| Title: | Unified Preprocessing Toolkit for Proteomics and Metabolomics |
| Version: | 0.1.1 |
| Maintainer: | Isaac Osei <ikemillar65@gmail.com> |
| Description: | Provides unified workflows for quality control, normalization, and visualization of proteomic and metabolomic data. The package simplifies preprocessing through automated imputation, scaling, and principal component analysis (PCA)-based exploratory analysis, enabling researchers to prepare omics datasets efficiently for downstream statistical and machine learning analyses. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | ggplot2, stats, utils |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| RoxygenNote: | 7.3.3 |
| URL: | https://github.com/ikemillar/OmicsPrepR |
| BugReports: | https://github.com/ikemillar/OmicsPrepR/issues |
| NeedsCompilation: | no |
| Packaged: | 2025-11-06 19:13:18 UTC; isaacosei |
| Author: | Isaac Osei [aut, cre], Dennis Opoku Boadu [aut], Chettupally Anil Carie [aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-11-11 21:30:08 UTC |
Export Cleaned Omics Data
Description
Saves a cleaned omics dataset to a CSV file.
Usage
export_clean(data, file_path)
Arguments
data |
A cleaned omics data frame or matrix. |
file_path |
Path to the file where the data will be saved. |
Value
None. A file is written to disk.
Examples
# Create sample data
data <- matrix(rnorm(100), nrow = 10)
cleaned_data <- as.data.frame(data)
# Save to a temporary location (CRAN policy compliant)
temp_file <- tempfile(fileext = ".csv")
export_clean(cleaned_data, temp_file)
Impute Missing Values in Omics Data
Description
Impute Missing Values in Omics Data
Usage
impute_missing(data, method = c("mean", "median"))
Arguments
data |
Omics data frame with missing values. |
method |
Imputation method ("mean", "median"). |
Value
Data frame with imputed values.
Integrate Proteomic and Metabolomic Data
Description
Integrate Proteomic and Metabolomic Data
Usage
integrate_omics(prot, met)
Arguments
prot |
Proteomics data frame. |
met |
Metabolomics data frame. |
Value
A merged data frame with common samples.
Load Proteomics or Metabolomics Data
Description
Load Proteomics or Metabolomics Data
Usage
load_omics(file, type = c("proteomics", "metabolomics"))
Arguments
file |
Path to data file (.csv or .tsv) |
type |
Type of omics data ("proteomics" or "metabolomics") |
Value
A data frame containing the omics dataset
Examples
# Create a temporary CSV file with example omics data
tmp <- tempfile(fileext = ".csv")
write.csv(matrix(rnorm(20), nrow = 5), tmp, row.names = FALSE)
# Load the omics data
data <- load_omics(tmp, type = "proteomics")
head(data)
Normalize Omics Data
Description
Normalize Omics Data
Usage
normalize_omics(data, method = c("zscore", "log2"))
Arguments
data |
A numeric data frame of omics values. |
method |
Normalization method ("zscore", "log2", "quantile"). |
Value
Normalized data frame.
Plot Omics Data (PCA, Heatmap, or Density)
Description
Visualizes omics datasets using PCA, heatmap, or density plot options.
Arguments
data |
A numeric matrix or data frame containing omics measurements. |
type |
A character string specifying the visualization type. One of "pca", "heatmap", or "density". |
Value
A plot object (for PCA and density) or a heatmap visualization.
Examples
data <- matrix(rnorm(100), nrow = 10)
plot_omics(data, type = "pca")