## ----setup, include = FALSE--------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----eval=FALSE, results='hide', message=FALSE, warning=FALSE----------------- # if (!requireNamespace("BiocManager", quietly = TRUE)) # install.packages("BiocManager", repos = "http://cran.us.r-project.org") # BiocManager::install("OmicsLonDA") # ## ---- results='hide',message=FALSE,warning=FALSE------------------------------ library(OmicsLonDA) library(SummarizedExperiment) ## Load 10 simulated features and metadata data("omicslonda_data_example") ## ----------------------------------------------------------------------------- omicslonda_data_example$ome_matrix[1:5, 1:5] ## ----------------------------------------------------------------------------- head(omicslonda_data_example$metadata) ## ----message=FALSE,warning=FALSE---------------------------------------------- se_ome_matrix = as.matrix(omicslonda_data_example$ome_matrix) se_metadata = DataFrame(omicslonda_data_example$metadata) omicslonda_se_object = SummarizedExperiment(assays=list(se_ome_matrix), colData = se_metadata) ## ----message=FALSE,warning=FALSE---------------------------------------------- omicslonda_se_object_adjusted = adjustBaseline(se_object = omicslonda_se_object) ## ----------------------------------------------------------------------------- assay(omicslonda_se_object_adjusted)[1:5, 1:5] ## ----message=FALSE,warning=FALSE---------------------------------------------- omicslonda_test_object = omicslonda_se_object_adjusted[1,] visualizeFeature(se_object = omicslonda_test_object, text = "Feature_1", unit = "days", ylabel = "Normalized Count", col = c("blue", "firebrick"), prefix = tempfile()) ## ----------------------------------------------------------------------------- points = seq(1, 500, length.out = 500) ## ----results='hide', message=FALSE,warning=FALSE------------------------------ 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()) ## ----message=FALSE,warning=FALSE---------------------------------------------- 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") ## ----results='hide', message=FALSE,warning=FALSE------------------------------ visualizeTestStatHistogram(omicslonda_object = res, text = "Feature_1", fit.method = "ssgaussian", prefix = tempfile()) ## ----message=FALSE,warning=FALSE---------------------------------------------- visualizeArea(omicslonda_object = res, fit.method = "ssgaussian", text = "Feature_1", unit = "days", ylabel = "Normalized Count", col = c("blue", "firebrick"), prefix = tempfile()) ## ----message=FALSE,warning=FALSE---------------------------------------------- prefix = tempfile() if (!dir.exists(prefix)){ dir.create(file.path(prefix)) } ## Save OmicsLonDA results in RData file save(res, file = sprintf("%s/Feature_%s_results_%s.RData", prefix = prefix, text = "Feature_1", fit.method = "ssgaussian")) ## Save a summary of time intervals statistics in csv file feature.summary = as.data.frame(do.call(cbind, res$details), stringsAsFactors = FALSE) write.csv(feature.summary, file = sprintf("%s/Feature_%s_Summary_%s.csv", prefix = prefix, text = "Feature_1", fit.method = "ssgaussian"), row.names = FALSE)