CytoProfile

Installation

## install BiocManagerif (!requireNamespace("BiocManager",quietly=TRUE)) install.packages("BiocManager")## install mixOmicsBiocManager::install('mixOmics')

# install.packages("devtools")devtools::install_github("saraswatsh/CytoProfile")

install.packages("CytoProfile")

Example

1. Data Loading and set up

# Loading all packages required# Data manipulation and reshapinglibrary(dplyr)# For data filtering, grouping, and summarising.library(tidyr)# For reshaping data (e.g., pivot_longer, pivot_wider).# Plotting and visualizationlibrary(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(pheatmap)# 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 analysislibrary(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 learninglibrary(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 renderinglibrary(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 packagelibrary(CytoProfile)# Loading in datadata("ExampleData1")data_df<-ExampleData1

2. Exploratory Data Analysis

Boxplots

# Generating boxplots to check for outliers for raw valuescyt_bp(data_df[,-c(1:3)],pdf_title=NULL)

# Removing the first 3 columns to retain only continuous variables.# Generating boxplots to check for outliers for log2 valuescyt_bp(data_df[,-c(1:3)],pdf_title=NULL,scale="log2")

# Using log2 transformation for cytokine values.

Group-Specific Boxplots

data_df<-ExampleData1[,-c(3,5:28)]data_df<-dplyr::filter(data_df,Group=="T2D",Treatment=="Unstimulated")# Raw values for group-specific boxplotscyt_bp2(data_df,pdf_title=NULL,scale=NULL)

# Log2-transformed group-specific boxplotscyt_bp2(data_df,pdf_title=NULL,scale="log2")

3. Skewness and Kurtosis

data_df<-ExampleData1# Histogram of skewness and kurtosis for raw datacyt_skku(data_df[,-c(1:3)],pdf_title=NULL,group_cols=NULL)

# Histogram of skewness and kurtosis with grouping (e.g., "Group")cyt_skku(ExampleData1[,-c(2:3)],pdf_title=NULL,group_cols= c("Group"))

4. Error Bar Plots

Basic Error Bar Plot

# Generating basic error bar plotsdata_df<-ExampleData1cyt_errbp(data_df[, c("Group","CCL.20.MIP.3A","IL.10")],group_col="Group",p_lab=FALSE,es_lab=FALSE,class_symbol=FALSE,x_lab="Cytokines",y_lab="Concentrations in log2 scale",log2=TRUE)

Enriched Error Bar Plot with p-values and Effect Sizes

# Generating Error Bar Plot enriched with p-value and effect sizedata_df<-ExampleData1cyt_errbp(data_df[, c("Group","CCL.20.MIP.3A","IL.10")],group_col="Group",p_lab=TRUE,es_lab=TRUE,class_symbol=TRUE,x_lab="Cytokines",y_lab="Concentrations in log2 scale",log2=TRUE)

5. Univariate Analysis

Two Sample T-test and Mann Whitney U Test

# Performing Testdata_df<-ExampleData1[,-c(3)]data_df<-dplyr::filter(data_df,Group!="ND",Treatment!="Unstimulated")# Test examplecyt_ttest(data_df[, c(1:2,5:6)],scale="log2",verbose=TRUE,format_output=TRUE)#> $results#>   Outcome Categorical      Comparison#> 1   IFN.G       Group   PreT2D vs T2D#> 2   IL.10       Group   PreT2D vs T2D#> 3   IFN.G   Treatment CD3/CD28 vs LPS#> 4   IL.10   Treatment CD3/CD28 vs LPS#>                                                Test Estimate Statistic P_value#> 1 Wilcoxon rank sum test with continuity correction   -2.463    1599.0   0.008#> 2 Wilcoxon rank sum test with continuity correction   -0.956    1625.0   0.012#> 3 Wilcoxon rank sum test with continuity correction    9.024    4132.5   0.000#> 4 Wilcoxon rank sum test with continuity correction    1.690    3091.0   0.000

ANOVA Comparisons Test

# Perform ANOVA comparisons test (example with 2 cytokines)data_df<-ExampleData1[,-c(3)]cyt_anova(data_df[, c(1:2,5:6)],format_output=TRUE)#>                        Outcome Categorical            Comparison  P_adj#> PreT2D-ND                IFN.G       Group             PreT2D-ND 0.0883#> T2D-ND                   IFN.G       Group                T2D-ND 0.9779#> T2D-PreT2D               IFN.G       Group            T2D-PreT2D 0.0550#> PreT2D-ND1               IL.10       Group             PreT2D-ND 0.7745#> T2D-ND1                  IL.10       Group                T2D-ND 0.1546#> T2D-PreT2D1              IL.10       Group            T2D-PreT2D 0.0316#> LPS-CD3/CD28             IFN.G   Treatment          LPS-CD3/CD28 0.0000#> Unstimulated-CD3/CD28    IFN.G   Treatment Unstimulated-CD3/CD28 0.0000#> Unstimulated-LPS         IFN.G   Treatment      Unstimulated-LPS 0.9988#> LPS-CD3/CD281            IL.10   Treatment          LPS-CD3/CD28 0.0000#> Unstimulated-CD3/CD281   IL.10   Treatment Unstimulated-CD3/CD28 0.0000#> Unstimulated-LPS1        IL.10   Treatment      Unstimulated-LPS 0.0001

6. Multivariate Analysis

Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

# cyt_plsda function.data<-ExampleData1[,-c(3)]data_df<-dplyr::filter(data,Group!="ND"&Treatment=="CD3/CD28")cyt_splsda(data_df,pdf_title=NULL,colors= c("black","purple"),bg=FALSE,scale="log2",ellipse=TRUE,conf_mat=FALSE,var_num=25,cv_opt="loocv",comp_num=2,pch_values= c(16,4),group_col="Group",group_col2="Treatment",roc=TRUE)

Multivariate INTegration Partial Least Squares Discriminant Analysis (MINT sPLS-DA)

# cyt_mint_plsda function.data_df<-ExampleData5[,-c(2,4)]data_df<-dplyr::filter(data_df,Group!="ND")cyt_mint_splsda(data_df,group_col="Group",batch_col="Batch",colors= c("black","purple"),ellipse=TRUE,var_num=25,comp_num=2,scale="log2",verbose=FALSE)

Principal Component Analysis (PCA)

data<-ExampleData1[,-c(3,23)]data_df<- filter(data,Group!="ND"&Treatment!="Unstimulated")cyt_pca(data_df,pdf_title=NULL,colors= c("black","red2"),scale="log2",comp_num=2,pch_values= c(16,4),group_col="Group")

7. Statistical Visualizations

Volcano Plot

# Generating Volcano Plotdata_df<-ExampleData1[,-c(2:3)]cyt_volc(data_df,group_col="Group",cond1="T2D",cond2="ND",fold_change_thresh=2.0,top_labels=15)#> $`T2D vs ND`

Heatmap

# Generating Heat mapcyt_heatmap(data=data_df,scale="log2",# Optional scalingannotation_col="Group",title=NULL)

Dual Flashlight Plot

# Generating dual flashlights plotdata_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)# Print the plotdfp

# Print the table data used for plottingprint(dfp$data,n=25)#> # 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>

8. Machine Learning Models

Using XGBoost for classification

# Using XGBoost for classificationdata_df0<-ExampleData1data_df<-data.frame(data_df0[,1:3], log2(data_df0[,-c(1:3)]))data_df<-data_df[,-c(2:3)]data_df<-dplyr::filter(data_df,Group!="ND")cyt_xgb(data=data_df,group_col="Group",nrounds=500,max_depth=4,min_split_loss=0,learning_rate=0.05,nfold=5,cv=TRUE,objective="multi:softprob",eval_metric="auc",early_stopping_rounds=NULL,top_n_features=10,verbose=0,plot_roc=TRUE,print_results=FALSE)

Using Random Forest for classification

# Using Random Forest for classificationcyt_rf(data=data_df,group_col="Group",k_folds=5,ntree=1000,mtry=4,run_rfcv=TRUE,plot_roc=TRUE,verbose=FALSE)