omicslonda {OmicsLonDA} | R Documentation |
Find significant time intervals of omic feature
omicslonda(se_object = NULL, n.perm = 500, fit.method = "ssgaussian", points = NULL, text = "FeatureName", parall = FALSE, pvalue.threshold = 0.05, adjust.method = "BH", time.unit = "days", ylabel = "Normalized Count", col = c("blue", "firebrick"), prefix = "Test")
se_object |
SummarizedExperiment object contains omics count/level matrix and metadata contains (subject, time, group, and any any other covariates) |
n.perm |
number of permutations. |
fit.method |
fitting method (ssguassian). |
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 |
a list of the significant time intervals for the tested feature, fitted model for each group, null distribution of the test statistic of the tested feature, and the original input data.
Ahmed Metwally (ametwall@stanford.edu)
library(SummarizedExperiment) data(omicslonda_data_example) omicslonda_se_object_adjusted = adjustBaseline( se_object = omicslonda_data_example$omicslonda_se_object) omicslonda_test_object = omicslonda_se_object_adjusted[1,] points = seq(1, 500, length.out = 500) res = omicslonda(se_object = omicslonda_test_object, n.perm = 10, fit.method = "ssgaussian", points = points, text = "Feature_1", parall = FALSE, pvalue.threshold = 0.05, adjust.method = "BH", time.unit = "days", ylabel = "Normalized Count", col = c("blue", "firebrick"), prefix = tempfile())