omicsTools1.1.7 package

Omics Data Process Toolbox

calculate_cooks_distance

Calculate Cook's Distance

calculate_lof

Calculate Local Outlier Factor (LOF)

calculate_measures

Calculate Measures for Each Feature

calculate_qc_rsd

Calculate QC Statistics and RSD

check_and_sort_columns

Check and Sort Columns, Compare Values

check_match

Check Match

combine_logical_tibbles

Combine Multiple Logical Tibbles with Intersection or Union

convert_mrm_data

Convert MRM Data to Wide Format

convert_to_binary_matrix

Convert to Binary Matrix for UpSetR

createOmicsData

Constructor for OmicsData

define_thresholds

Define Anomaly Thresholds

detect_duplicates

Detect Duplicate MRM Transitions

ensure_enough_sets_for_upset

Ensure There Are Enough Sets for UpSet Plot

flag_anomalies

Flag Anomalies

flag_underexpressed_features

Flag Underexpressed Features in Samples Based on Blank Samples

generate_data_with_anomalies

Generate High-Dimensional Data with Anomalies

generate_process_report

Generate Process Report for Sciex 7500/5500 Raw Data

handle_missing_values

Handle Missing Values in a Tibble

initialize_results_df

Initialize Results Data Frame

internal_standard_normalize

Internal Standard Normalize

load_parse_sciex_txt

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

ms1_annotation

MS1 Annotation

OmicsData-class

OmicsData Class

perform_batch_assessment

Perform Principal Variance Component Analysis for Batch Effect Assessm...

perform_feature_selection

Perform Feature Selection

pieDraw

Plot PVCA results (pie chart)

pipe

Pipe operator

plot_distribution_measures

Plot Distribution Measures

plot_lipid_data_summary

Plot and Analyze Lipid Class Data

plot_met_data_summary

Plot and Analyze Metabolomics Data Summary

plot_sample_measures

Plot Sample Measures

pqn_normalize

Perform Probabilistic Quotient Normalization for intensities

prepare_upset_data

Prepare Data for UpSet Plot

process_mrm_duplicates

Process All MRM Transitions for Duplicates

pvcaDraw

Plot PVCA results (bar chart)

qc_normalize

QC-RLSC Normalize function

run_app

Run the Shiny Application

transpose_df

Transpose DataFrame

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>.

  • Maintainer: Yaoxiang Li
  • License: AGPL (>= 3)
  • Last published: 2025-12-16