Omics Data Process Toolbox
Calculate Cook's Distance
Calculate Local Outlier Factor (LOF)
Calculate Measures for Each Feature
Calculate QC Statistics and RSD
Check and Sort Columns, Compare Values
Check Match
Combine Multiple Logical Tibbles with Intersection or Union
Convert MRM Data to Wide Format
Convert to Binary Matrix for UpSetR
Constructor for OmicsData
Define Anomaly Thresholds
Detect Duplicate MRM Transitions
Ensure There Are Enough Sets for UpSet Plot
Flag Anomalies
Flag Underexpressed Features in Samples Based on Blank Samples
Generate High-Dimensional Data with Anomalies
Generate Process Report for Sciex 7500/5500 Raw Data
Handle Missing Values in a Tibble
Initialize Results Data Frame
Internal Standard Normalize
Load and Parse SCIEX OS Exported LC-MRM-MS2 Data
MS1 Annotation
OmicsData Class
Perform Principal Variance Component Analysis for Batch Effect Assessm...
Perform Feature Selection
Plot PVCA results (pie chart)
Pipe operator
Plot Distribution Measures
Plot and Analyze Lipid Class Data
Plot and Analyze Metabolomics Data Summary
Plot Sample Measures
Perform Probabilistic Quotient Normalization for intensities
Prepare Data for UpSet Plot
Process All MRM Transitions for Duplicates
Plot PVCA results (bar chart)
QC-RLSC Normalize function
Run the Shiny Application
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>.