Calculate Entropy and mututal information for each treatment level
Given an MEA recording, this function computes entropy and mutual information measures for each treatment level.
calculate_entropy_and_mi(mea, treatments, mult_factor = 1.5, bin_size = 0.1)
mea |
The input mea spikelist object |
treatments |
The treatment levels that MI and entropy will be computed. |
mult_factor |
The multiplication factor relating to the inter quartile range used in the algorithm. It serves as a tuning parameter with a default value of 1.5. |
bin_size |
The bin size(in second) used to compute mutual information. |
A list object holding MI and Entropy for each treatment level.
library(meaRtools)
data(S)
S <- filter_nonactive_spikes(S,spikes_per_minute_min=1)
treatments <- c("treatX", "treatY")
## compute entropies and MI's
ENT.MI <- calculate_entropy_and_mi(S, treatments, mult_factor=1.5, bin_size=0.1)
data_dists <- ENT.MI[["data_dists"]]
norm_mis_per_well <- ENT.MI[["norm_mis_per_well"]]
# test for difference in mean entropy between treatmentA, treatmentB
ent <- data_dists[["ENT"]]
ent.WT <- mean(ent[[treatments[1]]])
ent.MUT <- mean(ent[[treatments[2]]])
ent.res <- wilcox.test(ent[[treatments[1]]], ent[[treatments[2]]])
cat("entropy means (WT / MUT) :", ent.WT, "/", ent.MUT, "\n")
print(ent.res)
# test for diff in mutual info btwn treatmentA, treatmentB
mi <- data_dists[["MI"]]
mi.WT <- mean(mi[[treatments[1]]])
mi.MUT <- mean(mi[[treatments[2]]])
mi.res <- wilcox.test(mi[[treatments[1]]], mi[[treatments[2]]])
cat("mutual info means (WT / MUT) :", mi.WT, "/", mi.MUT, "\n")
print(mi.res)
plot(density(mi[[treatments[1]]]))
lines(density(mi[[treatments[2]]]), col="red")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.