Regularized adversarial learning for normalization of multi-batch untargeted metabolomics data

Abstract

Motivation Untargeted metabolomics by mass spectrometry is the method of choice for unbiased analysis of molecules in complex samples of biological, clinical, or environmental relevance. The exceptional versatility and sensitivity of modern high-resolution instruments allows profiling of thousands of known and unknown molecules in parallel. Inter-batch differences constitute a common and unresolved problem in untargeted metabolomics, and hinder the analysis of multi-batch studies or the intercomparison of experiments. Results We present a new method, Regularized Adversarial Learning Preserving Similarity (RALPS), for the normalization of multi-batch untargeted metabolomics data. RALPS builds on deep adversarial learning with a three-term loss function that mitigates batch effects while preserving biological identity, spectral properties, and coefficients of variation. Using two large metabolomics datasets, we showcase the superior performance of RALPS as compared with six state-of-the-art methods for batch correction. Further, we demonstrate that RALPS scales well, is robust, deals with missing values, and can handle different experimental designs. Availability https://github.com/zamboni-lab/RALPS Supplementary information Supplementary material is available at Bioinformatics online.

CellDeathPred: A Deep Learning framework for Ferroptosis and Apoptosis prediction based on cell painting

“…Another study proposed a U-Net architecture to synthesize AT8-pTau image given two DAPI and YFP-tau image channels (15). With the potential of DL architectures in extracting meaningful features directly from microscopic images, recent studies proposed self-supervised learning frameworks, including a framework for studying the temporal drug effect on cancer cell images, or a framework to learn phenotypic embeddings of HCS images using self-supervised triplet network (16, 17). While these advancements in DL application to HCS images offer the potential to accelerate drug discovery, so far there is only very little work about the analysis and prediction of regulated cell death.…”

Exposomic Biomonitoring of Polyphenols by Non-Targeted Analysis and Suspect Screening

“…9 The second strategy, known as untargeted, involves acquiring spectrometric data on all compounds that can be ionized and are sufficiently abundant. 10 The untargeted approach can be split into suspect screening, which involves identifying the unknown chemical features by matching different parameters (e.g., monoisotopic mass) with compounds of interest present in databases; and into nontargeted analysis, which includes various techniques such as annotation by in silico fragmentation and/or finding relevant features by statistical analysis. 8,9 Identifying the chemical features in untargeted datasets is a complex endeavor.…”

“…The first, called traditional human biomonitoring or targeted, focuses on acquiring LC-MS data for only specific known analytes of interest using available reference standards . The second strategy, known as untargeted, involves acquiring spectrometric data on all compounds that can be ionized and are sufficiently abundant . The untargeted approach can be split into suspect screening, which involves identifying the unknown chemical features by matching different parameters (e.g., monoisotopic mass) with compounds of interest present in databases; and into non-targeted analysis, which includes various techniques such as annotation by in silico fragmentation and/or finding relevant features by statistical analysis. , …”