Microarray missing values imputation methods: Critical analysis review

Hourani, Mou'ath; Emary, Ibrahiem M. M. El

doi:10.2298/csis0902165h

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Note also that C ⊂ C. Indeed, it is easy to see that σ 1 is the largest eigenvalue of Σ with multiplicity 1 and associated eigenvector θ with less than s 1 nonzero components, I p − θθ ⊤ ∞ = 1 and (I p − θθ ⊤ )θ = 0. Next, we define ω 0 = (1, 1, 0, • • • , 0) ∈ {0, 1} p and Ω = ω = (ω (1) , • • • , ω (p) ) ∈ {0, 1} p : ω (1) = ω (2) = 1, |ω| 0 = s 1 ∪ {ω 0 }.…”

Section: Proof Of Theoremmentioning

confidence: 99%

“…The simple strategy that consists in eliminating from the PCA study any gene with at least one missing observation is not acceptable in this context since up to 95% of the genes can be eliminated from the study. An alternative strategy consists in infering the missing values prior to the PCA using complex imputation schemes [2,5]. These schemes usually assume that the genes interactions follow some specified model and involve intensive computational preprocessing to imput the missing observations.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Principal Component Analysis with Missing Observations

Lounici

2013

Progress in Probability

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In this paper, we study the problem of sparse Principal Component Analysis (PCA) in the high-dimensional setting with missing observations. Our goal is to estimate the first principal component when we only have access to partial observations. Existing estimation techniques are usually derived for fully observed data sets and require a prior knowledge of the sparsity of the first principal component in order to achieve good statistical guarantees. Our contributions is threefold. First, we establish the first information-theoretic lower bound for the sparse PCA problem with missing observations. Second, we propose a simple procedure that does not require any prior knowledge on the sparsity of the unknown first principal component or any imputation of the missing observations, adapts to the unknown sparsity of the first principal component and achieves the optimal rate of estimation up to a logarithmic factor. Third, if the covariance matrix of interest admits a sparse first principal component and is in addition approximately low-rank, then we can derive a completely data-driven procedure computationally tractable in high-dimension, adaptive to the unknown sparsity of the first principal component and statistically optimal (up to a logarithmic factor).

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Section: Proof Of Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse Principal Component Analysis with Missing Observations

Lounici

2013

Progress in Probability

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“…Imputation strategies, such as substituting row averages for missing values, are necessary. Additionally, SVD’s performance is sensitive to data type, exhibiting potential challenges in non-time series datasets lacking clear expression patterns (59). Its linear regression nature in lower-dimensional space may result in diminished performance for non-time series data, where expression patterns are often less distinct (57).…”

Section: Complex Imputation Methodsmentioning

confidence: 99%

Enhancing data integrity in Electronic Health Records: Review of methods for handling missing data

Vahdati,

Cotterill,

Marsden

et al. 2024

Preprint

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Introduction Electronic Health Records (EHRs) are vital repositories of patient information for medical research, but the prevalence of missing data presents an obstacle to the validity and reliability of research. This study aimed to review and categorise methods for handling missing data in EHRs to help researchers better understand and address the challenges related to missing data in EHRs. Materials and Methods This study employed scoping review methodology. Through systematic searches on EMBASE up to October 2023, including review articles and original studies, relevant literature was identified. After removing duplicates, titles and abstracts were screened against inclusion criteria, followed by full-text assessment. Additional manual searches and reference list screenings were conducted. Data extraction focused on imputation techniques, dataset characteristics, assumptions about missing data, and article types. Additionally, we explored the availability of code within widely used software applications. Results We reviewed 101 articles, with two exclusions as duplicates. Of the 99 remaining documents, 21 underwent full-text screening, with nine deemed eligible for data extraction. These articles introduced 31 imputation approaches classified into ten distinct methods, ranging from simple techniques like Complete Case Analysis to more complex methods like Multiple Imputation, Maximum Likelihood, and Expectation-Maximization algorithm. Additionally, machine learning methods were explored. The different imputation methods, present varying reliability. We identified a total of 32 packages across the four software platforms (R, Python, SAS, and Stata) for imputation methods. However, it's significant that machine learning methods for imputation were not found in specific packages for SAS and Stata. Out of the 9 imputation methods we investigated, package implementations were available for 7 methods in all four software platforms. Conclusions Several methods to handle missing data in EHRs are available. These methods range in complexity and make different assumptions about the missing data mechanisms. Knowledge gaps remain, notably in handling non-monotone missing data patterns and implementing imputation methods in real-world healthcare settings under the Missing Not at Random assumption. Future research should prioritize refining and directly comparing existing methods.

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“…Even though this is a low-cost solution, it might produce lowquality datasets. The third category, imputation, is one of the best methods that can renew the whole dataset in order to prove the best means to process the missing values in the experiments (Tian et al, 2012;Hourani & Emary, 2009). Accordingly, the imputation method attempts to increase the relevancy and knowledge from the data that are able to construct a complete dataset.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Missing Values Using Optimised Hybrid Fuzzy C-Means and Majority Vote for Microarray Data

Kumaran

Othman

Yusuf

2020

Journal of Information and Communication Technology

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Missing values are a huge constraint in microarray technologies towards improving and identifying disease-causing genes. Estimating missing values is an undeniable scenario faced by field experts. The imputation method is an effective way to impute the proper values to proceed with the next process in microarray technology. Missing value imputation methods may increase the classification accuracy. Although these methods might predict the values, classification accuracy rates prove the ability of the methods to identify the missing values in gene expression data. In this study, a novel method, Optimised Hybrid of Fuzzy C-Means and Majority Vote (opt-FCMMV), was proposed to identify the missing values in the data. Using the Majority Vote (MV) and optimisation through Particle Swarm Optimisation (PSO), this study predicted missing values in the data to form more informative and solid data. In order to verify the effectiveness of opt-FCMMV, several experiments were carried out on two publicly available microarray datasets (i.e. Ovary and Lung Cancer) under three missing value mechanisms with five different percentage values in the biomedical domain using Support Vector Machine (SVM) classifier. The experimental results showed that the proposed method functioned efficiently by showcasing the highest accuracy rate as compared to the one without imputations, with imputation by Fuzzy C-Means (FCM), and imputation by Fuzzy C-Means with Majority Vote (FCMMV). For example, the accuracy rates for Ovary Cancer data with 5% missing values were 64.0% for no imputation, 81.8% (FCM), 90.0% (FCMMV), and 93.7% (opt-FCMMV). Such an outcome indicates that the opt-FCMMV may also be applied in different domains in order to prepare the dataset for various data mining tasks.

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Microarray missing values imputation methods: Critical analysis review

Cited by 9 publications

References 11 publications

Sparse Principal Component Analysis with Missing Observations

Sparse Principal Component Analysis with Missing Observations

Enhancing data integrity in Electronic Health Records: Review of methods for handling missing data

Estimation of Missing Values Using Optimised Hybrid Fuzzy C-Means and Majority Vote for Microarray Data

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