predict_model {ttgsea} | R Documentation |
From the result of the function "ttgsea", we can predict enrichment scores. For each new term, lemmatized text, predicted enrichment score, Monte Carlo p-value and adjusted p-value are provided. The function "token_vector" is used for encoding as we did for training. Of course, mapping from tokens to integers should be the same.
predict_model(object, new_text, num_simulations = 1000, adj_p_method = "fdr")
object |
result of "ttgsea" |
new_text |
new text data |
num_simulations |
number of simulations for Monte Carlo p-value (default: 1000) |
adj_p_method |
correction method (default: "fdr") |
table for lemmatized text, predicted enrichment score, MC p-value and adjusted p-value
Dongmin Jung
stats::p.adjust
library(reticulate) if (keras::is_keras_available() & reticulate::py_available()) { library(fgsea) data(examplePathways) data(exampleRanks) names(examplePathways) <- gsub("_", " ", substr(names(examplePathways), 9, 1000)) set.seed(1) fgseaRes <- fgsea(examplePathways, exampleRanks) num_tokens <- 1000 length_seq <- 30 batch_size <- 32 embedding_dims <- 50 num_units <- 32 epochs <- 1 ttgseaRes <- fit_model(fgseaRes, "pathway", "NES", model = bi_gru(num_tokens, embedding_dims, length_seq, num_units), num_tokens = num_tokens, length_seq = length_seq, epochs = epochs, batch_size = batch_size, use_generator = FALSE) set.seed(1) predict_model(ttgseaRes, "Cell Cycle") }