View source: R/DCP_Rhythmicity.R
toTOJR | R Documentation |
Categorize genes to four types of joint rhythmicity (TOJR).
toTOJR( x, method = "Sidak_FS", amp.cutoff = 0, alpha = 0.05, adjustP = TRUE, p.adjust.method = "BH", parallel.ncores = 1 )
x |
one of the following two:
|
method |
character string specifying the algorithm used for joint rhythmicity categorization. Should be one of "Sidak_FS", "Sidak_BS", "VDA", "AWFisher". |
amp.cutoff |
Only genes with amplitude greater than amp.cutoff are consirdered rhythmic |
alpha |
integer. Threshold for rhythmicity p-value in joint rhythmicity categorization. |
adjustP |
logic. Should joint rhythmicity categorization be based on adjusted p-value? |
p.adjust.method |
input for p.adjust() in R package |
parallel.ncores |
integer. Number of cores used if using parallel computing with |
Joint rhythmicity categories of genes
#Re-calculate TOJR for DCP_rhythmicity(x1, x2) output with q-value cutoff 0.1 x = DCP_sim_data(ngene=1000, nsample=30, A1=c(1, 3), A2=c(1, 3), phase1=c(0, pi/4), phase2=c(pi/4, pi/2), M1=c(4, 6), M2=c(4, 6), sigma1=1, sigma2=1) rhythm.res = DCP_Rhythmicity(x1 = x[[1]], x2 = x[[2]]) TOJR.new = toTOJR(rhythm.res, alpha = 0.1, adjustP = TRUE) #Calculate TOJR for two DCP_rhythmicity(x1, x2 = NULL) outputs with p-value cutoff 0.05 x = DCP_sim_data(ngene=1000, nsample=30, A1=c(1, 3), A2=c(1, 3), phase1=c(0, pi/4), phase2=c(pi/4, pi/2), M1=c(4, 6), M2=c(4, 6), sigma1=1, sigma2=1) rhythm.res1 = DCP_Rhythmicity(x1 = x[[1]]) rhythm.res2 = DCP_Rhythmicity(x1 = x[[2]]) TOJR = toTOJR(x = list(x1 = rhythm.res1, x2 = rhythm.res2), alpha = 0.05, adjustP = FALSE)
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