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
if (!require("BiocManager", quietly = TRUE)) { install.packages("BiocManager") }
if (!require("pvca", quietly = TRUE)) { BiocManager::install("pvca") }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_packagesTo 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':
#>
#> installdevtools::install_github("cheemalab/omicsTools")
#> Using GitHub PAT from the git credential store.
#> Downloading GitHub repo cheemalab/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\cheemalab-omicsTools-baa6316/DESCRIPTION' ... ✔ checking for file 'C:\Users\bach\AppData\Local\Temp\RtmpIN7Pjh\remotes53285a4b5044\cheemalab-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)# 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... 🚀
#> ℹ Calculating missing value percentages... ⏳
#> ✔ 446 features removed due to missing values exceeding the threshold. ✅
#> ℹ Imputing missing values using method: mean 🔧
#> ✔ Missing value handling completed! 🎉# Write the imputed data to a new CSV file
readr::write_csv(imputed_data, paste0(tempdir(), "/imputed_data.csv"))# Load the CSV data
data_file <- system.file("extdata", "example2.csv", package = "omicsTools")
data <- readr::read_csv(data_file)
#> Rows: 63 Columns: 202
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): Sample
#> dbl (201): Urea_pos, Lipoamide_pos, Glycerophosphocholine_pos, Allanoate_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 normalize function
normalized_data <- omicsTools::qc_normalize(data)
# Write the normalized data to a new CSV file
readr::write_csv(normalized_data, paste0(tempdir(), "/normalized_data.csv"))# Using generated data
library(omicsTools)
#>
#> This is omicsTools version 1.1.3.
#> omicsTools is free software and comes with ABSOLUTELY NO WARRANTY.
#> Please use at your own risk.omics_data <- createOmicsData()
#>
#> ── Generating High-Dimensional Data with Anomalies ─────────────────────────────
#> ℹ Introducing anomalies in specific features
#> ✔ Data generation complete# Define custom thresholds
custom_thresholds <- define_thresholds(skewness = 3, kurtosis = 8)
# Update the OmicsData object with custom thresholds
omics_data@thresholds <- custom_thresholds
# Calculate measures for each feature
omics_data <- calculate_measures(omics_data)
#>
#> ── Calculating Measures for Each Feature ───────────────────────────────────────
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#> ✔ Measures calculation complete# Flag anomalies
omics_data <- flag_anomalies(omics_data)
#>
#> ── Flagging Anomalies ──────────────────────────────────────────────────────────
#> ✔ Anomaly flagging complete# Plotting distribution measures
plot_distribution_measures(omics_data)
#>
#> ── Plotting Distribution Measures ──────────────────────────────────────────────
#> ✔ Distribution measures plot complete
# Plotting sample measures
plot_sample_measures(omics_data)
#>
#> ── Plotting Sample Measures ────────────────────────────────────────────────────
#> ✔ Sample measures plot complete
# Prepare data for UpSet plot
upset_data <- prepare_upset_data(omics_data)
#>
#> ── Preparing Data for UpSet Plot ───────────────────────────────────────────────
#> ✔ Data preparation for UpSet plot complete# Convert to binary matrix for UpSetR
upset_matrix <- convert_to_binary_matrix(upset_data)
#>
#> ── Converting Data to Binary Matrix for UpSetR ─────────────────────────────────
#> ✔ Data conversion to binary matrix complete# Ensure there are at least two columns for the UpSet plot
ensure_enough_sets_for_upset(upset_matrix)
#>
#> ── Checking Data for UpSet Plot ────────────────────────────────────────────────
#> ✔ Enough sets for UpSet plot
library(omicsTools)
# Assuming user_data is a data frame where rows are samples and columns are features
user_data <- generate_data_with_anomalies(n_samples = 100, n_features = 200) # Replace with actual data loading if available
#>
#> ── Generating High-Dimensional Data with Anomalies ─────────────────────────────
#> ℹ Introducing anomalies in specific features
#> ✔ Data generation completeomics_data_user <- createOmicsData(data = user_data)
# Define custom thresholds
custom_thresholds_user <- define_thresholds(skewness = 4, kurtosis = 6, shapiro_p = 1e-5, cooks_distance = 0.5, lof = 10)
# Update the OmicsData object with custom thresholds
omics_data_user@thresholds <- custom_thresholds_user
# Calculate measures for each feature
omics_data_user <- calculate_measures(omics_data_user)
#>
#> ── Calculating Measures for Each Feature ───────────────────────────────────────
#> Calculating [=====================================>--------------] 74% in 0sCalculating [======================================>-------------] 74% in 0sCalculating [======================================>-------------] 75% in 0sCalculating [======================================>-------------] 76% in 0sCalculating [=======================================>------------] 76% in 0sCalculating [=======================================>------------] 77% in 0sCalculating [=======================================>------------] 78% in 0sCalculating [========================================>-----------] 78% in 0sCalculating [========================================>-----------] 79% in 0sCalculating [========================================>-----------] 80% in 0sCalculating [=========================================>----------] 80% in 0sCalculating [=========================================>----------] 81% in 0sCalculating [=========================================>----------] 82% in 0sCalculating [==========================================>---------] 82% in 0sCalculating [==========================================>---------] 83% in 0sCalculating [==========================================>---------] 84% in 0sCalculating [===========================================>--------] 84% in 0sCalculating [===========================================>--------] 85% in 0sCalculating [===========================================>--------] 86% in 0sCalculating [============================================>-------] 86% in 0sCalculating [============================================>-------] 87% in 0sCalculating [=============================================>------] 88% in 0sCalculating [=============================================>------] 89% in 0sCalculating [==============================================>-----] 90% in 0sCalculating [==============================================>-----] 91% in 0sCalculating [===============================================>----] 92% in 0sCalculating [===============================================>----] 93% in 0sCalculating [================================================>---] 94% in 0sCalculating [================================================>---] 95% in 0sCalculating [=================================================>--] 96% in 0sCalculating [=================================================>--] 97% in 0sCalculating [==================================================>-] 98% in 0sCalculating [==================================================>-] 99% in 0sCalculating [===================================================>] 100% in 0sCalculating [====================================================] 100% in 0s
#> ✔ Measures calculation complete# Flag anomalies
omics_data_user <- flag_anomalies(omics_data_user)
#>
#> ── Flagging Anomalies ──────────────────────────────────────────────────────────
#> ✔ Anomaly flagging complete# Plotting distribution measures
plot_distribution_measures(omics_data_user)
#>
#> ── Plotting Distribution Measures ──────────────────────────────────────────────
#> ✔ Distribution measures plot complete
# Plotting sample measures
plot_sample_measures(omics_data_user)
#>
#> ── Plotting Sample Measures ────────────────────────────────────────────────────
#> ✔ Sample measures plot complete
# Prepare data for UpSet plot
upset_data_user <- prepare_upset_data(omics_data_user)
#>
#> ── Preparing Data for UpSet Plot ───────────────────────────────────────────────
#> ✔ Data preparation for UpSet plot complete# Convert to binary matrix for UpSetR
upset_matrix_user <- convert_to_binary_matrix(upset_data_user)
#>
#> ── Converting Data to Binary Matrix for UpSetR ─────────────────────────────────
#> ✔ Data conversion to binary matrix complete# Ensure there are at least two columns for the UpSet plot
ensure_enough_sets_for_upset(upset_matrix_user)
#>
#> ── Checking Data for UpSet Plot ────────────────────────────────────────────────
#> ✔ Enough sets for UpSet plot
Since this is a collaborative project, please adhere to the following code formatting conventions: * We use the tidyverse style guide (https://style.tidyverse.org/) * Please write roxygen2 comments as full sentences, starting with a capital letter and ending with a period. Brevity is preferred (e.g., “Calculates standard deviation” is preferred over “This method calculates and returns a standard deviation of given set of numbers”).