Main TimeCycle Function — TimeCycle • TimeCycle

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").

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()