vignettes/DeepLearningTutorial.R
In kstawiski/OmicSelector: OmicSelector - a package for biomarker selection based on high-throughput experiments.
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE, message=FALSE, warning=FALSE,
comment = "#>"
)
knitr::opts_chunk$set(fig.width=12, fig.height=8)
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=150),tidy=TRUE)
options(rgl.useNULL = TRUE)
options(warn=-1)
suppressMessages(library(dplyr))
set.seed(1)
options(knitr.table.format = "html")
library(OmicSelector)
## -----------------------------------------------------------------------------
OmicSelector::OmicSelector_load_extension("deeplearning")
## ----eval=F-------------------------------------------------------------------
# OmicSelector_deep_learning = function(selected_miRNAs = ".",
# wd = getwd(),
# SMOTE = F,
# keras_batch_size = 64,
# clean_temp_files = T,
# save_threshold_trainacc = 0.85,
# save_threshold_testacc = 0.8,
# keras_epochae = 5000,
# keras_epoch = 2000,
# keras_patience = 50,
# hyperparameters = expand.grid(
# layer1 = seq(3, 11, by = 2),
# layer2 = c(0, seq(3, 11, by = 2)),
# layer3 c(0, seq(3, 11, by = 2)),
# activation_function_layer1 = c("relu", "sigmoid"),
# activation_function_layer2 = c("relu", "sigmoid"),
# activation_function_layer3 = c("relu", "sigmoid"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0, 0.1),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(T, F),
# layer3_regularizer = c(T, F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0, 7, -7),
# balanced = SMOTE,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# ),
# add_features_to_predictions = F,
# keras_threads = ceiling(parallel::detectCores() /
# 2),
# start = 1,
# end = nrow(hyperparameters),
# output_file = "deeplearning_results.csv",
# save_all_vars = F)
# OmicSelector_load_datamix()
## ----eval=T-------------------------------------------------------------------
balanced = F # not balanced
selected_miRNAs = c("hsa.a","hsa.b","hsa.c") # selected features
hyperparameters = expand.grid(
layer1 = seq(2, 10, by = 1),
layer2 = c(0),
layer3 = c(0),
activation_function_layer1 = c("relu", "sigmoid", "selu"),
activation_function_layer2 = c("relu"),
activation_function_layer3 = c("relu"),
dropout_layer1 = c(0, 0.1),
dropout_layer2 = c(0),
dropout_layer3 = c(0),
layer1_regularizer = c(T, F),
layer2_regularizer = c(F),
layer3_regularizer = c(F),
optimizer = c("adam", "rmsprop", "sgd"),
autoencoder = c(0, -7, 7),
balanced = balanced,
formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
scaled = c(T, F),
stringsAsFactors = F
)
DT::datatable(hyperparameters,
extensions = c('FixedColumns',"FixedHeader"),
options = list(scrollX = TRUE,
paging=FALSE,
fixedHeader=TRUE))
## ----eval=F-------------------------------------------------------------------
# hyperparameters_part1 = expand.grid(
# layer1 = seq(2, 10, by = 1),
# layer2 = c(0),
# layer3 = c(0),
# activation_function_layer1 = c("relu", "sigmoid", "selu"),
# activation_function_layer2 = c("relu"),
# activation_function_layer3 = c("relu"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(F),
# layer3_regularizer = c(F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0),
# balanced = balanced,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# )
# hyperparameters_part2 = expand.grid(
# layer1 = seq(3, 11, by = 2),
# layer2 = c(seq(3, 11, by = 2)),
# layer3 = c(seq(0, 11, by = 2)),
# activation_function_layer1 = c("relu", "sigmoid", "selu"),
# activation_function_layer2 = c("relu", "sigmoid", "selu"),
# activation_function_layer3 = c("relu", "sigmoid", "selu"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(F),
# layer3_regularizer = c(F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0),
# balanced = balanced,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# )
# hyperparameters = rbind(hyperparameters_part1, hyperparameters_part2)
## ----eval=F-------------------------------------------------------------------
# hyperparameters_part1 = expand.grid(
# layer1 = seq(2, 10, by = 1),
# layer2 = c(0),
# layer3 = c(0),
# activation_function_layer1 = c("relu", "sigmoid", "selu"),
# activation_function_layer2 = c("relu"),
# activation_function_layer3 = c("relu"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(F),
# layer3_regularizer = c(F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0, -7, 7),
# balanced = balanced,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# )
# hyperparameters_part2 = expand.grid(
# layer1 = seq(3, 11, by = 2),
# layer2 = c(seq(3, 11, by = 2)),
# layer3 = c(seq(0, 11, by = 2)),
# activation_function_layer1 = c("relu", "sigmoid", "selu"),
# activation_function_layer2 = c("relu", "sigmoid", "selu"),
# activation_function_layer3 = c("relu", "sigmoid", "selu"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(F),
# layer3_regularizer = c(F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0, -7, 7),
# balanced = balanced,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# )
# hyperparameters = rbind(hyperparameters_part1, hyperparameters_part2)
## -----------------------------------------------------------------------------
data("orginal_TCGA_data")
suppressWarnings(suppressMessages(library(dplyr)))
cancer_cases = filter(orginal_TCGA_data, primary_site == "Pancreas" & sample_type == "PrimaryTumor")
control_cases = filter(orginal_TCGA_data, sample_type == "SolidTissueNormal")
cancer_cases$Class = "Case"
control_cases$Class = "Control"
dataset = rbind(cancer_cases, control_cases)
ttpm = OmicSelector_counts_to_log10tpm(danex = dplyr::select(dataset, starts_with("hsa")),
metadane = dplyr::select(dataset, -starts_with("hsa")),
ids = dataset$sample, filtr = F,
filtr_minimalcounts = 1,
filtr_howmany = 0.01)
ttpm = as.data.frame(ttpm)
zero_var = which(apply(ttpm, 2, var) == 0) #which have no variance
ttpm = ttpm[,-zero_var]
# Not run:
# DE = OmicSelector_differential_expression_ttest(ttpm_features = dplyr::select(dataset, starts_with("hsa")), classes = dataset$Class, mode = "logtpm")
# significant = DE$miR[DE$`p-value Bonferroni`<0.05]
selected_miRNAs = make.names(c('hsa-miR-192-5p', 'hsa-let-7g-5p', 'hsa-let-7a-5p', 'hsa-miR-194-5p', 'hsa-miR-122-5p', 'hsa-miR-340-5p', 'hsa-miR-26b-5p')) # some selected miRNAs
match(selected_miRNAs, colnames(ttpm))
#dataset = dataset[sample(1:nrow(dataset),200),] # sample 100 random cases to make it quicker
OmicSelector_table(table(dataset$Class), col.names = c("Class", "Number of cases"))
# For full analysis:
# merged = OmicSelector_prepare_split(metadane = dplyr::select(dataset, -starts_with("hsa")), ttpm = ttpm)
merged = OmicSelector_prepare_split(metadane = dplyr::select(dataset, -starts_with("hsa")), ttpm = dplyr::select(ttpm, selected_miRNAs))
knitr::kable(table(merged$Class, merged$mix))
## ----include = FALSE----------------------------------------------------------
OmicSelector_load_datamix()
## ----eval=FALSE---------------------------------------------------------------
# SMOTE = F # use unbalanced set
# deep_learning_results = OmicSelector_deep_learning(selected_miRNAs = selected_miRNAs, start = 5, end = 10) # use default set of hyperparameters and options
# DT::datatable(deep_learning_results,
# extensions = c('FixedColumns',"FixedHeader"),
# options = list(scrollX = TRUE,
# paging=FALSE,
# fixedHeader=TRUE))
## ----echo = FALSE-------------------------------------------------------------
deep_learning_results = data.table::fread("deeplearning_results.cs")
DT::datatable(deep_learning_results,
extensions = c('FixedColumns',"FixedHeader"),
options = list(scrollX = TRUE,
paging=FALSE,
fixedHeader=TRUE))
## ----eval=F-------------------------------------------------------------------
# OmicSelector_deep_learning_predict = function(model_path = "our_models/model5.zip",
# new_dataset = data.table::fread("Data/ks_data.csv"),
# new_scaling = F,
# old_train_csv_to_restore_scaling = NULL,
# override_cutoff = NULL,
# blinded = F)
kstawiski/OmicSelector documentation built on April 10, 2024, 11:11 p.m.
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## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE, message=FALSE, warning=FALSE,
comment = "#>"
)
knitr::opts_chunk$set(fig.width=12, fig.height=8)
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=150),tidy=TRUE)
options(rgl.useNULL = TRUE)
options(warn=-1)
suppressMessages(library(dplyr))
set.seed(1)
options(knitr.table.format = "html")
library(OmicSelector)
## -----------------------------------------------------------------------------
OmicSelector::OmicSelector_load_extension("deeplearning")
## ----eval=F-------------------------------------------------------------------
# OmicSelector_deep_learning = function(selected_miRNAs = ".",
# wd = getwd(),
# SMOTE = F,
# keras_batch_size = 64,
# clean_temp_files = T,
# save_threshold_trainacc = 0.85,
# save_threshold_testacc = 0.8,
# keras_epochae = 5000,
# keras_epoch = 2000,
# keras_patience = 50,
# hyperparameters = expand.grid(
# layer1 = seq(3, 11, by = 2),
# layer2 = c(0, seq(3, 11, by = 2)),
# layer3 c(0, seq(3, 11, by = 2)),
# activation_function_layer1 = c("relu", "sigmoid"),
# activation_function_layer2 = c("relu", "sigmoid"),
# activation_function_layer3 = c("relu", "sigmoid"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0, 0.1),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(T, F),
# layer3_regularizer = c(T, F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0, 7, -7),
# balanced = SMOTE,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# ),
# add_features_to_predictions = F,
# keras_threads = ceiling(parallel::detectCores() /
# 2),
# start = 1,
# end = nrow(hyperparameters),
# output_file = "deeplearning_results.csv",
# save_all_vars = F)
# OmicSelector_load_datamix()
## ----eval=T-------------------------------------------------------------------
balanced = F # not balanced
selected_miRNAs = c("hsa.a","hsa.b","hsa.c") # selected features
hyperparameters = expand.grid(
layer1 = seq(2, 10, by = 1),
layer2 = c(0),
layer3 = c(0),
activation_function_layer1 = c("relu", "sigmoid", "selu"),
activation_function_layer2 = c("relu"),
activation_function_layer3 = c("relu"),
dropout_layer1 = c(0, 0.1),
dropout_layer2 = c(0),
dropout_layer3 = c(0),
layer1_regularizer = c(T, F),
layer2_regularizer = c(F),
layer3_regularizer = c(F),
optimizer = c("adam", "rmsprop", "sgd"),
autoencoder = c(0, -7, 7),
balanced = balanced,
formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
scaled = c(T, F),
stringsAsFactors = F
)
DT::datatable(hyperparameters,
extensions = c('FixedColumns',"FixedHeader"),
options = list(scrollX = TRUE,
paging=FALSE,
fixedHeader=TRUE))
## ----eval=F-------------------------------------------------------------------
# hyperparameters_part1 = expand.grid(
# layer1 = seq(2, 10, by = 1),
# layer2 = c(0),
# layer3 = c(0),
# activation_function_layer1 = c("relu", "sigmoid", "selu"),
# activation_function_layer2 = c("relu"),
# activation_function_layer3 = c("relu"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(F),
# layer3_regularizer = c(F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0),
# balanced = balanced,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# )
# hyperparameters_part2 = expand.grid(
# layer1 = seq(3, 11, by = 2),
# layer2 = c(seq(3, 11, by = 2)),
# layer3 = c(seq(0, 11, by = 2)),
# activation_function_layer1 = c("relu", "sigmoid", "selu"),
# activation_function_layer2 = c("relu", "sigmoid", "selu"),
# activation_function_layer3 = c("relu", "sigmoid", "selu"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(F),
# layer3_regularizer = c(F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0),
# balanced = balanced,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# )
# hyperparameters = rbind(hyperparameters_part1, hyperparameters_part2)
## ----eval=F-------------------------------------------------------------------
# hyperparameters_part1 = expand.grid(
# layer1 = seq(2, 10, by = 1),
# layer2 = c(0),
# layer3 = c(0),
# activation_function_layer1 = c("relu", "sigmoid", "selu"),
# activation_function_layer2 = c("relu"),
# activation_function_layer3 = c("relu"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(F),
# layer3_regularizer = c(F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0, -7, 7),
# balanced = balanced,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# )
# hyperparameters_part2 = expand.grid(
# layer1 = seq(3, 11, by = 2),
# layer2 = c(seq(3, 11, by = 2)),
# layer3 = c(seq(0, 11, by = 2)),
# activation_function_layer1 = c("relu", "sigmoid", "selu"),
# activation_function_layer2 = c("relu", "sigmoid", "selu"),
# activation_function_layer3 = c("relu", "sigmoid", "selu"),
# dropout_layer1 = c(0, 0.1),
# dropout_layer2 = c(0),
# dropout_layer3 = c(0),
# layer1_regularizer = c(T, F),
# layer2_regularizer = c(F),
# layer3_regularizer = c(F),
# optimizer = c("adam", "rmsprop", "sgd"),
# autoencoder = c(0, -7, 7),
# balanced = balanced,
# formula = as.character(OmicSelector_create_formula(selected_miRNAs))[3],
# scaled = c(T, F),
# stringsAsFactors = F
# )
# hyperparameters = rbind(hyperparameters_part1, hyperparameters_part2)
## -----------------------------------------------------------------------------
data("orginal_TCGA_data")
suppressWarnings(suppressMessages(library(dplyr)))
cancer_cases = filter(orginal_TCGA_data, primary_site == "Pancreas" & sample_type == "PrimaryTumor")
control_cases = filter(orginal_TCGA_data, sample_type == "SolidTissueNormal")
cancer_cases$Class = "Case"
control_cases$Class = "Control"
dataset = rbind(cancer_cases, control_cases)
ttpm = OmicSelector_counts_to_log10tpm(danex = dplyr::select(dataset, starts_with("hsa")),
metadane = dplyr::select(dataset, -starts_with("hsa")),
ids = dataset$sample, filtr = F,
filtr_minimalcounts = 1,
filtr_howmany = 0.01)
ttpm = as.data.frame(ttpm)
zero_var = which(apply(ttpm, 2, var) == 0) #which have no variance
ttpm = ttpm[,-zero_var]
# Not run:
# DE = OmicSelector_differential_expression_ttest(ttpm_features = dplyr::select(dataset, starts_with("hsa")), classes = dataset$Class, mode = "logtpm")
# significant = DE$miR[DE$`p-value Bonferroni`<0.05]
selected_miRNAs = make.names(c('hsa-miR-192-5p', 'hsa-let-7g-5p', 'hsa-let-7a-5p', 'hsa-miR-194-5p', 'hsa-miR-122-5p', 'hsa-miR-340-5p', 'hsa-miR-26b-5p')) # some selected miRNAs
match(selected_miRNAs, colnames(ttpm))
#dataset = dataset[sample(1:nrow(dataset),200),] # sample 100 random cases to make it quicker
OmicSelector_table(table(dataset$Class), col.names = c("Class", "Number of cases"))
# For full analysis:
# merged = OmicSelector_prepare_split(metadane = dplyr::select(dataset, -starts_with("hsa")), ttpm = ttpm)
merged = OmicSelector_prepare_split(metadane = dplyr::select(dataset, -starts_with("hsa")), ttpm = dplyr::select(ttpm, selected_miRNAs))
knitr::kable(table(merged$Class, merged$mix))
## ----include = FALSE----------------------------------------------------------
OmicSelector_load_datamix()
## ----eval=FALSE---------------------------------------------------------------
# SMOTE = F # use unbalanced set
# deep_learning_results = OmicSelector_deep_learning(selected_miRNAs = selected_miRNAs, start = 5, end = 10) # use default set of hyperparameters and options
# DT::datatable(deep_learning_results,
# extensions = c('FixedColumns',"FixedHeader"),
# options = list(scrollX = TRUE,
# paging=FALSE,
# fixedHeader=TRUE))
## ----echo = FALSE-------------------------------------------------------------
deep_learning_results = data.table::fread("deeplearning_results.cs")
DT::datatable(deep_learning_results,
extensions = c('FixedColumns',"FixedHeader"),
options = list(scrollX = TRUE,
paging=FALSE,
fixedHeader=TRUE))
## ----eval=F-------------------------------------------------------------------
# OmicSelector_deep_learning_predict = function(model_path = "our_models/model5.zip",
# new_dataset = data.table::fread("Data/ks_data.csv"),
# new_scaling = F,
# old_train_csv_to_restore_scaling = NULL,
# override_cutoff = NULL,
# blinded = F)
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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