subtype_pins: Subtypes using PINSPlus package
In agapow/subtypr: Robust and Validated Patient Subtyping for Precision Medicine

Description Usage Arguments Details Value See Also

Perform subtyping using multiple types of data

1 2	subtype_pins(data_list, minimal_return = FALSE, return_stage_2 = TRUE, k_max = 5, agreement_cutoff = 0.5, verbose = T, ...)

`data_list`	a list of data matrices with continuous data of format samples x features (with the same number of samples).
`minimal_return`	logical, if TRUE, the result of the function will just be what's needed to evaluate the goodness of the partition, i.e. the partition and the element for internal metrics.
`return_stage_2`	logical to return the partition of the stage 2 of the PINS method's workflow.
`k_max`	The maximum number of clusters tested (from 2 to k_max). Default value is 5.
`agreement_cutoff`	Agreement threshold to be considered consistent. Default value is 0.5.
`verbose`	Set it to TRUE of FALSE to get more or less details respectively.
`...`	these arguments will be passed to PerturbationClustering algorithm. See `PerturbationClustering`.

subtype_pins uses SubtypingOmicsData. The input is a list of data matrices where each matrix represents the molecular measurements of a data type. The input matrices must have the same number of rows. The function aims to find the optimum number of subtypes and location of each sample in the clusters from integrated input data dataList through two processing stages:

Stage I: The algorithm first partitions each data type using the function PerturbationClustering. It then merges the connectivities across data types into similarity matrices. Both kmeans and similarity-based clustering algorithms - partitioning around medoids pam are used to partition the built similarity. The algorithm returns the partitioning that agrees the most with individual data types.
Stage II: The algorithm attempts to split each discovered group if there is a strong agreement between data types, or if the subtyping in Stage I is very unbalanced.

a result list containing:

$partition: The predicted partition
$element_for_metric: The name of the element in the result list. containing the data to be used with internal metrics.
$dataTypeResult: A list of results for individual data type. Each element of the list is the result of PerturbationClustering for the corresponding data matrix provided in dataList.

PerturbationClustering, SubtypingOmicsData.

agapow/subtypr documentation built on May 5, 2019, 1:33 a.m.