add the general test script

tests/test.R

@@ -0,0 +1,125 @@
+# test.R
+# this is a test script updated based on the README.md to test all the functions in the package
+
+library(iSubGen);
+library(tensorflow);
+library(reticulate);
+library(keras3);
+
+## testing load.molecular.aberration.data() ########################################
+molecular.data <- list();
+for(i in c('cna','methy','snv')) {
+    molecular.data[[i]] <- load.molecular.aberration.data(
+        system.file('exdata',paste0(i,'_profiles.txt'), package='iSubGen'),
+        patients = c(paste0('EP00',1:9), paste0('EP0',10:30))
+        );
+}
+
+## testing calculate.scaling() #####################################################
+# scale the mrna and mirna data
+scaling.factors <- calculate.scaling(
+	list(
+		mirna = molecular.data$mirna,
+		mrna_tc_together = molecular.data$mrna_tc_together,
+		mrna_tac_together = molecular.data$mrna_tac_together
+		)
+	);
+
+## testing write.scaling.factors() #################################################
+# write the scaling factors to a file
+write.scaling.factors(
+	scaling.factors,
+	paste0(pipe.vars$pipeline.dir,'input_data/scaling_factors/')
+	);
+
+## testing apply.scaling() #########################################################
+scaled.matrices <- apply.scaling(
+	list(
+		mirna = molecular.data$mirna,
+		mrna_tc_together = molecular.data$mrna_tc_together,
+		mrna_tac_together = molecular.data$mrna_tac_together
+		),
+	scaling.factors
+	);
+molecular.data$mrna_tc_together <- scaled.matrices$mrna_tc_together;
+molecular.data$mrna_tac_together <- scaled.matrices$mrna_tac_together;
+molecular.data$mirna <- scaled.matrices$mirna;
+
+## testing create.autoencoder() ####################################################
+# Create a list to store the autoencoders
+autoencoders <- list();
+
+# Create and train an autoencoder using CNA data
+autoencoders[['cna']] <- create.autoencoder(
+    data.type = 'cna',
+    data.matrix = molecular.data$cna,
+    encoder.layers.node.nums = c(20,2)
+    )$autoencoder;
+# take a look at the layers/number of nodes in the autoencoder
+str(autoencoders$cna);
+
+# Create and train an autoencoder using methylation data
+autoencoders[['methy']] <- create.autoencoder(
+    data.type = 'methy',
+    data.matrix = molecular.data$methy,
+    encoder.layers.node.nums = c(15,1)
+    )$autoencoder;
+# take a look at the layers/number of nodes in the autoencoder
+str(autoencoders$methy);
+
+# Create and train an autoencoder using coding SNV data
+autoencoders[['snv']] <- create.autoencoder(
+    data.type = 'snv',
+    data.matrix = molecular.data$snv,
+    encoder.layers.node.nums = c(15,1)
+    )$autoencoder;
+
+## test create.autoencoder.irf.matrix() ############################################
+# Get the independent reduced features from the autoencoders
+irf.matrix <- create.autoencoder.irf.matrix(
+    data.types = names(molecular.data),
+    data.matrices = molecular.data,
+    autoencoders = autoencoders
+    );
+
+## test calculate.integrative.similarity.matrix() ##################################
+# Calculate a similarity matrix using correlations
+similarity.matrix <- calculate.integrative.similarity.matrix(
+    data.types = names(molecular.data),
+    data.matrices = molecular.data,
+    dist.metrics = list(
+        cna = 'euclidean',
+        snv = 'euclidean',
+        methy = 'euclidean'
+        )
+    );
+
+## test calculate.cis.matrix() #####################################################
+cis.matrix <- calculate.cis.matrix(
+    data.types = names(molecular.data),
+    data.matrices = molecular.data,
+    dist.metrics = list(
+        cna = 'euclidean',
+        snv = 'euclidean',
+        methy = 'euclidean'
+        )
+    );
+
+## test combine.integrative.features() ##############################################
+# Combine IRFs and CISs into one matrix
+integrative.features.matrix <- combine.integrative.features(
+    irf.matrix,
+    cis.matrix
+    )$integrative.feature.matrix;
+
+## test cluster.patients() #########################################################
+# Perform consensus clustering to get integrative subtypes
+subtyping.results <- cluster.patients(
+    data.matrix = integrative.features.matrix,
+    distance.metric = 'euclidean',
+    parent.output.dir = './',
+    new.result.dir = 'vignette_subtypes',
+    max.num.subtypes = 5,
+    clustering.reps = 50,
+    consensus.cluster.write.table = FALSE
+    );