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Merge pull request #13 from tanaylab/feat@interactions
Feat@interactions
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| ^man/figures/pipeline\.png$ | ||
| ^example_data$ | ||
| ^data-raw$ | ||
| ^vignettes/articles/iceqream\.R$ | ||
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| vignettes/articles/iceqream_cache/ | ||
| data-raw/*.ipynb | ||
| data-raw/*.png | ||
| vignettes/articles/iceqream.R | ||
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| has_interactions <- function(traj_model) { | ||
| n_interactions(traj_model) > 0 | ||
| } | ||
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| n_interactions <- function(traj_model) { | ||
| ncol(traj_model@interactions) | ||
| } | ||
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| create_specifc_terms <- function(energies, terms) { | ||
| term1_matrix <- energies[, terms$term1] | ||
| term2_matrix <- energies[, terms$term2] | ||
| inter <- term1_matrix * term2_matrix | ||
| inter <- t(t(inter) / apply(inter, 2, max, na.rm = TRUE)) | ||
| inter <- apply(inter, 2, norm01) * 1 | ||
| colnames(inter) <- terms$variable | ||
| return(inter) | ||
| } | ||
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| create_interaction_terms <- function(energies, motif_feats = NULL, add_feats = NULL, additional_features = NULL, max_motif_n = NULL, max_add_n = NULL) { | ||
| create_interactions <- function(features, data, max_n) { | ||
| if (is.null(features) || is.null(data)) { | ||
| return(NULL) | ||
| } | ||
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| features <- head(features, n = max_n %||% length(features)) | ||
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| interactions <- purrr::map_dfc(features, ~ { | ||
| inter <- energies[, setdiff(colnames(energies), .x)] * data[, .x] | ||
| inter <- t(t(inter) / apply(inter, 2, max, na.rm = TRUE)) | ||
| colnames(inter) <- paste0(.x, ":", colnames(inter)) | ||
| inter | ||
| }) | ||
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| interactions <- apply(interactions, 2, norm01) * 1 | ||
| interactions | ||
| } | ||
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| add_inter <- create_interactions(add_feats, additional_features, max_add_n) | ||
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| if (!is.null(add_inter)) { | ||
| cli::cli_alert_info("Created {.val {ncol(add_inter)}} interactions between additional features and motif features.") | ||
| } | ||
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| motif_inter <- create_interactions(motif_feats, energies, max_motif_n) | ||
| if (!is.null(motif_inter)) { | ||
| cli::cli_alert_info("Created {.val {ncol(motif_inter)}} interactions between motif features.") | ||
| } | ||
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| interactions <- cbind(motif_inter, add_inter) | ||
| if (!is.null(interactions)) { | ||
| if (!is.null(rownames(energies))) { | ||
| rownames(interactions) <- rownames(energies) | ||
| } | ||
| cli::cli_alert_info("Created {.val {ncol(interactions)}} interactions in total.") | ||
| } | ||
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| return(interactions) | ||
| } | ||
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| get_significant_interactions <- function( | ||
| energies, y, interaction_threshold, max_motif_n = NULL, max_add_n = NULL, | ||
| additional_features = NULL, lambda = 1e-5, alpha = 1, seed = 60427, | ||
| ignore_feats = c("TT", "CT", "GT", "AT", "TC", "CC", "GC", "AC", "TG", "CG", "GG", "AG", "TA", "CA", "GA", "AA")) { | ||
| glm_model_lin <- glmnet::glmnet(as.matrix(energies), y, binomial(link = "logit"), alpha = alpha, lambda = lambda, seed = seed) | ||
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| feats_all <- abs(stats::coef(glm_model_lin)[-1]) | ||
| names(feats_all) <- rownames(stats::coef(glm_model_lin))[-1] | ||
| sig_feats <- names(feats_all)[feats_all > interaction_threshold] | ||
| sig_feats <- setdiff(sig_feats, ignore_feats) | ||
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| if (length(sig_feats) == 0) { | ||
| cli::cli_alert_warning("No significant features to consider for interactions.") | ||
| return(NULL) | ||
| } | ||
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| add_feats <- intersect(sig_feats, colnames(additional_features)) | ||
| motif_feats <- setdiff(sig_feats, add_feats) | ||
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| cli::cli_alert_info("# of significant features to consider for interactions: {.val {length(sig_feats)}} (out of {.val {ncol(energies)}}) above the threshold of {.val {interaction_threshold}}. Of these, {.val {length(motif_feats)}} are motif features and {.val {length(add_feats)}} are additional features.") | ||
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| if (!is.null(additional_features)) { | ||
| # remove the features from energies | ||
| energies <- energies[, setdiff(colnames(energies), colnames(additional_features))] | ||
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| # remove the ignored features | ||
| energies <- energies[, setdiff(colnames(energies), ignore_feats)] | ||
| } | ||
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| create_interaction_terms(energies, | ||
| motif_feats = motif_feats, add_feats = add_feats, | ||
| additional_features = additional_features, max_motif_n = max_motif_n, max_add_n = max_add_n | ||
| ) | ||
| } | ||
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| #' Add interactions to a trajectory model | ||
| #' | ||
| #' This function adds significant interactions to a given trajectory model if they do not already exist. | ||
| #' It identifies significant interactions based on the provided threshold and updates the model features | ||
| #' with logistic features derived from these interactions. The trajectory model is then re-learned with | ||
| #' the new features. | ||
| #' | ||
| #' @inheritParams regress_trajectory_motifs | ||
| #' | ||
| #' @return The updated trajectory model with added interactions. | ||
| #' @export | ||
| add_interactions <- function(traj_model, interaction_threshold = 0.001, max_motif_n = NULL, max_add_n = NULL, lambda = 1e-5, alpha = 1, seed = 60427) { | ||
| if (!has_interactions(traj_model)) { | ||
| cli::cli_alert("Adding interactions") | ||
| interactions <- get_significant_interactions( | ||
| cbind(traj_model@normalized_energies, traj_model@additional_features), norm01(traj_model@diff_score), interaction_threshold, | ||
| max_motif_n = max_motif_n, max_add_n = max_add_n, | ||
| additional_features = traj_model@additional_features, lambda = lambda, alpha = alpha, seed = seed | ||
| ) | ||
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| if (!is.null(interactions)) { | ||
| traj_model@interactions <- interactions | ||
| } | ||
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| logist_inter <- create_logist_features(interactions) | ||
| traj_model@model_features <- cbind(traj_model@model_features, logist_inter) | ||
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| cli::cli_alert_info("Re-learning the model with the new interactions. Number of features: {.val {ncol(traj_model@model_features)}}") | ||
| cli::cli_alert_info("R^2 all before learning: {.val {cor(traj_model@diff_score, traj_model@predicted_diff_score)^2}}") | ||
| if (traj_model_has_test(traj_model)) { | ||
| cli::cli_alert_info("R^2 train before learning: {.val {cor(traj_model@diff_score[traj_model@type == 'train'], traj_model@predicted_diff_score[traj_model@type == 'train'])^2}}") | ||
| cli::cli_alert_info("R^2 test before learning: {.val {cor(traj_model@diff_score[traj_model@type == 'test'], traj_model@predicted_diff_score[traj_model@type == 'test'])^2}}") | ||
| } | ||
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| traj_model <- relearn_traj_model(traj_model, new_energies = FALSE, new_logist = FALSE, use_additional_features = TRUE, use_motifs = TRUE, verbose = FALSE) | ||
| cli::cli_alert_info("R^2 all after learning: {.val {cor(traj_model@diff_score, traj_model@predicted_diff_score)^2}}") | ||
| if (traj_model_has_test(traj_model)) { | ||
| cli::cli_alert_info("R^2 train after learning: {.val {cor(traj_model@diff_score[traj_model@type == 'train'], traj_model@predicted_diff_score[traj_model@type == 'train'])^2}}") | ||
| cli::cli_alert_info("R^2 test after learning: {.val {cor(traj_model@diff_score[traj_model@type == 'test'], traj_model@predicted_diff_score[traj_model@type == 'test'])^2}}") | ||
| } | ||
| } else { | ||
| cli::cli_alert_warning("Interactions already exist.") | ||
| } | ||
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| return(traj_model) | ||
| } |
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