The goal of CytoProfile is to conduct quality control using biological meaningful cutoff on raw measured values of cytokines. Specifically, test on distributional symmetry to suggest the adopt of transformation. Conduct exploratory analysis including summary statistics, generate enriched barplots, and boxplots. Further, conduct univariate analysis and multivariate analysis for advance analysis.
Before installation of the CytoProfile package, make sure to install BiocManager and mix0mics packages using:
## install BiocManager
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
## install mixOmics
BiocManager::install('mixOmics')You can install the development version of CytoProfile from GitHub with:
# install.packages("devtools")
devtools::install_github("saraswatsh/CytoProfile")Install CytoProfile from CRAN with:
install.packages("CytoProfile")Below are examples of using the functions provided in CytoProfile. Any saved or generated files that are PDF or PNG format will be found at in the Output Folder.
# Loading all packages required
# Data manipulation and reshaping
library(dplyr) # For data filtering, grouping, and summarising.
library(tidyr) # For reshaping data (e.g., pivot_longer, pivot_wider).
# Plotting and visualization
library(ggplot2) # For creating all the ggplot-based visualizations.
library(gridExtra) # For arranging multiple plots on a single page.
library(ggrepel) # For improved label placement in plots (e.g., volcano plots).
library(gplots) # For heatmap.2, which is used to generate heatmaps.
library(plot3D) # For creating 3D scatter plots in PCA and sPLS-DA analyses.
library(reshape2) # For data transformation (e.g., melt) in cross-validation plots.
# Statistical analysis
library(mixOmics) # For multivariate analyses (PCA, sPLS-DA, etc.).
library(e1071) # For computing skewness and kurtosis.
library(pROC) # For ROC curve generation in machine learning model evaluation.
# Machine learning
library(xgboost) # For building XGBoost classification models.
library(randomForest) # For building Random Forest classification models.
library(caret) # For cross-validation and other machine learning utilities.
# Package development and document rendering
library(knitr) # For knitting RMarkdown files and setting chunk options.
library(devtools) # For installing the development version of the package from GitHub.
# Load in the CytoProfile package
library(CytoProfile)
# Loading in data
data("ExampleData1")
data_df <- ExampleData1
## Setting working directory to output folder to save the PDF files.
opts_knit$set(root.dir = "E:/Desktop/RA/R Package/CytoProfile/output")# Generating boxplots to check for outliers for raw values
cyt_bp(data_df[, -c(1:3)],
pdf_title = "boxplot_by_cytokine_raw.pdf")
# Removing the first 3 columns to retain only continuous variables.
# Generating boxplots to check for outliers for log2 values
cyt_bp(data_df[, -c(1:3)],
pdf_title = "boxplot_by_cytokine_log2.pdf",
scale = "log2")
# Using log2 transformation for cytokine values.# Raw values for group-specific boxplots
cyt_bp2(data_df[, -c(3)],
pdf_title = "boxplot_by_group_and_treatment_raw.pdf",
scale = NULL)
#> png
#> 2
# Log2-transformed group-specific boxplots
cyt_bp2(data_df[, -c(3)],
pdf_title = "boxplot_by_group_and_treatment_log2.pdf",
scale = "log2")
#> png
#> 2# Histogram of skewness and kurtosis for raw data
cyt_skku(data_df[, -c(1:3)],
pdf_title = "skew_and_kurtosis.pdf",
group_cols = NULL)
# Histogram of skewness and kurtosis with grouping (e.g., "Group")
cyt_skku(ExampleData1[, -c(2:3)],
pdf_title = "skew_and_kurtosis_2.pdf",
group_cols = c("Group"))# Generating basic error bar plots
cytokine_mat <- ExampleData1[, -c(1:3)] # Extract all cytokines
cytokineNames <- colnames(cytokine_mat) # Extract cytokine names
nCytokine <- length(cytokineNames) # Total number of cytokines
results <- cyt_skku(ExampleData1[, -c(3)], print_res_log = TRUE,
group_cols = c("Group", "Treatment"))
pdf("bar_error_plot.pdf")
par(mfrow = c(2,2), mar = c(8.1, 4.1, 4.1, 2.1))
for (k in 1:nCytokine) {
center_df <- data.frame(name = rownames(results[,,k]), results[,,k])
cyt_errbp(center_df,
p_lab = FALSE, es_lab = FALSE, class_symbol = TRUE,
y_lab = "Concentration in log2 scale", main = cytokineNames[k])
}
dev.off()
#> png
#> 2# Generating Error Bar Plot enriched with p-value and effect size
data_df <- ExampleData1[, -3]
cyt_mat <- log2(data_df[, -c(1:2)])
data_df1 <- data.frame(data_df[, 1:2], cyt_mat)
cytokineNames <- colnames(cyt_mat)
nCytokine <- length(cytokineNames)
condt <- !is.na(cyt_mat) & (cyt_mat > 0)
Cutoff <- min(cyt_mat[condt], na.rm = TRUE) / 10
# Create matrices for ANOVA and Tukey results
p_aov_mat <- matrix(NA, nrow = nCytokine, ncol = 3)
dimnames(p_aov_mat) <- list(cytokineNames,
c("Group", "Treatment", "Interaction"))
p_groupComp_mat <- matrix(NA, nrow = nCytokine, ncol = 3)
dimnames(p_groupComp_mat) <- list(cytokineNames,
c("2-1", "3-1", "3-2"))
ssmd_groupComp_stm_mat <- mD_groupComp_stm_mat <- p_groupComp_stm_mat <-
p_groupComp_mat
for (i in 1:nCytokine) {
Cytokine <- (cyt_mat[, i] + Cutoff)
cytokine_aov <- aov(Cytokine ~ Group * Treatment, data = data_df)
aov_table <- summary(cytokine_aov)[[1]]
p_aov_mat[i, ] <- aov_table[1:3, 5]
p_groupComp_mat[i, ] <- TukeyHSD(cytokine_aov)$Group[1:3, 4]
p_groupComp_stm_mat[i, ] <- TukeyHSD(cytokine_aov)$`Group:Treatment`[1:3, 4]
mD_groupComp_stm_mat[i, ] <- TukeyHSD(cytokine_aov)$`Group:Treatment`[1:3, 1]
ssmd_groupComp_stm_mat[i, ] <- mD_groupComp_stm_mat[i, ] /
sqrt(2 * aov_table["Residuals", "Mean Sq"])
}
results <- cyt_skku(ExampleData1[, -c(3)], print_res_log = TRUE,
group_cols = c("Group", "Treatment"))
pdf("bar_error_plot_enriched.pdf")
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
par(mfrow = c(2,3), mar = c(8.1, 4.1, 4.1, 2.1))
for (k in 1:nCytokine) {
result_mat <- results[1:9, , k]
center_df <- data.frame(
name = rownames(result_mat),
result_mat[, c("center", "spread")],
p.value = c(1, p_groupComp_stm_mat[k, 1:2]),
effect.size = c(0, ssmd_groupComp_stm_mat[k, 1:2])
)
cyt_errbp(center_df, p_lab = TRUE, es_lab = TRUE,
class_symbol = TRUE,
y_lab = "Concentration in log2 scale",
main = cytokineNames[k])
}
dev.off()
#> png
#> 2# Performing Two Sample T-test and Mann Whitney U Test
data_df <- ExampleData1[, -c(3)]
data_df <- filter(data_df, Group != "ND", Treatment != "Unstimulated")
# Two sample T-test
cyt_ttest(data_df[, c(1:2, 5:6)], scale = "log2")
#> T-test p-value for PreT2D vs T2D on IFN.G: 0.02082
#> T-test p-value for PreT2D vs T2D on IL.10: 0.02484
#> T-test p-value for CD3/CD28 vs LPS on IFN.G: 7.31e-22
#> T-test p-value for CD3/CD28 vs LPS on IL.10: 0.0001402
# Mann-Whitney U Test
cyt_ttest(data_df[, c(1:2, 5:6)])
#> Mann-Whitney U test p-value for PreT2D vs T2D on IFN.G: 0.008462
#> Mann-Whitney U test p-value for PreT2D vs T2D on IL.10: 0.01191
#> Mann-Whitney U test p-value for CD3/CD28 vs LPS on IFN.G: 5.915e-19
#> Mann-Whitney U test p-value for CD3/CD28 vs LPS on IL.10: 3.278e-05# Perform ANOVA comparisons test (example with 2 cytokines)
cyt_anova(data_df[, c(1:2, 5:6)])
#> $IFN.G_Group
#> [1] 0.00356939
#>
#> $IL.10_Group
#> [1] 0.003323717
#>
#> $IFN.G_Treatment
#> [1] 3.805612e-08
#>
#> $IL.10_Treatment
#> [1] 0.0002059574# cyt.plsda function.
data <- ExampleData1[, -c(3)]
data_df <- dplyr::filter(data, Group != "ND" & Treatment == "CD3/CD28")
cyt_splsda(data_df,
pdf_title = "example_spls_da_analysis.pdf",
colors = c("black", "purple"),
bg = FALSE, scale = "log2", ellipse = TRUE,
conf_mat = TRUE, var_num = 25,
cv_opt = "loocv",
comp_num = 3, pch_values = c(16, 4),
style = "3d",
group_col = "Group", group_col2 = "Treatment",
roc = TRUE)
#> [1] "CD3/CD28 LOOCV Accuracy: 77%"
#> [1] "CD3/CD28 LOOCV Accuracy (VIP>1): 77%"
#> Confusion Matrix for PLS-DA Comparison: CD3/CD28
#> Reference
#> Prediction PreT2D T2D
#> PreT2D 28 7
#> T2D 5 26
#> Accuracy: 0.82
#> Sensitivity: 0.85
#> Specificity: 0.79
#> Confusion Matrix for PLS-DA Comparison with VIP > 1: CD3/CD28
#> Reference
#> Prediction PreT2D T2D
#> PreT2D 27 6
#> T2D 6 27
#> Accuracy: 0.82
#> Sensitivity: 0.82
#> Specificity: 0.82
#> png
#> 2data <- ExampleData1[, -c(3,23)]
data_df <- filter(data, Group != "ND" & Treatment != "Unstimulated")
cyt_pca(data_df,
pdf_title = "example_pca_analysis.pdf",
colors = c("black", "red2"),
scale = "log2",
comp_num = 3, pch_values = c(16, 4),
style = "3D", group_col = "Group", group_col2 = "Treatment")
#> [1] "Results based on log2 transformation:"
#> png
#> 2
cyt_pca(data_df,
pdf_title = "example_pca_analysis_2.pdf",
colors = c("black", "red2"),
scale = "log2",
comp_num = 2, pch_values = c(16, 4),
group_col = "Group")
#> [1] "Results based on log2 transformation:"
#> png
#> 2# Generating Volcano Plot
data_df <- ExampleData1[, -c(2:3)]
volc_plot <- cyt_volc(data_df, group_col = "Group",
cond1 = "T2D", cond2 = "ND",
fold_change_thresh = 2.0,
top_labels = 15)
#> cytokine fc_log p_log significant
#> IL.12.P70 IL.12.P70 -2.60117683 2.18641971 TRUE
#> IL.6 IL.6 -0.95013174 3.94758527 FALSE
#> IL.27 IL.27 -0.67878724 2.33099419 FALSE
#> IL.23 IL.23 -0.87320747 1.95290632 FALSE
#> CCL.20.MIP.3A CCL.20.MIP.3A -0.48569948 1.40917287 FALSE
#> IL.2 IL.2 -0.80577278 1.22848122 FALSE
#> IL.17F IL.17F -0.93024059 1.16938373 FALSE
#> IL.10 IL.10 -0.48121242 1.01734902 FALSE
#> IL.28A IL.28A -0.31081278 0.98351262 FALSE
#> IL.17A IL.17A -0.80415853 0.90173665 FALSE
#> IL.1B IL.1B -0.61564856 0.83381951 FALSE
#> GM.CSF GM.CSF 0.45980342 0.62042612 FALSE
#> IL.21 IL.21 -0.62254771 0.51843946 FALSE
#> IL.17E.IL.25 IL.17E.IL.25 0.01449957 0.49515782 FALSE
#> IL.22 IL.22 -0.30363695 0.47550506 FALSE
#> IL.9 IL.9 -0.32752255 0.43117675 FALSE
#> TNF.A TNF.A -0.15647551 0.21142412 FALSE
#> IL.31 IL.31 0.21056699 0.20929529 FALSE
#> IL.4 IL.4 0.21161574 0.20542291 FALSE
#> IL.5 IL.5 -0.20808037 0.17512546 FALSE
#> IL.15 IL.15 -0.05298748 0.12055764 FALSE
#> IL.13 IL.13 -0.07717527 0.06654004 FALSE
#> IFN.G IFN.G -0.09088794 0.06221451 FALSE
#> TNF.B TNF.B 0.07037796 0.05224667 FALSE
#> IL.33 IL.33 0.01213249 0.01719622 FALSE
ggsave("volcano_plot.png", plot = volc_plot$`T2D vs ND`,
dpi = 300)
# Print the final plot data (excluding the label column)
print(volc_plot$`T2D vs ND`$data)
#> cytokine fc_log p_log significant label
#> IL.12.P70 IL.12.P70 -2.60117683 2.18641971 TRUE IL.12.P70
#> IL.6 IL.6 -0.95013174 3.94758527 FALSE IL.6
#> IL.27 IL.27 -0.67878724 2.33099419 FALSE IL.27
#> IL.23 IL.23 -0.87320747 1.95290632 FALSE IL.23
#> CCL.20.MIP.3A CCL.20.MIP.3A -0.48569948 1.40917287 FALSE CCL.20.MIP.3A
#> IL.2 IL.2 -0.80577278 1.22848122 FALSE IL.2
#> IL.17F IL.17F -0.93024059 1.16938373 FALSE IL.17F
#> IL.10 IL.10 -0.48121242 1.01734902 FALSE IL.10
#> IL.28A IL.28A -0.31081278 0.98351262 FALSE IL.28A
#> IL.17A IL.17A -0.80415853 0.90173665 FALSE IL.17A
#> IL.1B IL.1B -0.61564856 0.83381951 FALSE IL.1B
#> GM.CSF GM.CSF 0.45980342 0.62042612 FALSE GM.CSF
#> IL.21 IL.21 -0.62254771 0.51843946 FALSE IL.21
#> IL.17E.IL.25 IL.17E.IL.25 0.01449957 0.49515782 FALSE IL.17E.IL.25
#> IL.22 IL.22 -0.30363695 0.47550506 FALSE IL.22
#> IL.9 IL.9 -0.32752255 0.43117675 FALSE
#> TNF.A TNF.A -0.15647551 0.21142412 FALSE
#> IL.31 IL.31 0.21056699 0.20929529 FALSE
#> IL.4 IL.4 0.21161574 0.20542291 FALSE
#> IL.5 IL.5 -0.20808037 0.17512546 FALSE
#> IL.15 IL.15 -0.05298748 0.12055764 FALSE
#> IL.13 IL.13 -0.07717527 0.06654004 FALSE
#> IFN.G IFN.G -0.09088794 0.06221451 FALSE
#> TNF.B TNF.B 0.07037796 0.05224667 FALSE
#> IL.33 IL.33 0.01213249 0.01719622 FALSE# Generating Heat map
cyt_heatmap(data = data_df,
scale = "log2", # Optional scaling
annotation_col_name = "Group",
title = "heatmap.png")
#> png
#> 2# Generating dual flashlights plot
data_df <- ExampleData1[, -c(2:3)]
dfp <- cyt_dualflashplot(data_df, group_var = "Group",
group1 = "T2D", group2 = "ND",
ssmd_thresh = -0.2, log2fc_thresh = 1,
top_labels = 10)
#> # A tibble: 25 × 11
#> cytokine mean_ND mean_PreT2D mean_T2D variance_ND variance_PreT2D
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 CCL.20.MIP.3A 634. 404. 887. 6.72e+ 5 2.74e+5
#> 2 GM.CSF 2.65 3.11 1.92 2.63e+ 1 3.14e+1
#> 3 IFN.G 57730. 18303. 61484. 2.86e+10 2.30e+9
#> 4 IL.10 979. 836. 1366. 1.99e+ 6 1.19e+6
#> 5 IL.12.P70 13.0 39.1 78.9 4.15e+ 2 2.56e+4
#> 6 IL.13 1064. 1543. 1122. 5.60e+ 6 1.11e+7
#> 7 IL.15 7.92 4.29 8.22 3.54e+ 1 2.58e+1
#> 8 IL.17A 352. 653. 615. 9.40e+ 5 2.88e+6
#> 9 IL.17E.IL.25 0.0101 0.0163 0.01 1.01e- 6 3.88e-3
#> 10 IL.17F 1.63 2.35 3.11 1.56e+ 1 3.37e+1
#> 11 IL.1B 2806. 2977. 4299. 6.63e+ 7 3.76e+7
#> 12 IL.2 9227. 10718. 16129. 2.60e+ 8 4.10e+8
#> 13 IL.21 205. 210. 316. 3.15e+ 5 2.49e+5
#> 14 IL.22 0.0513 0.0684 0.0633 4.58e- 3 4.51e-3
#> 15 IL.23 0.147 0.243 0.269 3.13e- 2 9.37e-2
#> 16 IL.27 0.0662 0.0834 0.106 6.18e- 3 5.66e-3
#> 17 IL.28A 0.0537 0.0710 0.0666 2.45e- 3 5.10e-3
#> 18 IL.31 0.0409 0.0905 0.0354 6.62e- 3 4.88e-2
#> 19 IL.33 1.17 1.43 1.16 2.09e+ 0 2.71e+0
#> 20 IL.4 0.344 0.707 0.297 4.24e- 1 2.96e+0
#> 21 IL.5 134. 340. 155. 1.09e+ 5 9.88e+5
#> 22 IL.6 4620. 5197. 8925. 2.86e+ 7 5.72e+7
#> 23 IL.9 203. 256. 254. 1.34e+ 5 2.11e+5
#> 24 TNF.A 5046. 3069. 5624. 7.02e+ 7 1.63e+7
#> 25 TNF.B 0.641 0.709 0.610 2.37e+ 0 2.76e+0
#> # ℹ 5 more variables: variance_T2D <dbl>, ssmd <dbl>, log2FC <dbl>,
#> # SSMD_Category <chr>, Significant <lgl>
ggsave("dual_flashlight_plot.png", plot = dfp$plot_env$p, dpi = 300,
width = 3000, height = 2000, units = "px")
# Print the table data used for plotting
print(dfp$data)
#> # A tibble: 25 × 11
#> cytokine mean_ND mean_PreT2D mean_T2D variance_ND variance_PreT2D
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 CCL.20.MIP.3A 634. 404. 887. 6.72e+ 5 2.74e+5
#> 2 GM.CSF 2.65 3.11 1.92 2.63e+ 1 3.14e+1
#> 3 IFN.G 57730. 18303. 61484. 2.86e+10 2.30e+9
#> 4 IL.10 979. 836. 1366. 1.99e+ 6 1.19e+6
#> 5 IL.12.P70 13.0 39.1 78.9 4.15e+ 2 2.56e+4
#> 6 IL.13 1064. 1543. 1122. 5.60e+ 6 1.11e+7
#> 7 IL.15 7.92 4.29 8.22 3.54e+ 1 2.58e+1
#> 8 IL.17A 352. 653. 615. 9.40e+ 5 2.88e+6
#> 9 IL.17E.IL.25 0.0101 0.0163 0.01 1.01e- 6 3.88e-3
#> 10 IL.17F 1.63 2.35 3.11 1.56e+ 1 3.37e+1
#> # ℹ 15 more rows
#> # ℹ 5 more variables: variance_T2D <dbl>, ssmd <dbl>, log2FC <dbl>,
#> # SSMD_Category <chr>, Significant <lgl># Using XGBoost for classification
data_df0 <- ExampleData1
data_df <- data.frame(data_df0[, 1:3], log2(data_df0[, -c(1:3)]))
data_df <- data_df[, -c(2:3)]
data_df <- filter(data_df, Group != "ND")
xgb_results <- cyt_xgb(data = data_df, group_col = "Group",
nrounds = 500, max_depth = 4, eta = 0.05,
nfold = 5, cv = TRUE, eval_metric = "mlogloss",
early_stopping_rounds = NULL, top_n_features = 10,
verbose = 0, plot_roc = TRUE)
#>
#> ### Group to Numeric Label Mapping ###
#> PreT2D T2D
#> 0 1
#>
#> ### TRAINING XGBOOST MODEL ###
#>
#> Best iteration from training (based on mlogloss ):
#> iter train_mlogloss test_mlogloss
#> <num> <num> <num>
#> 1: 313 0.01860717 0.4529601
#>
#> AUC: 0.9155767
#>
#> ### Confusion Matrix on Test Set ###
#> Confusion Matrix and Statistics
#>
#> Reference
#> Prediction 0 1
#> 0 25 7
#> 1 4 22
#>
#> Accuracy : 0.8103
#> 95% CI : (0.6859, 0.9013)
#> No Information Rate : 0.5
#> P-Value [Acc > NIR] : 1.016e-06
#>
#> Kappa : 0.6207
#>
#> Mcnemar's Test P-Value : 0.5465
#>
#> Sensitivity : 0.8621
#> Specificity : 0.7586
#> Pos Pred Value : 0.7812
#> Neg Pred Value : 0.8462
#> Prevalence : 0.5000
#> Detection Rate : 0.4310
#> Detection Prevalence : 0.5517
#> Balanced Accuracy : 0.8103
#>
#> 'Positive' Class : 0
#>
#>
#> ### Top 10 Important Features ###
#> Feature Gain Cover Frequency
#> <char> <num> <num> <num>
#> 1: TNF.A 0.18457678 0.10535233 0.09103448
#> 2: IL.22 0.15117263 0.16064281 0.09885057
#> 3: IL.12.P70 0.09955532 0.12855158 0.12321839
#> 4: IL.33 0.09384522 0.08514973 0.07908046
#> 5: IL.1B 0.07717208 0.03835908 0.05149425
#> 6: IL.9 0.07554566 0.06955229 0.07080460
#> 7: IL.15 0.04705740 0.05853239 0.03310345
#> 8: IL.23 0.04440949 0.02533791 0.02988506
#> 9: CCL.20.MIP.3A 0.03678675 0.05623800 0.05287356
#> 10: IL.13 0.02785650 0.02633047 0.03678161
#>
#> ### CROSS-VALIDATION USING XGBOOST ###
#>
#> Best iteration from cross-validation:
#> iter train_mlogloss_mean train_mlogloss_std test_mlogloss_mean
#> <num> <num> <num> <num>
#> 1: 45 0.1664957 0.01796909 0.457419
#> test_mlogloss_std
#> <num>
#> 1: 0.08743712
#> Confusion Matrix and Statistics
#>
#> Reference
#> Prediction 0 1
#> 0 57 13
#> 1 13 57
#>
#> Accuracy : 0.8143
#> 95% CI : (0.7398, 0.875)
#> No Information Rate : 0.5
#> P-Value [Acc > NIR] : 1.212e-14
#>
#> Kappa : 0.6286
#>
#> Mcnemar's Test P-Value : 1
#>
#> Sensitivity : 0.8143
#> Specificity : 0.8143
#> Pos Pred Value : 0.8143
#> Neg Pred Value : 0.8143
#> Prevalence : 0.5000
#> Detection Rate : 0.4071
#> Detection Prevalence : 0.5000
#> Balanced Accuracy : 0.8143
#>
#> 'Positive' Class : 0
#>
#>
#> Cross-Validation Accuracy: 0.8142857# Using Random Forest for classification
rf_results <- cyt_rf(data = data_df, group_col = "Group", k_folds = 5,
ntree = 1000, mtry = 4, run_rfcv = TRUE,
plot_roc = TRUE)
#>
#> ### RANDOM FOREST RESULTS ON TRAINING SET ###
#>
#> Call:
#> randomForest(formula = formula_rf, data = train_data, ntree = ntree, mtry = mtry, importance = TRUE)
#> Type of random forest: classification
#> Number of trees: 1000
#> No. of variables tried at each split: 4
#>
#> OOB estimate of error rate: 11.43%
#> Confusion matrix:
#> PreT2D T2D class.error
#> PreT2D 62 8 0.1142857
#> T2D 8 62 0.1142857
#>
#> Accuracy on training set: 0.8857143
#>
#> Class 'PreT2D' metrics:
#> Sensitivity: 0.886
#> Specificity: 0.886
#>
#> Class 'T2D' metrics:
#> Sensitivity: 0.886
#> Specificity: 0.886
#>
#> ### PREDICTIONS ON TEST SET ###
#> Reference
#> Prediction PreT2D T2D
#> PreT2D 25 7
#> T2D 4 22
#>
#> Accuracy on test set: 0.8103448
#>
#> Sensitivity by class:
#> Class: PreT2D: 0.862
#> Class: T2D: 0.241
#>
#> Specificity by class:
#> Class: T2D: 0.759
#> Class: PreT2D: 0.138
#>
#> AUC: 0.9298454
#>
#> ### RANDOM FOREST CROSS-VALIDATION FOR FEATURE SELECTION ###
#> Random Forest CV completed for feature selection.
#> Check the plot for error vs. number of variables.