OmicsLonDA (Omics Longitudinal Differential Analysis) is a statistical framework that provides robust identification of time intervals where omics features are significantly different between groups. OmicsLonDA is based on 5 main steps:
Install the latest version of OmicsLonDA from Bioconductor:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager", repos = "http://cran.us.r-project.org")
::install("OmicsLonDA") BiocManager
library(OmicsLonDA)
library(SummarizedExperiment)
## Load 10 simulated features and metadata
data("omicslonda_data_example")
The measurment matrix represents count/intensity of features from an omic experiment. Columns represent various samples from different subjects longitudinally. Rows represent various features. Here is an example:
$ome_matrix[1:5, 1:5]
omicslonda_data_example#> Sample_2 Sample_3 Sample_4 Sample_5 Sample_6
#> Feature_1 13.84393 7.976260 8.392602 7.511995 25.05878
#> Feature_2 17.35090 12.151211 12.758727 11.825749 28.55361
#> Feature_3 17.61570 13.617460 11.505888 13.906188 29.41523
#> Feature_4 17.63444 11.557786 11.081131 12.852518 28.61033
#> Feature_5 18.49954 9.642724 12.765664 11.103249 29.09817
The metadata dataframe contains annotations for each sample. Most impotantly it should have at least: (a) “Subject”: which denote from which subject this sample is coming from, (b) “Group”: which represents which group this sample is from (eg., healthy, disease, etc), (c) “Time”: which represents the collection time of the corresponding sample. Here is an example:
head(omicslonda_data_example$metadata)
#> Subject Group Time
#> Sample_2 SA_1 Disease 11
#> Sample_3 SA_1 Disease 22
#> Sample_4 SA_1 Disease 40
#> Sample_5 SA_1 Disease 49
#> Sample_6 SA_1 Disease 69
#> Sample_7 SA_1 Disease 75
as.matrix(omicslonda_data_example$ome_matrix)
se_ome_matrix = DataFrame(omicslonda_data_example$metadata)
se_metadata = SummarizedExperiment(assays=list(se_ome_matrix),
omicslonda_se_object =colData = se_metadata)
adjustBaseline(se_object = omicslonda_se_object) omicslonda_se_object_adjusted =
assay(omicslonda_se_object_adjusted)[1:5, 1:5]
#> Sample_2 Sample_3 Sample_4 Sample_5 Sample_6
#> Feature_1 0 -0.5513775 -0.5004965 -0.6113461 0.5933772
#> Feature_2 0 -0.3562154 -0.3074287 -0.3833650 0.4981392
#> Feature_3 0 -0.2574379 -0.4259317 -0.2364567 0.5127220
#> Feature_4 0 -0.4224943 -0.4646099 -0.3163138 0.4839143
#> Feature_5 0 -0.6515421 -0.3709867 -0.5105080 0.4529296
omicslonda_se_object_adjusted[1,]
omicslonda_test_object =visualizeFeature(se_object = omicslonda_test_object, text = "Feature_1",
unit = "days", ylabel = "Normalized Count",
col = c("blue", "firebrick"), prefix = tempfile())
seq(1, 500, length.out = 500) points =
omicslonda(se_object = omicslonda_test_object, n.perm = 10,
res =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())
visualizeFeatureSpline(se_object = omicslonda_test_object, omicslonda_object = res, fit.method = "ssgaussian",
text = "Feature_1", unit = "days",
ylabel = "Normalized Count",
col = c("blue", "firebrick"),
prefix = "OmicsLonDA_example")