## ----消息= false，警告= false -------------------------------------------------------------------------------------库（MOFA2）库（Tidyverse）库（pheatmap）## ------------------------------------------------------------------------------ - - - - - - - - - - - - - - - - - - - - - - - 放。种子（2020）＃设置样本数和时间点n <-200 time <-seq（0,1，length.out = n）＃生成示例数据dd <-make_example_data（sample_cov = time，n_samples = n，n_factors = n n_factors = n_factors = n_factors = n_factors =4，n_features = 200，n_views = 4，lscales = c（0.5，0.2，0，0，0））＃输入数据数据<-dd $数据＃列中带有样本的列中的样本<-dd $ sample_cov rownames（time）<- “时间” ＃＃ - - - - - - - - - - - - - - - - - - - - - ---------------------------------------------- df <- data.frame(dd$Z, t(time)) df <- gather(df, key = "factor", value = "value", starts_with("simulated_factor")) ggplot(df, aes(x = time, y = value)) + geom_point() + facet_grid(~factor) ## ----------------------------------------------------------------------------- sm <- create_mofa(data = dd$data) ## ---- message=FALSE, warning=FALSE-------------------------------------------- sm <- set_covariates(sm, covariates = time) sm ## ---- message=FALSE, warning=FALSE-------------------------------------------- data_opts <- get_default_data_options(sm) model_opts <- get_default_model_options(sm) model_opts$num_factors <- 4 train_opts <- get_default_training_options(sm) train_opts$maxiter <- 100 mefisto_opts <- get_default_mefisto_options(sm) sm <- prepare_mofa(sm, model_options = model_opts, mefisto_options = mefisto_opts, training_options = train_opts, data_options = data_opts) ## ---- warning=FALSE, message=FALSE-------------------------------------------- outfile = file.path(tempdir(),"model.hdf5") sm <- run_mofa(sm, outfile) ## ---- fig.width=5, fig.height=4----------------------------------------------- plot_variance_explained(sm) r <- plot_factor_cor(sm) ## ----------------------------------------------------------------------------- get_scales(sm) ## ----------------------------------------------------------------------------- plot_factors_vs_cov(sm, color_by = "time") ## ----------------------------------------------------------------------------- df <- plot_factors_vs_cov(sm, color_by = "time", legend = FALSE, return_data = TRUE) head(df) ## ---- fig.width=5, fig.height=4----------------------------------------------- plot_weights(sm, factors = 4, view = 1) plot_top_weights(sm, factors = 3, view = 2) ## ----------------------------------------------------------------------------- plot_data_vs_cov(sm, factor=3, features = 2, color_by = "time", dot_size = 1) ## ----------------------------------------------------------------------------- sm <- interpolate_factors(sm, new_values = seq(0,1.1,0.01)) plot_interpolation_vs_covariate(sm, covariate = "time", factors = "Factor3") ## ----------------------------------------------------------------------------- sessionInfo()