## ----load_files, message=FALSE------------------------------------------------ library("SIAMCAT") data("feat_crc_zeller", package="SIAMCAT") data("meta_crc_zeller", package="SIAMCAT") ## ----show_features------------------------------------------------------------ feat.crc.zeller[1:3, 1:3] dim(feat.crc.zeller) ## ----show_meta---------------------------------------------------------------- head(meta.crc.zeller) ## ----create_label------------------------------------------------------------- label.crc.zeller <- create.label(meta=meta.crc.zeller, label='Group', case='CRC') ## ----start-------------------------------------------------------------------- sc.obj <- siamcat(feat=feat.crc.zeller, label=label.crc.zeller, meta=meta.crc.zeller) ## ----show_siamcat------------------------------------------------------------- show(sc.obj) ## ----filter_feat-------------------------------------------------------------- sc.obj <- filter.features(sc.obj, filter.method = 'abundance', cutoff = 0.001) ## ----check_associations, eval=FALSE------------------------------------------- # sc.obj <- check.associations(sc.obj, log.n0 = 1e-06, alpha = 0.05) # association.plot(sc.obj, sort.by = 'fc', # panels = c('fc', 'prevalence', 'auroc')) ## ----check_confounders, eval=FALSE-------------------------------------------- # check.confounders(sc.obj, fn.plot = 'confounder_plots.pdf', # meta.in = NULL, feature.type = 'filtered') ## ----normalize_feat----------------------------------------------------------- sc.obj <- normalize.features(sc.obj, norm.method = "log.unit", norm.param = list(log.n0 = 1e-06, n.p = 2,norm.margin = 1)) ## ----data_split--------------------------------------------------------------- sc.obj <- create.data.split(sc.obj, num.folds = 5, num.resample = 2) ## ----train_model, message=FALSE, results='hide'------------------------------- sc.obj <- train.model(sc.obj, method = "lasso") ## ----show_models-------------------------------------------------------------- # get information about the model type model_type(sc.obj) # access the models models <- models(sc.obj) models[[1]]$model ## ----make_predictions, message=FALSE, results='hide'-------------------------- sc.obj <- make.predictions(sc.obj) pred_matrix <- pred_matrix(sc.obj) ## ----pred_matrix_head--------------------------------------------------------- head(pred_matrix) ## ----eval_predictions--------------------------------------------------------- sc.obj <- evaluate.predictions(sc.obj) ## ----eval_plot, eval=FALSE---------------------------------------------------- # model.evaluation.plot(sc.obj) ## ----eval=FALSE--------------------------------------------------------------- # model.interpretation.plot(sc.obj, fn.plot = 'interpretation.pdf', # consens.thres = 0.5, limits = c(-3, 3), heatmap.type = 'zscore') ## ----------------------------------------------------------------------------- sessionInfo()