This function computes the weighted correlation with a penalty for lags. It should only be used after the fixed lags have already been applied to the dataset and timepoints using the functions prep.data() and best.lag().
comp.corr(data, time, C)
| data | a lagged matrix or data frame with rows representing genes and columns representing different timepoints (NAs added when lags are needed) |
|---|---|
| time | a lagged matrix with rows representing each gene's timepoint and columns representing the number of timepoints, NA is introduced when it is lagged |
| C | a numeric value of C used in computing weighted correlation |
a simmilarity matrix with values between -1 and 1 (1 highly correlated, 0 no correlation)
## This function computes the correlation after the lags (or shifts) have ## been computed. In this example, the lags argument is randomly sampled ## for the sake of illustrating how prep.data() applies the lags and ## prepares a transformed dataset for comp.corr(). lagged <- prep.data(array(rnorm(30), c(3, 10)), timepoints = seq(0, 45, 5), lags = sample(c(0, 1, -1, 2, -2), size = 3)) comp.corr(data = lagged$data, time = lagged$time, C = 10)#> 1 2 #> 2 0.020033639 #> 3 0.004612629 0.166915429## This example shows how comp.corr is used in practice with real data. ## The best.lag() function is called first to pre-compute the lags, which ## are passed to prep.data(). randdata <- array(rnorm(120), c(10, 12)) bl <- best.lag(data = randdata, timepoints = 1:12, C = 5) lag.data <- prep.data(randdata, timepoints = 1:12, lags = bl) comp.corr(lag.data$data, time = lag.data$time, C = 5)#> 1 2 3 4 5 6 #> 2 0.67861143 #> 3 0.74379849 0.41604571 #> 4 -0.10883989 -0.29628916 0.29364153 #> 5 0.12171748 0.05236690 0.19625444 -0.16047194 #> 6 0.16555807 -0.15543591 0.33084530 0.12042764 0.46081170 #> 7 0.13117544 -0.01423879 0.10853907 -0.03027215 0.21047377 0.36789065 #> 8 0.32047719 0.31276718 0.18546001 0.29896231 0.49362817 0.14101381 #> 9 -0.49302349 -0.53412251 -0.14270635 -0.34752417 0.47685720 0.39360609 #> 10 -0.11940481 -0.37677106 0.32322744 0.62266654 -0.02413093 0.07260506 #> 7 8 9 #> 2 #> 3 #> 4 #> 5 #> 6 #> 7 #> 8 0.07397236 #> 9 0.12321073 -0.46186376 #> 10 -0.13460288 -0.07879476 0.16638854