partners = c("inputs", "outputs"),
  aggregate.query = TRUE,
  normalise = FALSE,
  rval = c("data.frame", "sparse", "matrix"),
  MoreArgs = list()
)

Arguments

ids: A list of ids named by the relevant datasets (see examples) or any other input that can be processed by the keys function (including a hclust dendrogram object.)
threshold: return only edges with at least this many matches. 0 is an option since neuprint sometimes returns 0 weight edges.
partners: Whether to return inputs or outputs
aggregate.query: Whether to aggregate all query neurons of the same type (the default) or when aggregate.query=FALSE only to aggregate the partner neurons.
normalise: Whether to normalise the reported weights as a fraction of the total for each query cell type (or individual query neuron when aggregate.query=TRUE).
rval: Choose what the function will return. sparse and matrix return sparse and dense (standard) matrices, respectively.
MoreArgs: Additional arguments in the form of a hierarchical list (expert use; see details and examples).

Value

a data.frame or (sparse) matrix based on rval. The column n refers to the number of partner neurons.

Details

This function currently groups by dataset, and pre and postsynaptic type. It does not currently group by side. The forms returning matrices rely on coconat::partner_summary2adjacency_matrix.

Examples

if (FALSE) { # \dontrun{
lal78in=cf_partner_summary(cf_ids("/type:LAL00[78]"), threshold=10, partners='in')
lal78in
lal78in %>%
  tidyr::pivot_wider(id_cols = c(type.pre,dataset),
    names_from = type.post, values_from = weight, values_fill = 0)
lal78in %>%
  tidyr::pivot_wider(id_cols = c(type.pre),
    names_from = c(type.post,dataset), values_from = weight, values_fill = 0)
} # }