Help for package CytoProfile

Title:

Cytokine Profiling Analysis Tool

Version:

0.2.1

Description:

Provides comprehensive cytokine profiling analysis through quality control using biologically meaningful cutoffs on raw cytokine measurements and by testing for distributional symmetry to recommend appropriate transformations. Offers exploratory data analysis with summary statistics, enhanced boxplots, and barplots, along with univariate and multivariate analytical capabilities for in-depth cytokine profiling such as Principal Component Analysis based on Andrzej Maćkiewicz and Waldemar Ratajczak (1993) <doi:10.1016/0098-3004(93)90090-R>, Sparse Partial Least Squares Discriminant Analysis based on Lê Cao K-A, Boitard S, and Besse P (2011) <doi:10.1186/1471-2105-12-253>, Random Forest based on Breiman, L. (2001) <doi:10.1023/A:1010933404324>, and Extreme Gradient Boosting based on Tianqi Chen and Carlos Guestrin (2016) <doi:10.1145/2939672.2939785>.

Encoding:

UTF-8

RoxygenNote:

7.3.2

URL:

https://github.com/saraswatsh/CytoProfile, https://cytoprofile.cytokineprofile.org/

Depends:

R (≥ 4.3)

Imports:

mixOmics, dplyr, tidyr, pROC, plot3D, caret, xgboost, randomForest, gplots, e1071, ggplot2, ggrepel, gridExtra, reshape2

Suggests:

spelling, BiocManager, testthat, knitr, rmarkdown, devtools, Ckmeans.1d.dp, prodlim

NeedsCompilation:

License:

GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]

LazyData:

true

VignetteBuilder:

knitr

BugReports:

https://github.com/saraswatsh/CytoProfile/issues

Language:

en-US

Packaged:

2025-05-19 15:40:28 UTC; shubh

Author:

Shubh Saraswat

[cre, aut, cph], Xiaohua Douglas Zhang

[aut]

Maintainer:

Shubh Saraswat <shubh.saraswat00@gmail.com>

Repository:

CRAN

Date/Publication:

2025-05-19 16:10:02 UTC

CytoProfile: Cytokine Profiling Analysis Tool

Description

Provides comprehensive cytokine profiling analysis through quality control using biologically meaningful cutoffs on raw cytokine measurements and by testing for distributional symmetry to recommend appropriate transformations. Offers exploratory data analysis with summary statistics, enhanced boxplots, and barplots, along with univariate and multivariate analytical capabilities for in-depth cytokine profiling such as Principal Component Analysis based on Andrzej Maćkiewicz and Waldemar Ratajczak (1993) doi:10.1016/0098-3004(93)90090-R, Sparse Partial Least Squares Discriminant Analysis based on Lê Cao K-A, Boitard S, and Besse P (2011) doi:10.1186/1471-2105-12-253, Random Forest based on Breiman, L. (2001) doi:10.1023/A:1010933404324, and Extreme Gradient Boosting based on Tianqi Chen and Carlos Guestrin (2016) doi:10.1145/2939672.2939785.

Author(s)

Maintainer: Shubh Saraswat shubh.saraswat00@gmail.com (ORCID) [copyright holder]

Authors:

Xiaohua Douglas Zhang douglas.zhang@uky.edu (ORCID)

Example Cytokine Profiling Data 1.

Description

Contains observed concentrations of cytokines and their respective treatment and groups, derived from:

Usage

ExampleData1

Format

A data frame with 297 rows and 29 columns:

Group: Group assigned to the subjects.
Treatment: Treatment received by subjects.
Time: Time point of the measurement.
IL.17F: Observed concentration of IL.17F cytokine.
GM.CSF: Observed concentration of GM.CSF cytokine.
IFN.G: Observed concentration of IFN.G cytokine.
IL.10: Observed concentration of IL.10 cytokine.
CCL.20.MIP.3A: Observed concentration of CCL.20.MIP.3A cytokine.
IL.12.P70: Observed concentration of IL.12.P70 cytokine.
IL.13: Observed concentration of IL.13 cytokine.
IL.15: Observed concentration of IL.15 cytokine.
IL.17A: Observed concentration of IL.17A cytokine.
IL.22: Observed concentration of IL.22 cytokine.
IL.9: Observed concentration of IL.9 cytokine.
IL.1B: Observed concentration of IL.1B cytokine.
IL.33: Observed concentration of IL.33 cytokine.
IL.2: Observed concentration of IL.2 cytokine.
IL.21: Observed concentration of IL.21 cytokine.
IL.4: Observed concentration of IL.4 cytokine.
IL.23: Observed concentration of IL.23 cytokine.
IL.5: Observed concentration of IL.5 cytokine.
IL.6: Observed concentration of IL.6 cytokine.
IL.17E.IL.25: Observed concentration of IL.17E.IL.25 cytokine.
IL.27: Observed concentration of IL.27 cytokine.
IL.31: Observed concentration of IL.31 cytokine.
TNF.A: Observed concentration of TNF.A cytokine.
TNF.B: Observed concentration of TNF.B cytokine.
IL.28A: Observed concentration of IL.28A cytokine.

Source

Example data compiled for cytokine profiling.

References

Pugh GH, Fouladvand S, SantaCruz-Calvo S, Agrawal M, Zhang XD, Chen J, Kern PA, Nikolajczyk BS. T cells dominate peripheral inflammation in a cross-sectional analysis of obesity-associated diabetes. Obesity (Silver Spring). 2022;30(10): 1983–1994. doi:10.1002/oby.23528.

Examples

data(ExampleData1)

Example Cytokine Profiling Data 2.

Description

Contains observed concentrations of cytokines and their respective treatment and groups, derived from:

Usage

ExampleData2

Format

A data frame with 66 rows and 20 columns:

Stimulation: Stimulation assigned to the subjects.
Group: Group assigned to the subjects.
IL.17F: Observed concentration of IL.17F cytokine.
GM.CSF: Observed concentration of GM.CSF cytokine.
IFN.G: Observed concentration of IFN.G cytokine.
IL.10: Observed concentration of IL.10 cytokine.
CCL.20: Observed concentration of CCL.20 cytokine.
IL.12: Observed concentration of IL.12 cytokine.
IL.13: Observed concentration of IL.13 cytokine.
IL.17A: Observed concentration of IL.17A cytokine.
IL.22: Observed concentration of IL.22 cytokine.
IL.9: Observed concentration of IL.9 cytokine.
IL.1B: Observed concentration of IL.1B cytokine.
IL.2: Observed concentration of IL.2 cytokine.
IL.21: Observed concentration of IL.21 cytokine.
IL.4: Observed concentration of IL.4 cytokine.
IL.5: Observed concentration of IL.5 cytokine.
IL.6: Observed concentration of IL.6 cytokine.
TNF.A: Observed concentration of TNF.A cytokine.
TNF.B: Observed concentration of TNF.B cytokine.

Source

Example data compiled for cytokine profiling.

References

SantaCruz-Calvo S, Saraswat S, Hasturk H, Dawson DR, Zhang XD, Nikolajczyk BS. Periodontitis and Diabetes Differentially Affect Inflammation in Obesity. J Dent Res. 2024;103(12):1313-1322. doi:10.1177/00220345241280743

Examples

data(ExampleData2)

Example Cytokine Profiling Data 3.

Description

Contains observed concentrations of cytokines and their respective treatment and groups, derived from:

Usage

ExampleData3

Format

A data frame with 64 rows and 14 columns:

Stimulation: Stimulation assigned to the subjects.
Group: Group assigned to the subjects.
GM.CSF: Observed concentration of GM.CSF cytokine.
IFN.G: Observed concentration of IFN.G cytokine.
IL.10: Observed concentration of IL.10 cytokine.
CCL.20.MIP.3A: Observed concentration of CCL.20.MIP.3A cytokine.
IL.12.P70: Observed concentration of IL.12.P70 cytokine.
IL.13: Observed concentration of IL.13 cytokine.
IL.15: Observed concentration of IL.15 cytokine.
IL.9: Observed concentration of IL.9 cytokine.
IL.1B: Observed concentration of IL.1B cytokine.
IL.21: Observed concentration of IL.21 cytokine.
IL.6: Observed concentration of IL.6 cytokine.
TNF.A: Observed concentration of TNF.A cytokine.

Source

Example data compiled for cytokine profiling.

References

Examples

data(ExampleData3)

Example Cytokine Profiling Data 4.

Description

Contains observed concentrations of cytokines and their respective treatment and groups, derived from:

Usage

ExampleData4

Format

A data frame with 64 rows and 14 columns:

Group: Group assigned to the subjects.
Treatment: Treatment received by subjects.
IL.17F: Observed concentration of IL.17F cytokine.
GM.CSF: Observed concentration of GM.CSF cytokine.
IFNg: Observed concentration of IFNg cytokine.
IL.10: Observed concentration of IL.10 cytokine.
CCL.20: Observed concentration of CCL.20 cytokine.
IL.12: Observed concentration of IL.12 cytokine.
IL.13: Observed concentration of IL.13 cytokine.
IL.17A: Observed concentration of IL.17A cytokine.
IL.22: Observed concentration of IL.22 cytokine.
IL.9: Observed concentration of IL.9 cytokine.
IL.2: Observed concentration of IL.2 cytokine.
IL.21: Observed concentration of IL.21 cytokine.
IL.4: Observed concentration of IL.4 cytokine.
IL.23: Observed concentration of IL.23 cytokine.
IL.5: Observed concentration of IL.5 cytokine.
IL.6: Observed concentration of IL.6 cytokine.
TNFa: Observed concentration of TNFa cytokine.
TNFb: Observed concentration of TNFb cytokine.

Source

Example data compiled for cytokine profiling.

References

SantaCruz-Calvo, S., Saraswat, S., Kalantar, G. H., Zukowski, E., Marszalkowski, H., Javidan, A., Gholamrezaeinejad, F., Bharath, L. P., Kern, P. A., Zhang, X. D., & Nikolajczyk, B. S. (2024). A unique inflammaging profile generated by T cells from people with obesity is metformin resistant. GeroScience, 10.1007/s11357-024-01441-4. Advance online publication. https://doi.org/10.1007/s11357-024-01441-4

Examples

data(ExampleData4)

ANOVA Analysis on Continuous Variables.

Description

This function performs an analysis of variance (ANOVA) for each continuous variable against every categorical predictor in the input data. Character columns are automatically converted to factors; all factor columns are used as predictors while numeric columns are used as continuous outcomes. For each valid predictor (i.e., with more than one level and no more than 10 levels), Tukey's Honest Significant Difference (HSD) test is conducted and the adjusted p-values for pairwise comparisons are extracted.

Usage

cyt_anova(data, format_output = FALSE)

Arguments

data

A data frame or matrix containing both categorical and continuous variables. Character columns will be converted to factors and used as predictors, while numeric columns will be used as continuous outcomes.

format_output

Logical. If TRUE, returns the results as a tidy data frame instead of a list. Default is FALSE.

Value

If format_output is FALSE (default), a list of adjusted p-values from Tukey's HSD tests for each combination of continuous outcome and categorical predictor. List elements are named in the format "Outcome_Categorical". If format_output is TRUE, a data frame in a tidy format.

Examples

data("ExampleData1")
cyt_anova(ExampleData1[, c(1:2, 5:6)], format_output = TRUE)

Boxplots for Overall Comparisons by Continuous Variables.

Description

This function creates a PDF file containing box plots for the continuous variables in the provided data. If the number of columns in data exceeds bin.size, the function splits the plots across multiple pages.

Usage

cyt_bp(data, pdf_title, bin_size = 25, y_lim = NULL, scale = NULL)

Arguments

data

A matrix or data frame containing the raw data to be plotted.

pdf_title

A string representing the name of the PDF file to be created. If set to NULL, the box plots are displayed on the current graphics device. Default is NULL.

bin_size

An integer specifying the maximum number of box plots to display on a single page.

y_lim

An optional numeric vector defining the y-axis limits for the plots.

scale

An optional character string. If set to "log2", numeric columns are log2-transformed.

Value

A PDF file containing the box plots for the continuous variables.

Examples

# Loading data
data.df <- ExampleData1
# Generate box plots for log2-transformed values to check for outliers:
cyt_bp(data.df[,-c(1:3)], pdf_title = NULL, scale = "log2")

Boxplot Function Enhanced for Specific Group Comparisons.

Description

This function generates a PDF file containing boxplots for each combination of numeric and factor variables in the provided data. It first converts any character columns to factors and checks that the data contains at least one numeric and one factor column. If the scale argument is set to "log2", all numeric columns are log2-transformed. The function then creates boxplots using ggplot2 for each numeric variable grouped by each factor variable.

Usage

cyt_bp2(data, pdf_title, scale = NULL, y_lim = NULL)

Arguments

data

A matrix or data frame of raw data.

pdf_title

A string representing the title (and filename) of the PDF file. If NULL, the boxplots are displayed on the current graphics device. Defaults to NULL.

scale

Transformation option for continuous variables. Options are NULL (default) and "log2". When set to "log2", numeric columns are transformed using the log2 function.

y_lim

An optional numeric vector defining the y-axis limits for the plots.

Value

A PDF file containing the boxplots.

Examples

# Loading data
data_df <- ExampleData1[, -c(3, 5:28)]
data_df <- dplyr::filter(data_df, Group == "T2D", Treatment == "Unstimulated")
cyt_bp2(data_df, pdf_title = NULL, scale = "log2")

Dual-flashlight Plot.

Description

This function reshapes the input data and computes summary statistics (mean and variance) for each variable grouped by a specified factor column. It then calculates the SSMD (Strictly Standardized Mean Difference) and log2 fold change between two groups (group1 and group2) and categorizes the effect strength as "Strong Effect", "Moderate Effect", or "Weak Effect". A dual flash plot is generated using ggplot2 where the x-axis represents the average log2 fold change and the y-axis represents the SSMD. Additionally, the function prints the computed statistics to the console.

Usage

cyt_dualflashplot(
  data,
  group_var,
  group1,
  group2,
  ssmd_thresh = 1,
  log2fc_thresh = 1,
  top_labels = 15,
  verbose = FALSE
)

Arguments

data

A data frame containing the input data.

group_var

A string specifying the name of the grouping column in the data.

group1

A string representing the name of the first group for comparison.

group2

A string representing the name of the second group for comparison.

ssmd_thresh

A numeric threshold for the SSMD value used to determine significance. Default is 1.

log2fc_thresh

A numeric threshold for the log2 fold change used to determine significance. Default is 1.

top_labels

An integer specifying the number of top variables (based on absolute SSMD) to label in the plot. Default is 15.

verbose

A logical indicating whether to print the computed statistics to the console. Default is FALSE.

Value

A ggplot object representing the dual flash plot for the comparisons between group1 and group2.

Examples

# Loading data
data_df <- ExampleData1[, -c(2:3)]

cyt_dualflashplot(
  data_df,
  group_var = "Group",
  group1 = "T2D",
  group2 = "ND",
  ssmd_thresh = -0.2,
  log2fc_thresh = 1,
  top_labels = 10,
  verbose = FALSE
)

Error-bar Plot.

Description

This function generates an error-bar plot to visually compare different groups against a designated baseline group. It displays the central tendency (mean or median) as a bar and overlays error bars to represent the data's spread (e.g., standard deviation, MAD, or standard error). The plot can also include p-value and effect size labels (based on SSMD), presented either as symbols or numeric values, to highlight significant differences and the magnitude of effects.

Usage

cyt_errbp(
  data,
  group_col = NULL,
  p_lab = FALSE,
  es_lab = FALSE,
  class_symbol = TRUE,
  x_lab = "",
  y_lab = "",
  title = "",
  log2 = FALSE,
  output_file = NULL
)

Arguments

data

A data frame containing the data for each group. It should include at least one numeric column for the measurements and a column specifying the group membership.

group_col

Character. The name of the column in data that specifies the group membership.

p_lab

Logical. If TRUE, p-values are displayed on the plot. Default is FALSE.

es_lab

Logical. If TRUE, effect sizes (SSMD) are displayed on the plot. Default is FALSE.

class_symbol

Logical. If TRUE, significance and effect size are represented using symbolic notation (e.g., *, **, >, <<). If FALSE, numeric values are used. Default is TRUE.

x_lab

Character. Label for the x-axis. If not provided, defaults to the name of the group_col or "Group" if group_col is NULL.

y_lab

Character. Label for the y-axis. If not provided, defaults to "Value".

title

Character. Title of the plot. If not provided, a default title is generated based on the measured variables.

log2

Logical. If TRUE, a log2 transformation (with a +1 offset) is applied to all numeric columns before analysis. Default is FALSE.

output_file

Character. The file path to save the plot as a PDF. If NULL, the plot is displayed but not saved. Default is NULL.

Details

The function performs the following steps:

Optionally applies a log2 transformation to numeric data.
Determines the baseline group (the first level of group_col).
Calculates summary statistics (sample size, mean, standard deviation) for each group and each numeric variable.
Performs t-tests to compare each group against the baseline for each numeric variable.
Computes effect sizes (SSMD) for each group compared to the baseline.
Generates a faceted error-bar plot, with one facet per numeric variable.
Optionally adds p-value and effect size labels to the plot.
Optionally saves the plot as a PDF.

Value

An error-bar plot (a ggplot object) is produced and optionally saved as a PDF. If output_file is specified, the function returns returns the ggplot object.

Examples

data <- ExampleData1

cyt_errbp(data[,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)

Heat Map.

Description

This function creates a heatmap using the numeric columns from the provided data frame. If requested via the scale parameter, the function applies a log2 transformation to the data (with non-positive values replaced by NA). The heatmap is saved as a file, with the format determined by the file extension in title.

Usage

cyt_heatmap(data, scale = NULL, annotation_col_name = NULL, title)

Arguments

data

A data frame containing the input data. Only numeric columns will be used to generate the heatmap.

scale

Character. An optional scaling option. If set to "log2", the numeric data will be log2-transformed (with non-positive values set to NA). Default is NULL.

annotation_col_name

Character. An optional column name from data to be used for generating annotation colors. Default is NULL.

title

Character. The title of the heatmap and the file name for saving the plot. The file extension (".pdf" or ".png") determines the output format. If NULL, the plot is generated on the current graphics device. Default is NULL.

Value

The function does not return a value. It saves the heatmap to a file.

Examples

# Load sample data
data("ExampleData1")
data_df <- ExampleData1
# Generate a heatmap with log2 scaling and annotation based on
# the "Group" column
cyt_heatmap(
  data = data_df[, -c(2:3)],
  scale = "log2",  # Optional scaling
  annotation_col_name = "Group",
  title = NULL
)

Analyze Data with Principal Component Analysis (PCA) for Cytokines.

Description

This function performs Principal Component Analysis (PCA) on cytokine data and generates several types of plots, including:

2D PCA plots using mixOmics' plotIndiv function,
3D scatter plots (if style is "3d" or "3D" and comp_num is 3) via the plot3D package,
Scree plots showing both individual and cumulative explained variance,
Loadings plots, and
Biplots and correlation circle plots.

The function optionally applies a log2 transformation to the numeric data and handles analyses based treatment groups.

Usage

cyt_pca(
  data,
  group_col = NULL,
  group_col2 = NULL,
  colors = NULL,
  pdf_title,
  ellipse = FALSE,
  comp_num = 2,
  scale = NULL,
  pch_values = NULL,
  style = NULL
)

Arguments

data

A data frame containing cytokine data. It should include at least one column representing grouping information and optionally a second column representing treatment or stimulation.

group_col

A string specifying the column name that contains the first group information. If group_col2 is not provided, an overall analysis will be performed.

group_col2

A string specifying the second grouping column. Default is NULL.

colors

A vector of colors corresponding to the groups. If set to NULL, a palette is generated using rainbow() based on the number of unique groups.

pdf_title

A string specifying the file name of the PDF where the PCA plots will be saved. If NULL, the plots are generated on the current graphics device. Default is NULL.

ellipse

Logical. If TRUE, a 95% confidence ellipse is drawn on the PCA individuals plot. Default is FALSE.

comp_num

Numeric. The number of principal components to compute and display. Default is 2.

scale

Character. If set to "log2", a log2 transformation is applied to the numeric cytokine measurements (excluding the grouping columns). Default is NULL.

pch_values

A vector of plotting symbols (pch values) to be used in the PCA plots. Default is NULL.

style

Character. If set to "3d" or "3D" and comp_num equals 3, a 3D scatter plot is generated using the plot3D package. Default is NULL.

Value

A PDF file containing the PCA plots is generated and saved.

Examples

# Load sample data
data <- ExampleData1[, -c(3,23)]
data_df <- dplyr::filter(data, Group != "ND" & Treatment != "Unstimulated")
# Run PCA analysis and save plots to a PDF file
cyt_pca(
  data = data_df,
  pdf_title = NULL,
  colors = c("black", "red2"),
  scale = "log2",
  comp_num = 3,
  pch_values = c(16, 4),
  style = "3D",
  group_col = "Group",
  group_col2 = "Treatment",
  ellipse = FALSE
)

Run Random Forest Classification on Cytokine Data,

Description

This function trains and evaluates a Random Forest classification model on cytokine data. It includes feature importance visualization, cross- validation for feature selection, and performance metrics such as accuracy, sensitivity, and specificity. Optionally, for binary classification, the function also plots the ROC curve and computes the AUC.

Usage

cyt_rf(
  data,
  group_col,
  ntree = 500,
  mtry = 5,
  train_fraction = 0.7,
  plot_roc = FALSE,
  k_folds = 5,
  step = 0.5,
  run_rfcv = TRUE,
  verbose = FALSE,
  seed = 123
)

Arguments

data

A data frame containing the cytokine data, with one column as the grouping variable and the rest as numerical features.

group_col

A string representing the name of the column with the grouping variable (the target variable for classification).

ntree

An integer specifying the number of trees to grow in the forest (default is 500).

mtry

An integer specifying the number of variables randomly selected at each split (default is 5).

train_fraction

A numeric value between 0 and 1 representing the proportion of data to use for training (default is 0.7).

plot_roc

A logical value indicating whether to plot the ROC curve and compute the AUC for binary classification (default is FALSE).

k_folds

An integer specifying the number of folds for cross-validation (default is 5).

step

A numeric value specifying the fraction of variables to remove at each step during cross-validation for feature selection (default is 0.5).

run_rfcv

A logical value indicating whether to run Random Forest cross-validation for feature selection (default is TRUE).

verbose

A logical value indicating whether to print additional informational output to the console. When TRUE, the function will display progress messages, and intermediate results when FALSE (the default), it runs quietly.

seed

An integer specifying the seed for reproducibility (default is 123).

Details

The function fits a Random Forest model to the provided data by splitting it into training and test sets. It calculates performance metrics such as accuracy, sensitivity, and specificity for both sets. For binary classification, it can also plot the ROC curve and compute the AUC. If run_rfcv is TRUE, cross-validation is performed to select the optimal number of features. If verbose is TRUE, the function prints additional information to the console, including training results, test results, and plots.

Value

A list containing:

model

The trained Random Forest model.

confusion_matrix

The confusion matrix of the test set predictions.

importance_plot

A ggplot object showing the variable importance plot based on Mean Decrease Gini.

rfcv_result

Results from Random Forest cross-validation for feature selection (if run_rfcv is TRUE).

importance_data

A data frame containing the variable importance based on the Gini index.

Examples

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 <- dplyr::filter(data.df, Group != "ND")

cyt_rf(
  data = data.df, group_col = "Group", k_folds = 5, ntree = 1000,
  mtry = 4, run_rfcv = TRUE, plot_roc = TRUE, verbose = FALSE
)

Distribution of the Data Set Shown by Skewness and Kurtosis.

Description

This function computes summary statistics — including sample size, mean, standard error, skewness, and kurtosis — for each numeric measurement column in a data set. If grouping columns are provided via group_cols, the function computes the metrics separately for each group defined by the combination of these columns (using the first element as the treatment variable and the second as the grouping variable, or the same column for both if only one is given). When no grouping columns are provided, the entire data set is treated as a single group ("Overall"). A log2 transformation (using a cutoff equal to one-tenth of the smallest positive value in the data) is applied to generate alternative metrics. Histograms showing the distribution of skewness and kurtosis for both raw and log2-transformed data are then generated and saved to a PDF if a file name is provided.

Usage

cyt_skku(
  data,
  group_cols = NULL,
  pdf_title = NULL,
  print_res_raw = FALSE,
  print_res_log = FALSE
)

Arguments

data

A matrix or data frame containing the raw data. If group_cols is specified, the columns with names in group_cols are treated as grouping variables and all other columns are assumed to be numeric measurement variables.

group_cols

A character vector specifying the names of the grouping columns. When provided, the first element is treated as the treatment variable and the second as the group variable. If not provided, the entire data set is treated as one group.

pdf_title

A character string specifying the file name for the PDF file in which the histograms will be saved. If NULL, the histograms are displayed on the current graphics device. Default is NULL.

print_res_raw

Logical. If TRUE, the function returns and prints the computed summary statistics for the raw data. Default is FALSE.

print_res_log

Logical. If TRUE, the function returns and prints the computed summary statistics for the log2-transformed data. Default is FALSE.

Details

A cutoff is computed as one-tenth of the minimum positive value among all numeric measurement columns to avoid taking logarithms of zero. When grouping columns are provided, the function loops over unique grouping columns and computes the metrics for each measurement column within each subgroup. Without grouping columns, the entire data set is analyzed as one overall group.

Value

The function generates histograms of skewness and kurtosis for both raw and log2-transformed data. Additionally, if either printResRaw and/or printResLog is TRUE, the function returns the corresponding summary statistics as a data frame or a list of data frames.

Examples

# Example with grouping columns (e.g., "Group" and "Treatment")
data(ExampleData1)
cyt_skku(ExampleData1[, -c(2:3)], pdf_title = NULL,
  group_cols = c("Group")
)

# Example without grouping columns (analyzes the entire data set)
cyt_skku(ExampleData1[, -c(1:3)], pdf_title = NULL)

Analyze data with Sparse Partial Least Squares Discriminant Analysis (sPLS-DA).

Description

This function conducts Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) on the provided data. It uses the specified group_col (and optionally group_col2) to define class labels while assuming the remaining columns contain continuous variables. The function supports a log2 transformation via the scale parameter and generates a series of plots, including classification plots, scree plots, loadings plots, and VIP score plots. Optionally, ROC curves are produced when roc is TRUE. Additionally, cross-validation is supported via LOOCV or Mfold methods. When both group_col and group_col2 are provided and differ, the function analyzes each treatment level separately.

Usage

cyt_splsda(
  data,
  group_col = NULL,
  group_col2 = NULL,
  colors = NULL,
  pdf_title,
  ellipse = FALSE,
  bg = FALSE,
  conf_mat = FALSE,
  var_num,
  cv_opt = NULL,
  fold_num = 5,
  scale = NULL,
  comp_num = 2,
  pch_values,
  style = NULL,
  roc = FALSE,
  verbose = FALSE,
  seed = 123
)

Arguments

data

A matrix or data frame containing the variables. Columns not specified by group_col or group_col2 are assumed to be continuous variables for analysis.

group_col

A string specifying the column name that contains the first group information. If group_col2 is not provided, an overall analysis will be performed.

group_col2

A string specifying the second grouping column. Default is NULL.

colors

A vector of colors for the groups or treatments. If NULL, a random palette (using rainbow) is generated based on the number of groups.

pdf_title

A string specifying the file name for saving the PDF output. Default is NULL which generates figures in the current graphics device.

ellipse

Logical. Whether to draw a 95\ figures. Default is FALSE.

bg

Logical. Whether to draw the prediction background in the figures. Default is FALSE.

conf_mat

Logical. Whether to print the confusion matrix for the classifications. Default is FALSE.

var_num

Numeric. The number of variables to be used in the PLS-DA model.

cv_opt

Character. Option for cross-validation method: either "loocv" or "Mfold". Default is NULL.

fold_num

Numeric. The number of folds to use if cv_opt is "Mfold". Default is 5.

scale

Character. Option for data transformation; if set to "log2", a log2 transformation is applied to the continuous variables. Default is NULL.

comp_num

Numeric. The number of components to calculate in the sPLS-DA model. Default is 2.

pch_values

A vector of integers specifying the plotting characters (pch values) to be used in the plots.

style

Character. If set to "3D" or "3d" and comp_num equals 3, a 3D plot is generated using the plot3D package. Default is NULL.

roc

Logical. Whether to compute and plot the ROC curve for the model. Default is FALSE.

verbose

seed

An integer specifying the seed for reproducibility (default is 123).

Details

When verbose is set to TRUE, additional diagnostic plots (e.g., VIP plots, ROC Plots, Cross-Validation Plots) are printed to the console. These plots provide extra insight into the model's performance but can be suppressed by keeping verbose = FALSE.

Value

Plots consisting of the classification figures, component figures with Variable of Importance in Projection (VIP) scores, and classifications based on VIP scores greater than 1. ROC curves and confusion matrices are also produced if requested.

Examples

# Loading Sample Data
data_df <- ExampleData1[,-c(3)]
data_df <- dplyr::filter(data_df, Group != "ND", Treatment != "Unstimulated")

cyt_splsda(data_df, pdf_title = NULL,
colors = c("black", "purple"), bg = FALSE, scale = "log2",
conf_mat = FALSE, var_num = 25, cv_opt = NULL, comp_num = 2,
pch_values = c(16, 4), style = NULL, ellipse = TRUE,
group_col = "Group", group_col2 = "Treatment", roc = FALSE, verbose = FALSE)

Two Sample T-test Comparisons.

Description

This function performs pairwise comparisons between two groups for each combination of a categorical predictor (with exactly two levels) and a continuous outcome variable. It first converts any character variables in data to factors and, if specified, applies a log2 transformation to the continuous variables. Depending on the value of scale, the function conducts either a two-sample t-test (if scale = "log2") or a Mann-Whitney U test (if scale is NULL). The resulting p-values are printed and returned.

Usage

cyt_ttest(data, scale = NULL, verbose = TRUE, format_output = FALSE)

Arguments

data

A matrix or data frame containing continuous and categorical variables.

scale

A character specifying a transformation for continuous variables. Options are NULL (default) and "log2". When scale = "log2", a log2 transformation is applied and a two-sample t-test is used; when scale is NULL, a Mann-Whitney U test is performed.

verbose

A logical indicating whether to print the p-values of the statistical tests. Default is TRUE.

format_output

Logical. If TRUE, returns the results as a tidy data frame. Default is FALSE.

Value

If format_output is FALSE, returns a list of p-values (named by Outcome and Categorical variable). If TRUE, returns a data frame in a tidy format.

Examples

data_df <- ExampleData1[, -c(3)]
data_df <- dplyr::filter(data_df, Group != "ND", Treatment != "Unstimulated")
# Test example
cyt_ttest(
 data_df[, c(1:2, 5:6)],
 scale = "log2",
 verbose = TRUE,
 format_output = TRUE
)

Volcano Plot.

Description

This function subsets the numeric columns from the input data and compares them based on a selected grouping column. It computes the fold changes (as the ratio of means) and associated p-values (using two-sample t-tests) for each numeric variable between two groups. The results are log2-transformed (for fold change) and -log10-transformed (for p-values) to generate a volcano plot.

Usage

cyt_volc(
  data,
  group_col,
  cond1 = NULL,
  cond2 = NULL,
  fold_change_thresh = 2,
  p_value_thresh = 0.05,
  top_labels = 10,
  verbose = FALSE
)

Arguments

data

A matrix or data frame containing the data to be analyzed.

group_col

A character string specifying the column name used for comparisons (e.g., group, treatment, or stimulation).

cond1

A character string specifying the name of the first condition for comparison. Default is NULL.

cond2

A character string specifying the name of the second condition for comparison. Default is NULL.

fold_change_thresh

A numeric threshold for the fold change. Default is 2.

p_value_thresh

A numeric threshold for the p-value. Default is 0.05.

top_labels

An integer specifying the number of top variables to label on the plot. Default is 10.

verbose

A logical indicating whether to print the computed statistics to the console. Default is FALSE.

Value

A list of volcano plots (as ggplot objects) for each pairwise comparison. Additionally, the function prints the data frame used for plotting (excluding the significance column) from the final comparison.

Note

If cond1 and cond2 are not provided, the function automatically generates all possible pairwise combinations of groups from the specified group_col for comparisons.

Examples

# Loading data
data_df <- ExampleData1[,-c(2:3)]

volc_plot <- cyt_volc(data_df, "Group", cond1 = "T2D", cond2 = "ND",
fold_change_thresh = 2.0, top_labels= 15)
print(volc_plot$`T2D vs ND`)

Run XGBoost Classification on Cytokine Data.

Description

This function trains and evaluates an XGBoost classification model on cytokine data. It allows for hyperparameter tuning, cross-validation, and visualizes feature importance.

Usage

cyt_xgb(
  data,
  group_col,
  train_fraction = 0.7,
  nrounds = 500,
  max_depth = 6,
  eta = 0.1,
  nfold = 5,
  cv = FALSE,
  objective = "multi:softprob",
  early_stopping_rounds = NULL,
  eval_metric = "mlogloss",
  gamma = 0,
  colsample_bytree = 1,
  subsample = 1,
  min_child_weight = 1,
  top_n_features = 10,
  verbose = 1,
  plot_roc = FALSE,
  print_results = FALSE,
  seed = 123
)

Arguments

data

A data frame containing the cytokine data, with one column as the grouping variable and the rest as numerical features.

group_col

A string representing the name of the column with the grouping variable (i.e., the target variable for classification).

train_fraction

A numeric value between 0 and 1 representing the proportion of data to use for training (default is 0.7).

nrounds

An integer specifying the number of boosting rounds (default is 500).

max_depth

An integer specifying the maximum depth of the trees (default is 6).

eta

A numeric value representing the learning rate (default is 0.1).

nfold

An integer specifying the number of folds for cross-validation (default is 5).

cv

A logical value indicating whether to perform cross-validation (default is FALSE).

objective

A string specifying the XGBoost objective function (default is "multi:softprob" for multi-class classification).

early_stopping_rounds

An integer specifying the number of rounds with no improvement to stop training early (default is NULL).

eval_metric

A string specifying the evaluation metric (default is "mlogloss").

gamma

A numeric value for the minimum loss reduction required to make a further partition (default is 0).

colsample_bytree

A numeric value specifying the subsample ratio of columns when constructing each tree (default is 1).

subsample

A numeric value specifying the subsample ratio of the training instances (default is 1).

min_child_weight

A numeric value specifying the minimum sum of instance weight needed in a child (default is 1).

top_n_features

An integer specifying the number of top features to display in the importance plot (default is 10).

verbose

An integer specifying the verbosity of the training process (default is 1).

plot_roc

A logical value indicating whether to plot the ROC curve and calculate the AUC for binary classification (default is FALSE).

print_results

A logical value indicating whether to print the results of the model training and evaluation (default is FALSE). If set to TRUE, it will print the confusion matrix, and feature importance.

seed

An integer specifying the seed for reproducibility (default is 123).

Details

The function allows for training an XGBoost model on cytokine data, splitting the data into training and test sets. If cross-validation is enabled (cv = TRUE), it performs k-fold cross-validation and prints the best iteration based on the evaluation metric. The function also visualizes the top N important features using xgb.ggplot.importance().

Value

A list containing:

model

The trained XGBoost model.

confusion_matrix

The confusion matrix of the test set predictions.

importance

The feature importance matrix for the top features.

class_mapping

A named vector showing the mapping from class labels to numeric values used for training.

cv_results

Cross-validation results, if cross-validation was performed (otherwise NULL).

plot

A ggplot object showing the feature importance plot.

Examples

# Example usage:
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 <- dplyr::filter(data_df, Group != "ND")

cyt_xgb(
  data = data_df, group_col = "Group",
  nrounds = 500, max_depth = 4, eta = 0.05,
  nfold = 5, cv = FALSE, eval_metric = "mlogloss",
  early_stopping_rounds = NULL, top_n_features = 10,
  verbose = 0, plot_roc = TRUE, print_results = FALSE
)

CytoProfile: Cytokine Profiling Analysis Tool

Description

Author(s)

See Also

Example Cytokine Profiling Data 1.

Description

Usage

Format

Source

References

Examples

Example Cytokine Profiling Data 2.

Description

Usage

Format

Source

References

Examples

Example Cytokine Profiling Data 3.

Description

Usage

Format

Source

References

Examples

Example Cytokine Profiling Data 4.

Description

Usage

Format

Source

References

Examples

ANOVA Analysis on Continuous Variables.

Description

Usage

Arguments

Value

Examples

Boxplots for Overall Comparisons by Continuous Variables.

Description

Usage

Arguments

Value

Examples

Boxplot Function Enhanced for Specific Group Comparisons.

Description

Usage

Arguments

Value

Examples

Dual-flashlight Plot.

Description

Usage

Arguments

Value

Examples

Error-bar Plot.

Description

Usage

Arguments

Details

Value

Examples

Heat Map.

Description

Usage

Arguments

Value

Examples

Analyze Data with Principal Component Analysis (PCA) for Cytokines.

Description

Usage

Arguments

Value

Examples

Run Random Forest Classification on Cytokine Data,

Description

Usage

Arguments

Details