Title:	Omics Data Process Toolbox
Version:	1.1.7
Date:	2025-12-16
Description:	Processing and analyzing omics data from genomics, transcriptomics, proteomics, and metabolomics platforms. It provides functions for preprocessing, normalization, visualization, and statistical analysis, as well as machine learning algorithms for predictive modeling. 'omicsTools' is an essential tool for researchers working with high-throughput omics data in fields such as biology, bioinformatics, and medicine.The QC-RLSC (quality control–based robust LOESS signal correction) algorithm is used for normalization. Dunn et al. (2011) <doi:10.1038/nprot.2011.335>.
License:	AGPL (≥ 3)
Depends:	R (≥ 4.1.0)
Imports:	bs4Dash, cli, config (≥ 0.3.1), dbscan, dplyr, DT, forcats, ggplot2, ggpubr, ggrepel, ggsci, ggvenn, golem (≥ 0.3.5), janitor, magrittr, matrixStats, grDevices, methods, moments, outliers, pheatmap, purrr, RColorBrewer, readxl, readr, rlang, stats, shiny (≥ 1.7.2), shinycssloaders, shinyWidgets, stringr, tibble, tidyr, utils, viridis, tools
Encoding:	UTF-8
RoxygenNote:	7.3.2
Suggests:	Biobase, pvca, testthat (≥ 3.0.0), knitr, rmarkdown, Boruta, randomForest, caret, recipes, parsnip, workflows, rsample, tune, dials, doParallel, vip, GOplot, openxlsx, UpSetR, zip
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-12-16 15:10:24 UTC; Bach
Author:	Yaoxiang Li [cre, aut]
Maintainer:	Yaoxiang Li <liyaoxiang@outlook.com>
Repository:	CRAN
Date/Publication:	2025-12-16 15:40:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

OmicsData Class

Description

A class to represent and analyze high-dimensional omics data.

Slots

data

The data frame containing the generated data with anomalies.

anomaly_measures

The data frame containing the calculated anomaly measures.

thresholds

The list of thresholds for anomaly detection.

Calculate Cook's Distance

Description

Calculate Cook's Distance

Usage

calculate_cooks_distance(values)

Arguments

Value

A numeric vector of Cook's Distance values.

Examples

values <- rnorm(100)
calculate_cooks_distance(values)

Calculate Local Outlier Factor (LOF)

Description

Calculate Local Outlier Factor (LOF)

Usage

calculate_lof(values, k = 5)

Arguments

Value

A numeric vector of LOF values.

Examples

values <- rnorm(100)
calculate_lof(values)

Calculate Measures for Each Feature

Description

Calculate Measures for Each Feature

Usage

calculate_measures(object)

## S4 method for signature 'OmicsData'
calculate_measures(object)

Arguments

Value

The OmicsData object with calculated measures for each feature.

Examples

omics_data <- createOmicsData()
omics_data <- calculate_measures(omics_data)

Calculate QC Statistics and RSD

Description

This function calculates the mean, standard deviation, and coefficient of variation for QC data, optionally ignoring NA values. It also calculates the relative standard deviation (RSD) for both Pooled QC and NIST samples. The input data should be a tibble where the first column is the sample ID and all other columns are features.

Usage

calculate_qc_rsd(
  data,
  qc_string = "Pooled QC|Pooled_QC",
  nist_string = "NIST",
  id_col = "sample_id",
  ignore_na = TRUE
)

Arguments

Value

A list containing tibbles with calculated statistics for Pooled QC and NIST samples. If no matching samples are found, returns NULL for that category.

Author(s)

Yaoxiang Li

Examples

# Generate example data
example_data <- tibble::tibble(
  sample_id = c("Pooled QC 1", "Pooled QC 2", "NIST 1", "NIST 2", "Sample 1"),
  feature1 = c(10, 15, 20, NA, 5),
  feature2 = c(20, NA, 25, 30, 10),
  feature3 = c(NA, 25, 30, 35, 15)
)

# Calculate QC statistics and RSD
rsd_stats <- calculate_qc_rsd(example_data)

# Access results
pooled_qc_rsd <- rsd_stats$Pooled_QC_RSD
nist_rsd <- rsd_stats$NIST_RSD

Check and Sort Columns, Compare Values

Description

This function checks if two tibbles have the same column names, sorts the columns of one tibble to match the order of the other, and then checks if all values in both tibbles are the same.

Usage

check_and_sort_columns(area_data, area_txt)

Arguments

Value

Prints messages indicating whether the tibbles have the same column names and whether all values are the same.

Author(s)

Yaoxiang Li

Examples

## Not run: 
area_data <- read.delim("path/to/All_txt.txt", check.names = FALSE)
area_txt <- read.delim("path/to/area.txt", check.names = FALSE)
check_and_sort_columns(area_data, area_txt)

## End(Not run)

Check Match

Description

This function checks if sample IDs and column names match between area data and internal standard area data.

Usage

check_match(area_data, istd_area_data)

Arguments

Value

A logical value indicating if the sample IDs and column names match.

Examples

area_data <- data.frame(sample_id = c("S1", "S2", "S3"), A = 1:3, B = 4:6)
istd_area_data <- data.frame(sample_id = c("S1", "S2", "S3"), A = 1:3, B = 4:6)
check_match(area_data, istd_area_data)

Combine Multiple Logical Tibbles with Intersection or Union

Description

Combine Multiple Logical Tibbles with Intersection or Union

Usage

combine_logical_tibbles(..., method = c("intersection", "union"))

Arguments

Value

A combined tibble where each cell is TRUE based on the method: - "intersection": TRUE if all corresponding cells in the input tibbles are TRUE, otherwise FALSE. - "union": TRUE if at least one corresponding cell in the input tibbles is TRUE, otherwise FALSE.

Author(s)

Yaoxiang Li

Examples

tibble1 <- tibble::tibble(id = 1:3, A = c(TRUE, FALSE, NA), B = c(TRUE, TRUE, FALSE))
tibble2 <- tibble::tibble(id = 1:3, A = c(TRUE, TRUE, TRUE), B = c(FALSE, TRUE, TRUE))
tibble3 <- tibble::tibble(id = 1:3, A = c(TRUE, FALSE, TRUE), B = c(TRUE, TRUE, NA))
combine_logical_tibbles(tibble1, tibble2, tibble3, method = "intersection")
combine_logical_tibbles(tibble1, tibble2, tibble3, method = "union")

Convert MRM Data to Wide Format

Description

This function converts a tibble containing MRM data to a wide format based on the specified response column.

Usage

convert_mrm_data(
  data,
  response_col,
  sample_name_col = "data_filename",
  sample_id_col = "sample_id",
  component_name_col = "component_name"
)

Arguments

Value

A tibble in wide format with samples as rows and compounds as columns.

Author(s)

Yaoxiang Li

Examples

## Not run: 
all_txt <- tibble::tribble(
  ~`data_filename`, ~`sample_id`, ~`component_name`, ~Area, ~`IS Area`,
  "Sample1", "ID1", "Compound1", 100, 50,
  "Sample1", "ID2", "Compound2", 200, 75,
  "Sample2", "ID1", "Compound1", 150, 60,
  "Sample2", "ID2", "Compound2", 250, 80
)
area_data <- convert_mrm_data(all_txt, "Area", "data_filename", "sample_id", "component_name")
is_area_data <- convert_mrm_data(all_txt, "IS Area", "data_filename", "sample_id", "component_name")
print(area_data)
print(is_area_data)

## End(Not run)

Convert to Binary Matrix for UpSetR

Description

Convert to Binary Matrix for UpSetR

Usage

convert_to_binary_matrix(upset_data)

## S4 method for signature 'data.frame'
convert_to_binary_matrix(upset_data)

Arguments

Value

A binary matrix for UpSetR.

Examples

omics_data <- createOmicsData()
omics_data <- calculate_measures(omics_data)
omics_data <- flag_anomalies(omics_data)
upset_data <- prepare_upset_data(omics_data)
upset_matrix <- convert_to_binary_matrix(upset_data)

Constructor for OmicsData

Description

Constructor for OmicsData

Usage

createOmicsData(data = NULL, n_samples = 100, n_features = 2000)

Arguments

Value

An OmicsData object.

Examples

omics_data <- createOmicsData()
user_data <- generate_data_with_anomalies()
omics_data <- createOmicsData(data = user_data)

Define Anomaly Thresholds

Description

Define Anomaly Thresholds

Usage

define_thresholds(
  skewness = 2.5,
  kurtosis = 10,
  shapiro_p = 1e-10,
  cooks_distance = 1,
  lof = 15
)

Arguments

Value

A list of thresholds for anomaly detection.

Examples

thresholds <- define_thresholds()
thresholds <- define_thresholds(skewness = 3, kurtosis = 8)

Detect Duplicate MRM Transitions

Description

This function adds a column 'MRM_Duplicate_Flag' to the tibble, indicating if a row is a duplicate based on the criteria: same polarity, less than a 1-minute retention time difference, and the same MRM transition (Q1/Q3).

Usage

detect_duplicates(
  data,
  polarity_col = "polarity",
  retention_time_col = "retention_time",
  mass_info_col = "Mass Info",
  component_name_col = "component_name"
)

Arguments

Value

The original tibble with an added 'MRM_Duplicate_Flag' column.

Author(s)

Yaoxiang Li

Examples

## Not run: 
sample_data <- tibble::tribble(
  ~polarity, ~`retention_time`, ~`Mass Info`, ~`component_name`,
  "Positive", 1.95, "61.0 / 44.0", "Urea_pos",
  "Positive", 8.34, "206.0 / 189.0", "Lipoamide_pos",
  "Positive", 2.18, "339.1 / 110.0", "AICAR_pos",
  "Positive", 1.76, "175.1 / 70.0", "Arginine_pos",
  "Positive", 1.75, "176.2 / 159.1", "Citrulline_pos",
  "Positive", 8.90, "198.0 / 181.0", "Dopa_pos",
  "Positive", 2.06, "132.1 / 86.0", "Isoleucine_pos",
  "Positive", 1.92, "132.1 / 43.1", "Leucine_pos",
  "Positive", 1.76, "150.1 / 133.0", "Methionine_pos",
  "Positive", 7.79, "166.1 / 103.0", "Phenylalanine_pos"
)
detect_duplicates(sample_data, "polarity", "retention_time", "Mass Info", "component_name")

## End(Not run)

Ensure There Are Enough Sets for UpSet Plot

Description

Ensure There Are Enough Sets for UpSet Plot

Usage

ensure_enough_sets_for_upset(upset_matrix)

## S4 method for signature 'data.frame'
ensure_enough_sets_for_upset(upset_matrix)

Arguments

Examples

omics_data <- createOmicsData()
omics_data <- calculate_measures(omics_data)
omics_data <- flag_anomalies(omics_data)
upset_data <- prepare_upset_data(omics_data)
upset_matrix <- convert_to_binary_matrix(upset_data)
ensure_enough_sets_for_upset(upset_matrix)

Flag Anomalies

Description

Flag Anomalies

Usage

flag_anomalies(object)

## S4 method for signature 'OmicsData'
flag_anomalies(object)

Arguments

Value

The OmicsData object with flagged anomalies.

Examples

omics_data <- createOmicsData()
omics_data <- calculate_measures(omics_data)
omics_data <- flag_anomalies(omics_data)

Flag Underexpressed Features in Samples Based on Blank Samples

Description

Flags features in samples based on their abundance in blank samples. If a feature is NA in the first blank sample, all samples for this feature are marked as TRUE. Otherwise, for each sample and feature, if the peak area is at least 10 times the area of the first blank sample, it is marked as TRUE, else FALSE. NA values in the samples remain unchanged.

Usage

flag_underexpressed_features(
  data,
  sample_id_col = "sample_id",
  feature_cols,
  threshold = 10
)

Arguments

Value

A tibble with the same dimensions and column names as the input data, containing TRUE, FALSE, or NA based on the criteria.

Author(s)

Yaoxiang Li

Examples

## Not run: 
area_data <- tibble::tibble(
  sample_id = c(
    "011_Blank", "012_sample_002", "013_NIST_Plasma", "014_Blank",
    "015_sample_006", "016_sample_003"
  ),
  `2-Deoxyglucose-6-Phosphate_neg` = c(NA, 345423.96, NA, NA, 125889.80, 323818.25),
  `2-Oxoisoleucine_neg` = c(NA, 53004.06, 124669.80, NA, 23650.90, 118364.36),
  `3-(4-Hydroxyphenyl)propionate_neg` = c(NA, 53004.06, 124669.80, NA, 23650.90, 118364.36)
)
flagged_data <- flag_underexpressed_features(
  area_data,
  sample_id_col = "sample_id",
  feature_cols = names(area_data)[-1]
)
print(flagged_data)

## End(Not run)

Generate High-Dimensional Data with Anomalies

Description

Generate High-Dimensional Data with Anomalies

Usage

generate_data_with_anomalies(n_samples = 100, n_features = 2000)

Arguments

Value

A data frame containing the generated data with anomalies.

Examples

data <- generate_data_with_anomalies()

Generate Process Report for Sciex 7500/5500 Raw Data

Description

This function generates a comprehensive process report for Sciex 7500/5500 raw data, including data normalization, missing value imputation, and optional normalization and flagging steps. The results are saved in a temporary directory and then zipped into a file for easy sharing.

Usage

generate_process_report(
  input_file,
  output_file = NULL,
  filter_blank = TRUE,
  blank_string = "Blank|BLANK|blank",
  filter_nist = TRUE,
  nist_string = "NIST|Nist|nist",
  imputation_threshold = 0.25,
  imputation_method = "half_min",
  qc_string = "QC",
  include_is_normalization = TRUE,
  include_qc_rlsc = TRUE,
  include_pqn = TRUE,
  include_qc_rsd = TRUE,
  include_snr_flag = TRUE,
  snr_threshold = 10,
  include_area_flag = TRUE,
  include_height_flag = TRUE,
  id_col = "sample_id",
  ignore_na = TRUE
)

Arguments

Value

The path to the generated zip file containing the process report.

Author(s)

Yaoxiang Li

Handle Missing Values in a Tibble

Description

This function filters features based on a missing value threshold and imputes missing values using various methods. Metadata columns are specified by the user and are exempt from filtering and imputation.

Usage

handle_missing_values(
  data,
  threshold = 0.2,
  imputation_method = "half_min",
  metadata_cols = NULL
)

Arguments

Value

A tibble with filtered features and imputed missing values.

Author(s)

Yaoxiang Li

Examples

data <- tibble::tibble(
  Feature1 = c(1, 2, NA, 4, 5),
  Feature2 = c(NA, 2, 3, 4, NA),
  Feature3 = c(1, NA, 3, NA, 5),
  Metadata = c("A", "B", "C", "D", "E")
)
imputed_data <- handle_missing_values(
  data,
  threshold = 0.20,
  imputation_method = "half_min",
  metadata_cols = "Metadata"
)
print(imputed_data)

Initialize Results Data Frame

Description

Initialize Results Data Frame

Usage

initialize_results_df(data)

Arguments

Value

A data frame initialized with columns for anomaly measures.

Examples

data <- generate_data_with_anomalies()
anomaly_measures <- initialize_results_df(data)

Internal Standard Normalize

Description

This function performs internal standard normalization.

Usage

internal_standard_normalize(area_data, istd_area_data)

Arguments

Value

A data frame with normalized data.

Examples

area_data <- data.frame(sample_id = c("S1", "S2", "S3"), A = 1:3, B = 4:6)
istd_area_data <- data.frame(sample_id = c("S1", "S2", "S3"), A = 1:3, B = 4:6)
normalized_data <- internal_standard_normalize(area_data, istd_area_data)

Load and Parse SCIEX OS Exported LC-MRM-MS2 Data

Description

Load and Parse SCIEX OS Exported LC-MRM-MS2 Data

Usage

load_parse_sciex_txt(
  file_path,
  input_data = NULL,
  return_all_columns = TRUE,
  check_negative_values = TRUE,
  fix_istds = TRUE
)

Arguments

Value

tibble with the fields appropriately renamed.

Author(s)

Yaoxiang Li

Examples

## Not run: 
data(sciex_mrm_ms_data)
data_tibble <- load_parse_sciex_txt(
  file_path = "path/to/file.txt",
  return_all_columns = FALSE,
  check_negative_values = TRUE
)

## End(Not run)

MS1 Annotation

Description

MS1 Annotation

Usage

ms1_annotation(
  AnnotaData,
  masstole = 0.05,
  toleUnit = 1,
  annotaDB = "metlin",
  ionMode = "pos",
  adducts = NULL
)

Arguments

Value

An annotation result object (typically a data frame) for the provided m/z list.

Perform Principal Variance Component Analysis for Batch Effect Assessment

Description

This function performs Principal Variance Component Analysis to assess batch effects in the dataset.

Usage

perform_batch_assessment(data_matrix, sample_info, batch_effects, threshold)

Arguments

Value

A PVCA object containing the results of the batch effect assessment.

Examples

# Example data
set.seed(123)
data_matrix <- data.frame(
  sample1 = rnorm(100),
  sample2 = rnorm(100),
  sample3 = rnorm(100),
  sample4 = rnorm(100),
  sample5 = rnorm(100),
  sample6 = rnorm(100),
  sample7 = rnorm(100),
  sample8 = rnorm(100),
  sample9 = rnorm(100),
  sample10 = rnorm(100)
)
rownames(data_matrix) <- paste0("feature", 1:100)

sample_info <- data.frame(
  dose = c(0, 1, 2, 1, 0, 2, 1, 0, 2, 1),
  time = c(-1, 1, 25, -1, 1, 25, -1, 1, 25, -1),
  batch = rep(c("A", "B", "C"), length.out = 10)
)
rownames(sample_info) <- colnames(data_matrix)

# Perform Batch Effect Assessment
if (requireNamespace("Biobase", quietly = TRUE) &&
    requireNamespace("pvca", quietly = TRUE)) {
  pvca_results <- perform_batch_assessment(
    data_matrix,
    sample_info,
    c("batch", "dose", "time"),
    0.6
  )
}

Perform Feature Selection

Description

This function performs feature selection using various methods such as LASSO, Elastic Net, Ridge regression, and Boruta. It outputs selected features and variable importance plots.

Usage

perform_feature_selection(
  group_info,
  features,
  id_col_group,
  id_col_features,
  group_col,
  outlier_col = NULL,
  outlier_vals = c("No"),
  group_vals = c("No", "Yes"),
  method = c("lasso", "elastic_net", "ridge", "boruta"),
  mixture = 0.5,
  penalty_vals = 50,
  seed = 1234,
  output_dir = "output"
)

Arguments

Value

A tibble containing selected features and variable importances.

Plot PVCA results (pie chart)

Description

Plot PVCA results (pie chart)

Usage

pieDraw(pvcaobj)

Arguments

Value

A ggplot object (invisibly).

Plot Distribution Measures

Description

Plot Distribution Measures

Usage

plot_distribution_measures(object)

## S4 method for signature 'OmicsData'
plot_distribution_measures(object)

Arguments

Examples

omics_data <- createOmicsData()
omics_data <- calculate_measures(omics_data)
omics_data <- flag_anomalies(omics_data)
plot_distribution_measures(omics_data)

Plot and Analyze Lipid Class Data

Description

This function loads a specified data file containing lipid measurements, calculates the summary of missing values and relative standard deviation (RSD) for each lipid class, and generates plots for both summaries. It also plots and saves figures showing the distribution of internal standards across samples.

Usage

plot_lipid_data_summary(
  file_path,
  output_xlsx = "lipid_classes_with_names.xlsx",
  missing_plot_path = "missing_values_plot.png",
  rsd_plot_path = "rsd_values_plot.png",
  is_plots_dir = "is_plots",
  blank_pattern = "Blank|Control|Neg",
  pooled_pattern = "Pooled QC|Pooled|Pool|PQ",
  nist_pattern = "NIST Plasma|NIST|nist"
)

Arguments

Value

Plots and saves summary figures of missing percentages, RSD percentages, and internal standard distributions.

Author(s)

Yaoxiang Li

Plot and Analyze Metabolomics Data Summary

Description

This function loads a specified data file containing metabolomics measurements, identifies internal standards based on a naming convention, and generates plots for the distribution of internal standards across samples. It also saves the internal standard information in an Excel file.

Usage

plot_met_data_summary(
  file_path,
  output_xlsx = "internal_standards.xlsx",
  is_plots_dir = "is_plots",
  blank_pattern = "Blank|Control|Neg",
  pooled_pattern = "Pooled QC|Pooled|Pool|PQ",
  nist_pattern = "NIST Plasma|NIST|nist",
  other_special_pattern = "Special Case|Extra Sample|Other QC"
)

Arguments

Value

Saves internal standard plots and an Excel file with the identified internal standards.

Author(s)

Yaoxiang Li

Plot Sample Measures

Description

Plot Sample Measures

Usage

plot_sample_measures(object)

## S4 method for signature 'OmicsData'
plot_sample_measures(object)

Arguments

Examples

omics_data <- createOmicsData()
omics_data <- calculate_measures(omics_data)
omics_data <- flag_anomalies(omics_data)
plot_sample_measures(omics_data)

Perform Probabilistic Quotient Normalization for intensities

Description

Perform Probabilistic Quotient Normalization (PQN) for sample intensities. The PQN method determines a dilution factor for each sample by comparing the distribution of quotients between samples and a reference spectrum, followed by sample normalization using this dilution factor. The reference spectrum in this method is the median spectrum of all samples.

Usage

pqn_normalize(data)

Arguments

Value

A data frame with the first column as the sample identifiers and the rest of the columns containing the normalized peak intensities.

Author(s)

Yaoxiang Li

References

Dieterle, F., Ross, A., Schlotterbeck, G., & Senn, H. (2006). Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Analytical chemistry, 78(13), 4281-4290.

Examples

# Load the CSV data
data_file <- system.file("extdata", "example2.csv", package = "omicsTools")
data <- readr::read_csv(data_file)

# Display the first few rows of the original data
print(head(data))

# Apply the pqn_normalize function
normalized_data <- pqn_normalize(data)

# Display the first few rows of the normalized data
print(head(normalized_data))


# Write the normalized data to a new CSV file
readr::write_csv(normalized_data, paste0(tempdir(), "/normalized_data.csv"))

Prepare Data for UpSet Plot

Description

Prepare Data for UpSet Plot

Usage

prepare_upset_data(object)

## S4 method for signature 'OmicsData'
prepare_upset_data(object)

Arguments

Value

A data frame prepared for UpSet plot.

Examples

omics_data <- createOmicsData()
omics_data <- calculate_measures(omics_data)
omics_data <- flag_anomalies(omics_data)
upset_data <- prepare_upset_data(omics_data)

Process All MRM Transitions for Duplicates

Description

This function takes a tibble containing MRM transition data, processes each sample_id separately to detect duplicates, and adds a column 'MRM_Duplicate_Flag' indicating if a row is a duplicate based on the criteria: same polarity, less than a 1-minute retention time difference, and the same MRM transition (Q1/Q3).

Usage

process_mrm_duplicates(
  mrm_data,
  sample_name_col = "data_filename",
  sample_id_col = "sample_id",
  polarity_col = "polarity",
  retention_time_col = "retention_time",
  mass_info_col = "Mass Info",
  component_name_col = "component_name"
)

Arguments

Value

The original tibble with an added 'MRM_Duplicate_Flag' column.

Author(s)

Yaoxiang Li

Examples

## Not run: 
mrm_data <- tibble::tribble(
  ~`data_filename`, ~`sample_id`, ~polarity, ~`retention_time`, ~`Mass Info`, ~`component_name`,
  "Sample1", "ID1", "Positive", 1.95, "61.0 / 44.0", "Urea_pos",
  "Sample1", "ID1", "Positive", 8.34, "206.0 / 189.0", "Lipoamide_pos",
  "Sample2", "ID2", "Positive", 2.18, "339.1 / 110.0", "AICAR_pos",
  "Sample2", "ID2", "Positive", 1.76, "175.1 / 70.0", "Arginine_pos")
processed_data <- process_mrm_duplicates(
  mrm_data,
  "data_filename",
  "sample_id",
  "polarity",
  "retention_time",
  "Mass Info",
  "component_name"
)
print(processed_data)

## End(Not run)

Plot PVCA results (bar chart)

Description

Plot PVCA results (bar chart)

Usage

pvcaDraw(pvcaobj)

Arguments

Value

A ggplot object (invisibly).

QC-RLSC Normalize function

Description

This function performs normalization on the input data matrix using the robust loess signal correction (RLSC) method. Normalization is based on Quality Control (QC) samples in the data.

Usage

qc_normalize(data, qc_label = "QC", sample_id_col = "sample_id")

Arguments

Value

A data frame with the first column as the sample identifiers and the rest of the columns containing the normalized peak intensities.

Author(s)

Yaoxiang Li

Examples

# Load the CSV data
data_file <- system.file("extdata", "example2.csv", package = "omicsTools")
data <- readr::read_csv(data_file)

# Display the first few rows of the original data
print(head(data))

# Apply the qc_rlsc_normalize function
normalized_data <- qc_normalize(data, qc_label = "QC", sample_id_col = "Sample")

# Display the first few rows of the normalized data
print(head(normalized_data))


# Write the normalized data to a new CSV file
readr::write_csv(normalized_data, paste0(tempdir(), "/normalized_data.csv"))

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(
  onStart = NULL,
  options = list(),
  enableBookmarking = NULL,
  uiPattern = "/",
  ...
)

Arguments

Value

No return value, called for launch the application.

Transpose DataFrame

Description

This function transposes a data frame by converting rows to columns and columns to rows. The first column is assumed to contain the variable names, and the remaining columns contain the values.

Usage

transpose_df(df)

Arguments

Value

A transposed data frame.

Examples

# Example usage:
df <- data.frame(
  ID = c("A", "B", "C"),
  Var1 = c(1, 2, 3),
  Var2 = c(4, 5, 6)
)
transpose_df(df)