News & Updates

Added set seed options at June/27/2018;

Elaborated the required data format at June/18/2018;

Added a new module for MNAR imputation (GSimp) at May/21/2018;

MetImp was started on March 2017!

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Contact

runmin@hawaii.edu

jingyew@hawaii.edu

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Citations

Please cite us :

10.1038/s41598-017-19120-0 10.1371/journal.pcbi.1005973

Introduction

MetImp is a web tool for -omics missing data imputation, especially for mass spectrometry-based metabolomics data from metabolic profiling and targeted analysis.

There are three types of missing values in Metabolomics: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). We recommend a comprehensive approach for handling missing values:

(1) Analyze the possible reasons of missing values: whether they are MNAR/MAR, or MNAR;

(2) If necessary, check raw data and/or adjust parameter settings of data preprocessing in order to fill back certain missing values in an accurate way;

(3) Apply missing filtering to remove those unreliable variables with unacceptable number of missing values;

(4) If missing value imputation is needed for your data, choose an appropriate method;

(5) MetImp provides:

A. Group-wise missing filtering: 0%~100% adjustable, 80% is the default proportion ;

B. For MCAR/MAR: we provide RF, SVD, kNN, Mean, Median imputation, and RF is default;

C. For MNAR: we provide GSimp, QRILC, HM, Zero, Binary imputation, and GSimp is default.

For the imputation evaluation pipeline, please visit: https://github.com/WandeRum/MVI-evaluation