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omicsTools

The goal of omicsTools is to provide tools for 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.

License: AGPL-3.0

Install dependencies

if (!require("BiocManager", quietly = TRUE)) { install.packages("BiocManager") }
if (!require("pvca", quietly = TRUE)) { BiocManager::install("pvca") }

Installation

CRAN version

You can install the Stable version of omicsTools like so:

install.packages("omicsTools")
#> Installing package into 'C:/Users/bach/AppData/Local/Temp/RtmpqQj0Pu/temp_libpath245079296a59'
#> (as 'lib' is unspecified)
#> package 'omicsTools' successfully unpacked and MD5 sums checked
#> 
#> The downloaded binary packages are in
#>  C:\Users\bach\AppData\Local\Temp\RtmpIN7Pjh\downloaded_packages

Development version

To get a bug fix, or use a feature from the development version, you can install omicsTools from GitHub.

if (!require("devtools", quietly = TRUE))
    install.packages("devtools")
#> 
#> Attaching package: 'devtools'
#> The following object is masked from 'package:BiocManager':
#> 
#>     install
devtools::install_github("omicsTools")
#> Using GitHub PAT from the git credential store.
#> Downloading GitHub repo omicsTools@HEAD
#> rlang  (1.1.3  -> 1.1.4 ) [CRAN]
#> cli    (3.6.2  -> 3.6.3 ) [CRAN]
#> digest (0.6.35 -> 0.6.36) [CRAN]
#> fresh  (0.2.0  -> 0.2.1 ) [CRAN]
#> dbscan (1.1-12 -> 1.2-0 ) [CRAN]
#> Installing 5 packages: rlang, cli, digest, fresh, dbscan
#> Installing packages into 'C:/Users/bach/AppData/Local/Temp/RtmpqQj0Pu/temp_libpath245079296a59'
#> (as 'lib' is unspecified)
#> package 'rlang' successfully unpacked and MD5 sums checked
#> package 'cli' successfully unpacked and MD5 sums checked
#> package 'digest' successfully unpacked and MD5 sums checked
#> package 'fresh' successfully unpacked and MD5 sums checked
#> package 'dbscan' successfully unpacked and MD5 sums checked
#> 
#> The downloaded binary packages are in
#>  C:\Users\bach\AppData\Local\Temp\RtmpIN7Pjh\downloaded_packages
#> ── R CMD build ─────────────────────────────────────────────────────────────────
#>          checking for file 'C:\Users\bach\AppData\Local\Temp\RtmpIN7Pjh\remotes53285a4b5044\omicsTools-baa6316/DESCRIPTION' ...  ✔  checking for file 'C:\Users\bach\AppData\Local\Temp\RtmpIN7Pjh\remotes53285a4b5044\omicsTools-baa6316/DESCRIPTION'
#>       ─  preparing 'omicsTools': (337ms)
#>    checking DESCRIPTION meta-information ...     checking DESCRIPTION meta-information ...   ✔  checking DESCRIPTION meta-information
#>       ─  checking for LF line-endings in source and make files and shell scripts
#>       ─  checking for empty or unneeded directories
#>      Omitted 'LazyData' from DESCRIPTION
#>       ─  building 'omicsTools_1.1.3.tar.gz'
#>      
#> 
#> Installing package into 'C:/Users/bach/AppData/Local/Temp/RtmpqQj0Pu/temp_libpath245079296a59'
#> (as 'lib' is unspecified)

Example of imputation

# Load the CSV data
data_file <- system.file("extdata", "example1.csv", package = "omicsTools")
data <- readr::read_csv(data_file)
#> Rows: 85 Columns: 482
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr   (1): Sample
#> dbl (414): Urea_pos, Lipoamide_pos, AcetylAmino Sugars_pos, Glycerophosphoch...
#> lgl  (67): DBQ_pos.IS, Aminolevulinic Acid_pos, Leucine_pos, Homocystine_pos...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Apply the impute function
imputed_data <- omicsTools::handle_missing_values(data)
#> Registered S3 method overwritten by 'GGally':
#>   method from   
#>   +.gg   ggplot2
#> ℹ Starting missing value handling... 

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Version

Install

install.packages('omicsTools')

Monthly Downloads

216

Version

1.1.7

License

AGPL (>= 3)

Maintainer

Yaoxiang Li

Last Published

December 16th, 2025

Functions in omicsTools (1.1.7)

plot_sample_measures

Plot Sample Measures
run_app

Run the Shiny Application
plot_distribution_measures

Plot Distribution Measures
define_thresholds

Define Anomaly Thresholds
combine_logical_tibbles

Combine Multiple Logical Tibbles with Intersection or Union
check_match

Check Match
prepare_upset_data

Prepare Data for UpSet Plot
process_mrm_duplicates

Process All MRM Transitions for Duplicates
createOmicsData

Constructor for OmicsData
load_parse_sciex_txt

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

Internal Standard Normalize
plot_lipid_data_summary

Plot and Analyze Lipid Class Data
pieDraw

Plot PVCA results (pie chart)
perform_feature_selection

Perform Feature Selection
pqn_normalize

Perform Probabilistic Quotient Normalization for intensities
pvcaDraw

Plot PVCA results (bar chart)
qc_normalize

QC-RLSC Normalize function
plot_met_data_summary

Plot and Analyze Metabolomics Data Summary
transpose_df

Transpose DataFrame
convert_mrm_data

Convert MRM Data to Wide Format
calculate_qc_rsd

Calculate QC Statistics and RSD
convert_to_binary_matrix

Convert to Binary Matrix for UpSetR
check_and_sort_columns

Check and Sort Columns, Compare Values
calculate_measures

Calculate Measures for Each Feature
calculate_lof

Calculate Local Outlier Factor (LOF)
flag_underexpressed_features

Flag Underexpressed Features in Samples Based on Blank Samples
generate_process_report

Generate Process Report for Sciex 7500/5500 Raw Data
flag_anomalies

Flag Anomalies
generate_data_with_anomalies

Generate High-Dimensional Data with Anomalies
ensure_enough_sets_for_upset

Ensure There Are Enough Sets for UpSet Plot
detect_duplicates

Detect Duplicate MRM Transitions
OmicsData-class

OmicsData Class
calculate_cooks_distance

Calculate Cook's Distance
ms1_annotation

MS1 Annotation
perform_batch_assessment

Perform Principal Variance Component Analysis for Batch Effect Assessment
%>%

Pipe operator
initialize_results_df

Initialize Results Data Frame
handle_missing_values

Handle Missing Values in a Tibble