reCAT is easy to use. Now we use ola_mES_2i in /data as example
####preparatory work
when you use our tools, you should install some packages first, the package list is as follows:
ggplot2 doParallel mclust cluster TSP
####input data
in reCAT, there are some requirements for the input data.
- the genes must be in cyclebaseGeneList
ola_mES_2i.RData in /data is an example
####get order
when you preprocessing your test data, you can get its order(cell's time series) easily with get_ordIndex function. In this function, there are two parameters, one is the input data, the other is thread number, so maybe you can choose a large thread number like 20 to speed it up. for example:
source("get_ordIndex.R")
load("../data/ola_mES_2i.RData")
ordIndex <- get_ordIndex(test_exp, 10)
####get bayes-score and mean score in reCAT, there are two scores to for example:
source("get_score.R")
score_result <- getScore(t(test_exp))
you can use the following two orders to get scores
score_result$bayes_score
score_result$mean_score
####plot1 plot with order you get:
source("plot.R")
plot_bayes(score_result$bayes_score, ordIndex)
plot_mean(score_result$mean_score, ordIndex)
the result is like follows:
####HMM for example:
source("get_bw.R")
load("../data/ola_mES_2i_ordIndex.RData")
load("../data/ola_mES_2i_region.RData")
hmm_result <- get_bw_three(score_result$bayes_score, score_result$mean_score, ordIndex, cls_num = 3, fob = 0)
####plot2 plot with HMM result:
source("plot.R")
load("../data/ola_mES_2i_hmm.RData")
plot_bayes_bw(score_result$bayes_score, ordIndex, hmm_result, hmm_order, 1)
plot_mean_bw(score_result$mean_score, ordIndex, hmm_result, hmm_order, 1)