Description Usage Arguments Value References Examples
Find significant time intervals of the one feature
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | 
| Count | matrix has the number of reads that mapped to each feature in each sample. | 
| Time | vector of the time label of each sample. | 
| Group | vector of the group label of each sample. | 
| ID | vector of the subject ID label of each sample. | 
| n.perm | number of permutations. | 
| fit.method | fitting method (nbinomial, lowess). | 
| points | points at which the prediction should happen. | 
| text | Feature's name. | 
| parall | boolean to indicate whether to use multicore. | 
| pvalue.threshold | p-value threshold cutoff for identifing significant time intervals. | 
| adjust.method | multiple testing correction method. | 
| time.unit | time unit used in the Time vector (hours, days, weeks, months, etc.) | 
| ylabel | text to be shown on the y-axis of all generated figures (default: "Normalized Count") | 
| col | two color to be used for the two groups (eg., c("red", "blue")). | 
| prefix | prefix to be used to create directory for the analysis results | 
returns a list of the significant time intervals for the tested feature.
Ahmed Metwally (ametwall@stanford.edu)
| 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | data(metalonda_test_data)
n.sample = 5
n.timepoints = 10
n.group = 2
Group = factor(c(rep(0, n.sample*n.timepoints), rep(1,n.sample*n.timepoints)))
Time = rep(rep(1:n.timepoints, times = n.sample), 2)
ID = factor(rep(1:(2*n.sample), each = n.timepoints))
points = seq(1, 10, length.out = 10)
## Not run: 
output.nbinomial = metalonda(Count = metalonda_test_data[1,], Time = Time, Group = Group,
ID = ID, fit.method =  "nbinomial", n.perm = 10, points = points,
text = rownames(metalonda_test_data)[1], parall = FALSE, pvalue.threshold = 0.05, 
adjust.method = "BH", time.unit = "hours", ylabel = "Normalized Count", col = c("black", "green"))
## End(Not run)
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