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app_tools.R
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# This script provides plotting, risk modeling and prediction tools for the app
# tools ----
require(plyr)
require(tidyverse)
require(stringi)
require(caret)
require(caretEnsemble)
require(readxl)
require(plotly)
require(writexl)
require(cowplot)
require(C50)
require(randomForest)
require(kernlab)
require(nnet)
require(glmnet)
## auxiliary scripts
scrpt_paths <- c('./tools/clust_tools2.R')
src_result <- try(scrpt_paths %>%
walk(source),
silent = T) ## if launched via app
if(any(class(src_result) == 'try-error')) {
scrpt_paths <- stri_replace(scrpt_paths,
fixed = './',
replacement = './CovILD Scorer/') ## if run standalone
src_result <- try(scrpt_paths %>%
walk(source),
silent = T)
}
# globals ----
globals <- list()
## graphics
globals$common_text <- element_text(size = 14,
face = 'plain',
color = 'black')
globals$common_margin <- ggplot2::margin(t = 5,
l = 5,
r = 5,
unit = 'mm')
globals$common_theme <- theme_classic() + theme(axis.text = globals$common_text,
axis.title = globals$common_text,
plot.title = element_text(size = 16,
face = 'bold'),
plot.subtitle = globals$common_text,
plot.tag = element_text(size = 14,
face = 'plain',
color = 'black',
hjust = 0,
vjust = 1),
plot.tag.position = 'bottom',
legend.text = globals$common_text,
legend.title = globals$common_text,
strip.text = globals$common_text,
plot.margin = globals$common_margin,
panel.grid.major = element_line(color = 'gray90'))
## ML model labels and colors
globals$model_labels <- c('C5.0' = 'C5.0',
'rf' = 'RF',
'svmRadial' = 'SVM-R',
'nnet' = 'NNet',
'glmnet' = 'glmNet',
'ensemble' = 'Ensemble',
'median' = 'Median')
globals$model_colors <- c('C5.0' = 'cadetblue3',
'rf' = 'darkolivegreen4',
'svmRadial' = 'steelblue',
'nnet' = 'gray60',
'glmnet' = 'plum2',
'ensemble' = 'coral3',
'median' = 'orangered3')
## app method text
globals$method_text <- list('Risk Clusters (LR: low risk, IR: intermediate risk, HR: high risk) were defined
in the training CovILD cohort (NCT04416100) by 52 binary non-CT and non-lung function parameters recorded during acute
SARS-CoV-2 infection and early convalescence (approx. 60 days post clinical onset). The clustering algorithm was
based on the combined self-organizing map (SOM) - hierarchical clustering procedure described by Vesanto (DOI: 10.1109/72.846731).
Prediction of cluster assignment in the user-provided patient data is done by a k-NN (k-nearest neighbors) label
algorithm (Leng et al., DOI: 10.3923/JSE.2014.14.22).',
'Machine learning classifiers: C5.0 (Quinlan, DOI: 10.5555/152181),
Random Forest (RF, Breiman, DOI: 10.1023/A:1010933404324),
shallow neural network (NNet, Ripley, DOI: 10.1017/CBO9780511812651),
support vector machines with radial kernel (SVM-R, Weston & Watkins, 1998) and
elastic net (glmNet, Friedman, DOI: 10.18637/jss.v033.i01) were trained
and cross-validated in the CovILD cohort data set including solely non-CT and non-lung function parameters recorded during acute
SARS-CoV-2 infection and early convalescence (approx. 60 days post clinical onset). Ensemble model was constructed based
on the glmNet algorithm.')
# reading the trained cluster objects and the ML models -----
if(class(try(load('./data/data.RData'), silent = TRUE)) == 'try-error') load('./CovILD Scorer/data/data.RData')
if(class(try(load('./data/clust.RData'), silent = TRUE)) == 'try-error') load('./CovILD Scorer/data/clust.RData')
if(class(try(load('./data/ml.RData'), silent = TRUE)) == 'try-error') load('./CovILD Scorer/data/ml.RData')
if(class(try(load('./data/clust_outcome.RData'), silent = TRUE)) == 'try-error') load('./CovILD Scorer/data/clust_outcome.RData')
# generating a plot panel with the prevalence of pulmonary disorders in the risk clusters ----
clust_patho <- clust_outcome$main_plots[c('CT_findings_rec',
'lung_function_impaired_V3')] %>%
map2(., c('CT abnormalities', 'Lung function impairment'),
~.x + labs(title = .y,
x = 'Risk Cluster')) %>%
map2(., c('Severity\nscore', ''),
~.x + labs(fill = .y)) %>%
map(~.x + globals$common_theme) %>%
plot_grid(plotlist = .,
ncol = 2,
align = 'hv')
# functions ----
read_input <- function(path = 'input_template.xlsx',
template_names = names(extract(part_clust$train_clust$clust_obj, 'data')$observation)) {
## reads the input excel file
input <- read_excel(path)
input <- input %>%
mutate(ID = `Patient ID`,
sex_male_V0 = ifelse(Sex == 'male', 'yes', 'no'),
sex_male_V0 = factor(sex_male_V0, c('no', 'yes')),
age_65_V0 = ifelse(`Age before CoV, years` > 65, 'yes', 'no'),
age_65_V0 = factor(age_65_V0, c('no', 'yes')),
obesity_rec_V0 = ifelse(`BMI before CoV, kg/m2` > 30, 'yes', 'no'),
obesity_rec_V0 = factor(obesity_rec_V0, c('no', 'yes')),
overweight_V0 = ifelse(`BMI before CoV, kg/m2` > 30 & obesity_rec_V0 == 'no', 'yes', 'no'),
overweight_V0 = factor(overweight_V0, c('no', 'yes')),
current_smoker_V0 = ifelse(smoking == 'active', 'yes', 'no'),
current_smoker_V0 = factor(current_smoker_V0, c('no', 'yes')),
smoking_ex_V0 = ifelse(smoking == 'ex', 'yes', 'no'),
smoking_ex_V0 = factor(smoking_ex_V0, c('no', 'yes')),
CVDis_rec_V0 = factor(`Cardiovascular disease`, c('no', 'yes')),
hypertension_rec_V0 = factor(Hypertension, c('no', 'yes')),
PDis_rec_V0 = factor(`Pulmonary disease`, c('no', 'yes')),
COPD_rec_V0 = factor(COPD, c('no', 'yes')),
asthma_rec_V0 = factor(Asthma, c('no', 'yes')),
endocrine_metabolic_rec_V0 = factor(`Endocrine or metabolic disease`, c('no', 'yes')),
hypercholesterolemia_rec_V0 = factor(Hypercholesterolemia, c('no', 'yes')),
diabetes_rec_V0 = factor(Diabetes, c('no', 'yes')),
CKDis_rec_V0 = factor(`Chronic kidney disease`, c('no', 'yes')),
GITDis_rec_V0 = factor(`Gastrointestinal disease`, c('no', 'yes')),
malignancy_rec_V0 = factor(Malignancy, c('no', 'yes')),
immune_deficiency_rec_V0 = factor(`Immune deficiency`, c('no', 'yes')),
weight_change_rec_V0 = factor(`Weight loss acute CoV`, c('no', 'yes')),
dyspnoe_rec_V0 = factor(`Dyspnoe acute CoV`, c('no', 'yes')),
cough_rec_V0 = factor(`Cough acute CoV`, c('no', 'yes')),
fever_rec_V0 = factor(`Fever acute CoV`, c('no', 'yes')),
night_sweat_rec_V0 = factor(`Night sweating acute CoV`, c('no', 'yes')),
pain_rec_V0 = factor(`Pain acute CoV`, c('no', 'yes')),
GI_sympt_rec_V0 = factor(`Gastrointestinal symptoms acute CoV`, c('no', 'yes')),
anosmia_rec_V0 = factor(`Hypo/anosmia acute CoV`, c('no', 'yes')),
ECOG_imp_rec_V0 = factor(`Impaired physical performance acute CoV`, c('no', 'yes')),
sleep_disorder_rec_V0 = factor(`Sleep problems acute CoV`, c('no', 'yes')),
treat_antiinfec_rec_V0 = factor(`Anti-infective therapy acute CoV`, c('no', 'yes')),
treat_antiplat_rec_V0 = factor(`Anti-platelet therapy acute CoV`, c('no', 'yes')),
treat_anticoag_rec_V0 = factor(`Anti-coagulation therapy acute CoV`, c('no', 'yes')),
treat_immunosuppr_rec_V0 = factor(`Immunosuppression acute CoV`, c('no', 'yes')),
anemia_rec_V1 = ifelse(sex_male_V0 == 'yes',
ifelse(`Hb, g/dL, 60-day FUP` < 13.5, 'yes', 'no'),
ifelse(`Hb, g/dL, 60-day FUP` < 12, 'yes', 'no')),
anemia_rec_V1 = factor(anemia_rec_V1, c('no', 'yes')),
ferr_elv_rec_V1 = ifelse(sex_male_V0 == 'yes',
ifelse(`Ferritin, ng/mL, 60-day FUP` > 300, 'yes', 'no'),
ifelse(`Ferritin, ng/mL, 60-day FUP` > 150, 'yes', 'no')),
ferr_elv_rec_V1 = factor(ferr_elv_rec_V1, c('no', 'yes')),
NTelv_rec_V1 = ifelse(`NT-pro-BNP, pg/mL, 60-day FUP` > 125, 'yes', 'no'),
NTelv_rec_V1 = factor(NTelv_rec_V1, c('no', 'yes')),
Ddimerelv_rec_V1 = ifelse(`D-dimer, pg/ml FEU, 60-day FUP` > 500, 'yes', 'no'),
Ddimerelv_rec_V1 = factor(Ddimerelv_rec_V1, c('no', 'yes')),
CRP_elv_rec_V1 = ifelse(`CRP, mg/dL, 60-day FUP` > 0.5, 'yes', 'no'),
CRP_elv_rec_V1 = factor(CRP_elv_rec_V1, c('no', 'yes')),
IL6_elv_rec_V1 = ifelse(`IL6, pg/mL, 60-day FUP` > 7, 'yes', 'no'),
IL6_elv_rec_V1 = factor(IL6_elv_rec_V1, c('no', 'yes')),
iron_deficiency_30_rec_V1 = ifelse(`TSAT, %, 60-day FUP` < 15, 'yes', 'no'),
iron_deficiency_30_rec_V1 = factor(iron_deficiency_30_rec_V1, c('no', 'yes')),
hosp_7d_V0 = ifelse(`Hospitalization, days, acute CoV` > 7, 'yes', 'no'),
hosp_7d_V0 = factor(hosp_7d_V0, c('no', 'yes')),
comorb_sum = as.numeric(CVDis_rec_V0) - 1 +
as.numeric(hypertension_rec_V0) - 1 +
as.numeric(PDis_rec_V0) - 1 +
as.numeric(COPD_rec_V0) - 1 +
as.numeric(asthma_rec_V0) - 1 +
as.numeric(endocrine_metabolic_rec_V0) - 1 +
as.numeric(hypercholesterolemia_rec_V0) - 1 +
as.numeric(diabetes_rec_V0) - 1 +
as.numeric(CKDis_rec_V0) - 1 +
as.numeric(GITDis_rec_V0) - 1 +
as.numeric(malignancy_rec_V0) - 1,
comorb_present_V0 = ifelse(comorb_sum > 0, 'yes', 'no'),
comorb_present_V0 = factor(comorb_present_V0, c('no', 'yes')),
comorb_3_V0 = ifelse(comorb_sum > 3, 'yes', 'no'),
comorb_3_V0 = factor(comorb_3_V0, c('no', 'yes')),
sympt_sum = as.numeric(dyspnoe_rec_V0) - 1 +
as.numeric(cough_rec_V0) - 1 +
as.numeric(fever_rec_V0) - 1 +
as.numeric(night_sweat_rec_V0) - 1 +
as.numeric(pain_rec_V0) - 1 +
as.numeric(GI_sympt_rec_V0) - 1 +
as.numeric(anosmia_rec_V0) - 1 +
as.numeric(ECOG_imp_rec_V0) - 1 +
as.numeric(sleep_disorder_rec_V0) - 1,
sympt_6_V0 = ifelse(sympt_sum > 6, 'yes', 'no'),
sympt_6_V0 = factor(sympt_6_V0, c('no', 'yes')),
sympt_present_V1 = factor(`Symptoms present, 60-day FUP`, c('no', 'yes')),
ab_0_V1 = ifelse(`anti-S1/S2 IgG, BAU, 60-day FUP` <= 312.36, 'yes', 'no'),
ab_0_V1 = factor(ab_0_V1, c('no', 'yes')),
ab_25_V1 = ifelse(312.36 < `anti-S1/S2 IgG, BAU, 60-day FUP` & `anti-S1/S2 IgG, BAU, 60-day FUP` <= 644.10, 'yes', 'no'),
ab_25_V1 = factor(ab_25_V1, c('no', 'yes')),
ab_50_V1 = ifelse(644.10 < `anti-S1/S2 IgG, BAU, 60-day FUP` & `anti-S1/S2 IgG, BAU, 60-day FUP` <= 974.70, 'yes', 'no'),
ab_50_V1 = factor(ab_50_V1, c('no', 'yes')),
ab_75_V1 = ifelse(`anti-S1/S2 IgG, BAU, 60-day FUP` > 974.70, 'yes', 'no'),
ab_75_V1 = factor(ab_75_V1, c('no', 'yes')),
pat_group_G1_V0 = ifelse(`Hospitalization, days, acute CoV` == 0, 'yes', 'no'),
pat_group_G1_V0 = factor(pat_group_G1_V0, c('no', 'yes')),
pat_group_G2_V0 = ifelse(`Hospitalization, days, acute CoV` > 0 & `Oxygen need, acute CoV2` == 'no', 'yes', 'no'),
pat_group_G2_V0 = factor(pat_group_G2_V0, c('no', 'yes')),
pat_group_G3_V0 = ifelse(`Hospitalization, days, acute CoV` > 0 & `Oxygen need, acute CoV2` == 'yes' & `ICU, acute CoV2` == 'no', 'yes', 'no'),
pat_group_G3_V0 = factor(pat_group_G3_V0, c('no', 'yes')),
pat_group_G4_V0 = factor(`ICU, acute CoV2`, c('no', 'yes'))) %>%
select(ID, all_of(template_names))
incomplete_records <- input %>%
filter(!complete.cases(.)) %>%
.$ID
input <- input %>%
filter(complete.cases(.))
clust_tbl <- input %>%
select(- ID) %>%
map_dfc(~as.numeric(.x) - 1) %>%
as.data.frame
rownames(clust_tbl) <- input$ID
ml_tbl <- input %>%
column_to_rownames('ID')
list(incomplete_records = incomplete_records,
clust_tbl = clust_tbl,
ml_tbl = ml_tbl)
}
predict_risk_clust <- function(combi_analysis_object = part_clust$train_clust$clust_obj,
newdata,
k = 5) {
## makes risk cluster predictions based on label propagation
predictions <- predict(part_clust$train_clust$clust_obj,
newdata = newdata,
type = 'propagation',
k = k)
## baustelle: hm and pca plot
return(predictions)
}
plot_risk_clust <- function(train_analysis_object = part_clust$train_clust$clust_obj,
test_analysis_object,
plot_title = NULL,
plot_subtitle = NULL,
interactive = FALSE) {
## generates a heat map with the test predictions highlighted
## cluster/node assignment
cmm_ass <- rbind(extract(train_analysis_object, 'assignment'),
extract(test_analysis_object, 'assignment'))
## plotting table
training_data <- extract(train_analysis_object, 'data')$observation
clust_features <- names(training_data)
plotting_tbl <- rbind(training_data,
extract(test_analysis_object, 'data'))
## appending the plotting table with the cluster assignment info
plotting_tbl <- plotting_tbl %>%
rownames_to_column('observation') %>%
gather(key = 'feature',
value = 'present',
all_of(clust_features)) %>%
left_join(cmm_ass, by = 'observation') %>%
mutate(present = ifelse(present == 1, 'yes', 'no'),
present = factor(present),
feature = part_clust$ft_labels[feature],
highlight = ifelse(observation %in% rownames(training_data),
'no', 'yes'))
## n numbers in the training collective
n_tag <- ngroups(train_analysis_object)
n_tag <- map2_chr(n_tag$clust_id,
n_tag$n,
~paste(.x, .y, sep = ': n = ')) %>%
paste(collapse = ', ') %>%
paste0('\n', .)
## plotting
hm <- plotting_tbl %>%
ggplot(aes(x = reorder(observation, as.numeric(node)),
y = reorder(feature, as.numeric(present)),
fill = present,
alpha = highlight)) +
geom_tile() +
facet_grid(.~clust_id,
scales = 'free',
space = 'free') +
scale_fill_manual(values = c(no = 'steelblue4',
yes = 'indianred3'),
name = 'Feature present') +
scale_alpha_manual(values = c(no = 0.5,
yes = 1)) +
guides(alpha = FALSE) +
globals$common_theme +
theme(axis.title.y = element_blank(),
axis.text.x = element_blank(),
axis.line.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text.y = element_text(size = 12)) +
labs(title = plot_title,
subtitle = plot_subtitle,
x = 'Patient',
tag = n_tag)
if(interactive) {
ggplotly(hm)
} else {
hm
}
}
pred_risk_clust_output <- function(test_analysis_object,
path = NULL) {
## makes a nice cluster assignment output
out_tbl <- extract(test_analysis_object, 'assignment') %>%
set_names(c('Patient', 'SOM node', 'Risk cluster'))
if(!is.null(path)) {
write_xlsx(out_tbl, path = path)
}
out_tbl
}
predict_ml <- function(train_list, newdata) {
## returns predictions (probabilities) by the single models and the ensemble
## a table with predictions
single_preds <- train_list$model_list %>%
map(predict,
newdata = newdata,
type = 'prob') %>%
map(as_tibble) %>%
map2_dfr(., names(.), ~mutate(.x[, 'yes'],
ID = rownames(newdata),
method = .y)) %>%
select(ID, method, yes) %>%
set_names(c('ID', 'method', 'risk'))
ensemble_preds <- predict(train_list$ensemble,
newdata = newdata,
type = 'prob')
ensemble_preds <- tibble(ID = rownames(newdata),
method = 'ensemble',
risk = ensemble_preds)
pred_tbl <- rbind(single_preds,
ensemble_preds)
## adding the plot order
order_tbl <- tibble(ID = rownames(newdata),
obs_order = 1:nrow(newdata))
## the classifier result median
med_tbl <- pred_tbl %>%
group_by(ID) %>%
summarise(risk = median(risk, na.rm = TRUE)) %>%
mutate(method = 'median')
rbind(pred_tbl, med_tbl) %>%
left_join(order_tbl, by = 'ID') %>%
arrange(obs_order, method)
}
plot_ml_risk <- function(pred_tbl,
plot_title = NULL,
plot_subtitle = NULL,
plot_tag = NULL,
interactive = FALSE) {
## plots the ml risk estimates given the output of the predict_ml() function
pred_tbl <- pred_tbl %>%
mutate(method = globals$model_labels[method])
med_tbl <- pred_tbl %>%
filter(method == 'Median')
pred_tbl <- pred_tbl %>%
filter(method != 'Median')
if(!interactive) {
base_plot <- pred_tbl %>%
ggplot(aes(y = risk,
x = reorder(ID, obs_order)))
} else {
base_plot <- pred_tbl %>%
ggplot(aes(y = risk,
x = reorder(ID, obs_order),
text = paste0('Patient: ', ID,
'\nRisk: ', signif(risk, 2),
'\nmethod: ', method)))
}
full_plot <- base_plot +
geom_hline(yintercept = 0.5,
linetype = 'dashed') +
geom_violin(aes(group = ID),
alpha = 0.25,
fill = 'cornsilk') +
scale_y_continuous(limits = c(0, 1)) +
coord_flip() +
geom_point(aes(color = method),
shape = 16,
size = 2,
alpha = 0.8,
position = position_jitter(width = 0, height = 0.1)) +
geom_point(data = med_tbl,
shape = 23,
size = 3.5,
fill = 'orangered3') +
scale_color_manual(values = set_names(globals$model_colors, globals$model_labels),
name = 'ML Algorithm') +
globals$common_theme +
labs(title = plot_title,
subtitle = plot_subtitle,
tag = plot_tag,
y = 'Risk estimate',
x = 'Patient')
if(interactive) {
full_plot %>%
ggplotly(tooltip = 'text')
} else {
full_plot
}
}
plot_ml_pred <- function(pred_tbl,
plot_title = NULL,
plot_subtitle = NULL,
plot_tag = NULL,
interactive = FALSE) {
## plots the ml predictions given the output of the predict_ml() function
pred_tbl <- pred_tbl %>%
mutate(method = globals$model_labels[method],
prediction = ifelse(risk > 0.5, 'yes', 'no'))
med_tbl <- pred_tbl %>%
filter(method == 'Median')
pred_tbl <- pred_tbl %>%
filter(method != 'Median')
if(!interactive) {
base_plot <- pred_tbl %>%
ggplot(aes(y = prediction,
x = reorder(ID, obs_order)))
} else {
base_plot <- pred_tbl %>%
ggplot(aes(y = prediction,
x = reorder(ID, obs_order),
text = paste0('Patient: ', ID,
'\nPrediction: ', prediction,
'\nRisk: ', signif(risk, 2),
'\nmethod: ', method)))
}
full_plot <- base_plot +
coord_flip() +
geom_point(data = med_tbl,
aes(color = risk),
shape = 16,
size = 7) +
geom_point(shape = 16,
size = 2,
alpha = 0.5,
color = 'gray60',
position = position_jitter(width = 0.1, height = 0.1)) +
scale_color_gradient2(low = 'steelblue3',
mid = 'gray80',
high = 'firebrick3',
midpoint = 0.5,
limits = c(0, 1),
name = 'Median risk\nestimate') +
globals$common_theme +
labs(title = plot_title,
subtitle = plot_subtitle,
tag = plot_tag,
y = 'Prediction',
x = 'Patient')
if(interactive) {
full_plot %>%
ggplotly(tooltip = 'text')
} else {
full_plot
}
}
plot_ml <- function(pred_tbl, plot_type = c('prediction', 'risk'), ...) {
plot_type <- match.arg(plot_type[1], c('prediction', 'risk'))
if(plot_type == 'prediction') {
plot_ml_pred(pred_tbl = pred_tbl, ...)
} else {
plot_ml_risk(pred_tbl = pred_tbl, ...)
}
}
format_prediction <- function(pred_tbl,
response_txt = 'Any CT abnormality',
path = NULL) {
## makes a nice table with the ML predictions
out_tbl <- pred_tbl %>%
mutate(method = globals$model_labels[method],
response = response_txt,
risk = signif(risk, 3),
predicted_outcome = ifelse(risk > 0.5, 'present', 'absent')) %>%
select(ID, method, response, risk, predicted_outcome) %>%
set_names(c('Patient', 'Algoritm', 'Outcome', 'Risk estimate', 'Prediction'))
if(!is.null(path)) {
write_xlsx(out_tbl, path = path)
}
out_tbl
}
# END ----