build_logit_vert() defines keras model
make_features() generate features for predicting vertex by expression profile of several genes with distance from that vertex
predict_vert() predict vertices using a model and attribute data (distance to vertex + feature of cells)
split_train_val() split data into training and validation. Shuffle examples to ensure that model sees all vertices in training and validation
plot_confusion_vert() plot vertex probabilities as confusion matrix
get_feature_weights() get gene weights and ranked features from keras model
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | build_logit_vert(features_list, penalty = 0.7,
regularizer = keras::regularizer_l2, initializer = "random_uniform",
activation = "softmax", loss = "categorical_crossentropy",
metrics = list("accuracy"))
make_features(data_attr, features_list, n_bins = 10, n_samples = 10,
select_bins = seq_len(n_bins), combine_bins = seq_len(n_bins))
predict_vert(vert_model, data_attr, features_data)
split_train_val(features_data, val_prop = 0.3, per_class = TRUE)
plot_confusion_vert(predicted)
get_feature_weights(vert_model, features_data)
|
val_prop |
proportion of samples used for validation. |
per_class |
Use proportion of samples within each class for validation? By default the function takes class into account when splitting data (TRUE) |
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