Dataset Information

Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

ABSTRACT: Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, "proBatch", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology.

SUBMITTER: Cuklina J

PROVIDER: S-EPMC8447595 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

Čuklina Jelena J Lee Chloe H CH Williams Evan G EG Sajic Tatjana T Collins Ben C BC Rodríguez Martínez María M Sharma Varun S VS Wendt Fabian F Goetze Sandra S Keele Gregory R GR Wollscheid Bernd B Aebersold Ruedi R Pedrioli Patrick G A PGA

Molecular systems biology 20210801 8

Advancements in mass spectrometry-based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much-needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step-by-step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity ...[more]

PMID: 34432947

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Project description:ObjectiveTo identify CT-acquisition parameters accounting for radiomics variability and to develop a post-acquisition CT-image correction method to reduce variability and improve radiomics classification in both phantom and clinical applications.MethodsCT-acquisition protocols were prospectively tested in a phantom. The multi-centric retrospective clinical study included CT scans of patients with colorectal/renal cancer liver metastases. Ninety-three radiomics features of first order and texture were extracted. Intraclass correlation coefficients (ICCs) between CT-acquisition protocols were evaluated to define sources of variability. Voxel size, ComBat, and singular value decomposition (SVD) compensation methods were explored for reducing the radiomics variability. The number of robust features was compared before and after correction using two-proportion z test. The radiomics classification accuracy (K-means purity) was assessed before and after ComBat- and SVD-based correction.ResultsFifty-three acquisition protocols in 13 tissue densities were analyzed. Ninety-seven liver metastases from 43 patients with CT from two vendors were included. Pixel size, reconstruction slice spacing, convolution kernel, and acquisition slice thickness are relevant sources of radiomics variability with a percentage of robust features lower than 80%. Resampling to isometric voxels increased the number of robust features when images were acquired with different pixel sizes (p < 0.05). SVD-based for thickness correction and ComBat correction for thickness and combined thickness-kernel increased the number of reproducible features (p < 0.05). ComBat showed the highest improvement of radiomics-based classification in both the phantom and clinical applications (K-means purity 65.98 vs 73.20).ConclusionCT-image post-acquisition processing and radiomics normalization by means of batch effect correction allow for standardization of large-scale data analysis and improve the classification accuracy.Key points• The voxel size (accounting for the pixel size and slice spacing), slice thickness, and convolution kernel are relevant sources of CT-radiomics variability. • Voxel size resampling increased the mean percentage of robust CT-radiomics features from 59.50 to 89.25% when comparing CT scans acquired with different pixel sizes and from 71.62 to 82.58% when the scans were acquired with different slice spacings. • ComBat batch effect correction reduced the CT-radiomics variability secondary to the slice thickness and convolution kernel, improving the capacity of CT-radiomics to differentiate tissues (in the phantom application) and the primary tumor type from liver metastases (in the clinical application).

Dataset Information

Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

Publications

Diagnostics and correction of batch effects in large-scale proteomic studies: a tutorial.

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