computePersistence: Computes Persistence Scores For a Data.Frame of Time-Series...

Description Usage Arguments Value References See Also

View source: R/computePersistence.R

Description

Takes a data.frame of numeric gene expression over time (genes X ZT times) and computes the persistence score using getPersistence. For a given gene, each lag (min to max) is used to transform the expression into a 3-D embedded space via time-delay embedding. A non-linear dimension reduction technique (laplacian eigenmaps) is used to transfrom the 3-D embedding to a 2-D embedding. Finally, the persistence score of the 2-D embedding is calculated via persistence homology. The median persistence score across all lags (min to max) for each gene is returned as a numeric vector. For more details see TimeCycle's vignette: vignette("TimeCycle").

Usage

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computePersistence(
  data,
  minLag = 2,
  maxLag = 5,
  cores = parallel::detectCores() - 2,
  laplacian = T
)

Arguments

data

a data.frame of numeric gene expression over time (row = genes x col = ZT times).

minLag

a numeric specifying the min lag to check in the 3-D embedding. Default is 2.

maxLag

a numeric specifying the max lag to check in the 3-D embedding. Default is 5.

cores

a numeric specifying the number of parallel cores to use. Default number of cores is parallel::detectedCores() - 2.

laplacian

a logical scalar. Should the Laplacian Eigenmaps be used for dimensionality reduction? Default TRUE.

Value

a vector of the median persistence score across lags (minLag to maxLag) for each gene in data

References

See Also


nesscoder/TimeCycle documentation built on June 29, 2021, 5:16 a.m.