Related Experiment Video
Updated: Feb 5, 2026

Dissection of Drosophila melanogaster Flight Muscles for Omics Approaches
Published on: October 17, 2019
TOBMI: trans-omics block missing data imputation using a k-nearest neighbor weighted approach
Xuesi Dong1,2, Lijuan Lin1, Ruyang Zhang1,3
1Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.
Motivation:
Stitching together trans-omics data is a powerful approach to assess the complex mechanisms of cancer occurrence, progression and treatment. However, the integration process suffers from the 'block missing' phenomena when part of individuals lacks some omics data.
Results:
We proposed a k-nearest neighbor (kNN) weighted imputation method for trans-omics block missing data (TOBMIkNN) to handle gene-absence individuals in RNA-seq datasets using external information obtained from DNA methylation probe datasets. Referencing to multi-hot deck, mean imputation and missing cases deletion, we assess the relative error, absolute error, inter-omics correlation structure change and variable selection.The proposed method, TOBMIkNN reliably imputed RNA-seq data by borrowing information from DNA methylation data, and showed superiority over the other three methods in imputation error and stability of correlation structure. Our study indicates that TOBMIkNN can be used as an advisable method for trans-omics block missing data imputation.
Availability And Implementation:
TOBMIkNN is freely available at https://github.com/XuesiDong/TOBMI.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Related Concept Videos
Model Approaches for Pharmacokinetic Data: Compartment Models
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Model Approaches for Pharmacokinetic Data: Physiological Models
Weighted Mean
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

