pagoda.reduce.redundancy {pagoda2}

Collapse aspects driven by similar patterns (i.e. separate the same sets of cells) Examines PC loading vectors underlying the identified aspects and clusters aspects based on score correlation. Clusters of aspects driven by the same patterns are determined based on the distance.threshold.

Description

Collapse aspects driven by similar patterns (i.e. separate the same sets of cells) Examines PC loading vectors underlying the identified aspects and clusters aspects based on score correlation. Clusters of aspects driven by the same patterns are determined based on the distance.threshold.

Usage

pagoda.reduce.redundancy(
  tamr,
  distance.threshold = 0.2,
  cluster.method = "complete",
  distance = NULL,
  weighted.correlation = TRUE,
  plot = FALSE,
  top = Inf,
  trim = 0,
  abs = FALSE,
  ...
)

Arguments

tamr

Combined pathways that show similar expression patterns, output of pagoda.reduce.loading.redundancy()

distance.threshold

numeric Similarity threshold for grouping interdependent aspects (default=0.2)

cluster.method

character One of the standard clustering methods to be used (default="complete")

distance

distance matrix (default=NULL)

weighted.correlation

boolean Whether to use a weighted correlation in determining the similarity of patterns (default=TRUE)

plot

boolean Whether to show plot (default=FALSE)

top

bololean Restrict output to the top N aspects of heterogeneity (default=Inf, i.e. no restriction)

trim

numeric Winsorization trim to use prior to determining the top aspects (default=0)

abs

boolean Whether to use absolute correlation (default=FALSE)

...

additional arguments are passed to the pagoda.view.aspects() method during plotting

Value

List structure analogous to that returned by pagoda.top.aspects(), but with addition of a $cnam element containing a list of aspects summarized by each row of the new (reduced) $xv and $xvw