Missing Value Imputation for RNA-Sequencing Data Using Statistical Models: A Comparative Study

Baghfalaki, Taban; Ganjali, Mojtaba; Berridge, Damon

doi:10.2991/jsta.2016.15.3.3

Cited by 5 publications

(3 citation statements)

References 33 publications

(16 reference statements)

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“…(3) Missing values were then imputed based on the Expectation–Maximization (EM) algorithm using the Amelia package (ver. 1.6.2) (imputation F) [ 52 , 53 ]. The EM algorithm is an iterative algorithm that is widely used to complete data with missing values.…”

Section: Methodsmentioning

confidence: 99%

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

Takemoto,

Ito,

Komori

et al. 2024

BMC Bioinformatics

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Background Extracellular vesicle-derived (EV)-miRNAs have potential to serve as biomarkers for the diagnosis of various diseases. miRNA microarrays are widely used to quantify circulating EV-miRNA levels, and the preprocessing of miRNA microarray data is critical for analytical accuracy and reliability. Thus, although microarray data have been used in various studies, the effects of preprocessing have not been studied for Toray’s 3D-Gene chip, a widely used measurement method. We aimed to evaluate batch effect, missing value imputation accuracy, and the influence of preprocessing on measured values in 18 different preprocessing pipelines for EV-miRNA microarray data from two cohorts with amyotrophic lateral sclerosis using 3D-Gene technology. Results Eighteen different pipelines with different types and orders of missing value completion and normalization were used to preprocess the 3D-Gene microarray EV-miRNA data. Notable results were suppressed in the batch effects in all pipelines using the batch effect correction method ComBat. Furthermore, pipelines utilizing missForest for missing value imputation showed high agreement with measured values. In contrast, imputation using constant values for missing data exhibited low agreement. Conclusions This study highlights the importance of selecting the appropriate preprocessing strategy for EV-miRNA microarray data when using 3D-Gene technology. These findings emphasize the importance of validating preprocessing approaches, particularly in the context of batch effect correction and missing value imputation, for reliably analyzing data in biomarker discovery and disease research.

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Section: Methodsmentioning

confidence: 99%

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

Takemoto,

Ito,

Komori

et al. 2024

BMC Bioinformatics

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“…Many such studies on cancer genomics require complete data sets [ 7 ]. However, missing values are frequently present in these data due to various reasons, including low resolution, missing probes, and artifacts [ 8 , 9 ]. Therefore, practical methods to handle missing data in genomic data sets are needed for effective downstream analyses.…”

Section: Introductionmentioning

confidence: 99%

Genomic data imputation with variational auto-encoders

2020

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Background As missing values are frequently present in genomic data, practical methods to handle missing data are necessary for downstream analyses that require complete data sets. State-of-the-art imputation techniques, including methods based on singular value decomposition and K-nearest neighbors, can be computationally expensive for large data sets and it is difficult to modify these algorithms to handle certain cases not missing at random. Results In this work, we use a deep-learning framework based on the variational auto-encoder (VAE) for genomic missing value imputation and demonstrate its effectiveness in transcriptome and methylome data analysis. We show that in the vast majority of our testing scenarios, VAE achieves similar or better performances than the most widely used imputation standards, while having a computational advantage at evaluation time. When dealing with data missing not at random (e.g., few values are missing), we develop simple yet effective methodologies to leverage the prior knowledge about missing data. Furthermore, we investigate the effect of varying latent space regularization strength in VAE on the imputation performances and, in this context, show why VAE has a better imputation capacity compared to a regular deterministic auto-encoder. Conclusions We describe a deep learning imputation framework for transcriptome and methylome data using a VAE and show that it can be a preferable alternative to traditional methods for data imputation, especially in the setting of large-scale data and certain missing-not-at-random scenarios.

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“…Many such studies on pan-cancer genomics require complete datasets (Champion, et al, 2018). However, missing values are frequently present in these data due to various reasons including low resolution, missing probes, and artifacts (Baghfalaki, et al, 2016;Libbrecht and Noble, 2015). Therefore, practical methods to handle missing data in pan-cancer genomic datasets are needed for effective downstream analyses.…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for imputing missing values in genomic data

Qiu

Zheng

Gevaert

2018

Preprint

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Motivation:The presence of missing values is a frequent problem encountered in genomic data analysis. Lost data can be an obstacle to downstream analyses that require complete data matrices. State-of-the-art imputation techniques including Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) based methods usually achieve good performances, but are computationally expensive especially for large datasets such as those involved in pan-cancer analysis. Results:This study describes a new method: a denoising autoencoder with partial loss (DAPL) as a deep learning based alternative for data imputation. Results on pan-cancer gene expression data and DNA methylation data from over 11,000 samples demonstrate significant improvement over standard denoising autoencoder for both data missing-at-random cases with a range of missing percentages, and missing-not-at-random cases based on expression level and GCcontent. We discuss the advantages of DAPL over traditional imputation methods and show that it achieves comparable or better performance with less computational burden.

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Missing Value Imputation for RNA-Sequencing Data Using Statistical Models: A Comparative Study

Cited by 5 publications

References 33 publications

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

Comparing preprocessing strategies for 3D-Gene microarray data of extracellular vesicle-derived miRNAs

Genomic data imputation with variational auto-encoders

A deep learning framework for imputing missing values in genomic data

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