» Articles » PMID: 17920334

Sequential Imputation for Missing Values

Overview
Publisher Elsevier
Date 2007 Oct 9
PMID 17920334
Citations 13
Authors
Affiliations
Soon will be listed here.
Abstract

As missing values are often encountered in gene expression data, many imputation methods have been developed to substitute these unknown values with estimated values. Despite the presence of many imputation methods, these available techniques have some disadvantages. Some imputation techniques constrain the imputation of missing values to a limited set of genes, whereas other imputation methods optimise a more global criterion whereby the computation time of the method becomes infeasible. Others might be fast but inaccurate. Therefore in this paper a new, fast and accurate estimation procedure, called SEQimpute, is proposed. By introducing the idea of minimisation of a statistical distance rather than a Euclidean distance the method is intrinsically different from the thus far existing imputation methods. Moreover, this newly proposed method can be easily embedded in a multiple imputation technique which is better suited to highlight the uncertainties about the missing value estimates. A comparative study is performed to assess the estimation of the missing values by different imputation approaches. The proposed imputation method is shown to outperform some of the existing imputation methods in terms of accuracy and computation speed.

Citing Articles

Proteomic landscape profiling of primary prostate cancer reveals a 16-protein panel for prognosis prediction.

Sun R, A J, Yu H, Wang Y, He M, Tan L Cell Rep Med. 2024; 5(8):101679.

PMID: 39168102 PMC: 11384950. DOI: 10.1016/j.xcrm.2024.101679.


Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning.

Webel H, Niu L, Bach Nielsen A, Locard-Paulet M, Mann M, Jensen L Nat Commun. 2024; 15(1):5405.

PMID: 38926340 PMC: 11208500. DOI: 10.1038/s41467-024-48711-5.


Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference.

Peng H, Wang H, Kong W, Li J, Goh W Nat Commun. 2024; 15(1):3922.

PMID: 38724498 PMC: 11082229. DOI: 10.1038/s41467-024-47899-w.


Ageing-dependent thiol oxidation reveals early oxidation of proteins with core proteostasis functions.

Jonak K, Suppanz I, Bender J, Chacinska A, Warscheid B, Topf U Life Sci Alliance. 2024; 7(5).

PMID: 38383455 PMC: 10881836. DOI: 10.26508/lsa.202302300.


Identification of TMEM126A as OXA1L-interacting protein reveals cotranslational quality control in mitochondria.

Poerschke S, Oeljeklaus S, Cruz-Zaragoza L, Schenzielorz A, Dahal D, Hillen H Mol Cell. 2024; 84(2):345-358.e5.

PMID: 38199007 PMC: 10805001. DOI: 10.1016/j.molcel.2023.12.013.