TimeCycle: Main TimeCycle Function

In nesscoder/TimeCycle: TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts

Description

Main function of the TimeCycle package used for detecting rhythmic signals in time-series gene expression sets. For additional help with parameter selection, see TimeCycle's vignette: vignette("TimeCycle").

Usage

TimeCycle(
  data,
  repLabel,
  resamplings = 10000,
  minLag = 2,
  maxLag = 5,
  period = 24,
  cores = parallel::detectCores() - 2,
  laplacian = T,
  linearTrend = F
)

Arguments

data	a data.frame of numeric gene expression over time (row = genes x col = ZT times).
repLabel	a vector defining the number of replicates at each time points.
resamplings	a numeric specifying the number of resamplings to use in the null-distribution. Default is 10000.
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.
period	a numeric specifying the period of interest in hours for rhythm detection. Default is 24.
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
linearTrend	a logical scalar. Should TimeCycle Prioritize detecting linear trending signals? Default FALSE. Not recommended to change from default FALSE - will increases false positives rate. See vignette("TimeCycle") for more details.

Value

a tidy data.frame of processed results for each gene:

sampleName	perScore	pVals	pVals.adj	Period.in.Hours	Amp	Phase.In.Hours
the gene name	the median persistence score across all lags (min to max)	raw empirical p-value	FDR adjusted p-value	period (h)	amplitude	phase (h)

References

• The TimeCycle Manuscript is available from Bioinformatics at https://doi.org/10.1093/bioinformatics/btab476

TDA Package References

• Wadhwa RR, Williamson DFK, Dhawan A, Scott JG. (2018). "TDAstats: R pipeline for computing persistent homology in topological data analysis." Journal of Open Source Software. 2018; 3(28): 860. doi:[10.21105/joss.00860]

• Bauer U. (2019). "Ripser: Efficient computation of Vietoris-Rips persistence barcodes." arXiv: 1908.02518.

Examples

# use built in zhang2014 data set sampled every
# 2 hours for 48 hours (i.e. 24 time points with 1 replicate each).
# Search for genes with period of 24 hours.

#set seed for reproducibility with random variables in example usage
#> set.seed(1234)

#> TimeCycleResults <- TimeCycle(data = zhang2014,
#>                               repLabel = rep(1,24),
#>                               period = 24)

# Check number of genes with FDR < 0.05 and period between 22 to 26 hours.
#> library(tidyverse)

#> TimeCycleResults %>%
#>    filter(22 < Period.in.Hours & Period.in.Hours < 26) %>%
#>    filter(pVals.adj < 0.05) %>%
#>    glimpse()

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