‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

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MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

Taking the leap between analytical chemistry and artificial intelligence: A tutorial review.

Deep learning (DL), an emerging area of investigation in the fields of machine learning and artificial intelligence, has markedly advanced over the past years. DL techniques are being applied to assist medical professionals and researchers in improving clinical diagnosis, disease prediction and drug discovery. It is expected that DL will help to provide actionable knowledge from a variety of 'big data', including metabolomics data. In this review, we discuss the applicability of DL to metabolomics, while presenting and discussing several examples from recent research. We emphasize the use of DL in tackling bottlenecks in metabolomics data acquisition, processing, metabolite identification, as well as in metabolic phenotyping and biomarker discovery. Finally, we discuss how DL is used in genome-scale metabolic modelling and in interpretation of metabolomics data. The DL-based approaches discussed here may assist computational biologists with the integration, prediction and drawing of statistical inference about biological outcomes, based on metabolomics data.

Deep learning meets metabolomics: a methodological perspective.

https://doi.org/10.1016/j.inffus.2022.01.001

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis.

Untargeted metabolomics based on liquid chromatography-mass spectrometry is affected by nonlinear batch effects, which cover up biological effects, result in nonreproducibility, and are difficult to be calibrate. In this study, we propose a novel deep learning model, called Normalization Autoencoder (NormAE), which is based on nonlinear autoencoders (AEs) and adversarial learning. An additional classifier and ranker are trained to provide adversarial regularization during the training of the AE model, latent representations are extracted by the encoder, and then the decoder reconstructs the data without batch effects. The NormAE method was tested on two real metabolomics data sets. After calibration by NormAE, the quality control samples (QCs) for both data sets gathered most closely in a PCA score plot (average distances decreased from 56.550 and 52.476 to 7.383 and 14.075, respectively) and obtained the highest average correlation coefficients (from 0.873 and 0.907 to 0.997 for both). Additionally, NormAE significantly improved biomarker discovery (median number of differential peaks increased from 322 and 466 to 1140 and 1622, respectively). NormAE was compared with four commonly used batch effect removal methods. The results demonstrated that using NormAE produces the best calibration results.

NormAE: Deep Adversarial Learning Model to Remove Batch Effects in Liquid Chromatography Mass Spectrometry-Based Metabolomics Data.

The tumor immune microenvironment is heterogeneous, and its impact on treatment responses is not well understood. It is still a challenge to analyze the interaction between malignant cells and the tumor microenvironment to apply suitable immunotherapy in lung adenocarcinoma. We performed the nonnegative matrix factorization method to 513 messenger RNA expression profiles of lung adenocarcinomas (LUADs) from The Cancer Genome Atlas (TCGA) to obtain an immune-related expression pattern. Subsequently, we characterized the immune-related gene signatures and clinical and survival characteristics. We used 576 patients from Gene Expression Omnibus to confirm our findings. Of the patients in the training cohort, 51% had a high immune enrichment score, high expression of immune cell signaling, cytolytic activity, and interferon (IFN)-related signatures (all P < .05). We denoted these as the Immune Class. We further subdivided the Immune Class into two subclasses based on the tumor microenvironment. These were denoted the Active Immune Class and Exhausted Immune Class. The former showed significant IFN, T-cells, M1 macrophage signatures, and better prognosis (all P < .05), while the latter presented an exhausted immune response with activated stromal enrichment, M2 macrophage signatures, and immunosuppressive factors such as WNT/transforming growth factor-β (all P < .05). Furthermore, we predicted the response of our immunophenotypes to immunological checkpoint inhibitors (P < .05). Our findings provide a novel insight into the immune-related state of LUAD and can identify the patients who will be receptive to suitable immunotherapeutic treatments.

Identification immunophenotyping of lung adenocarcinomas based on the tumor microenvironment.

Background: Primary Epithelial Ovarian Cancer (EOC), a malignant gynecologic disease, is considered one of the leading causes of mortality in women. The development of Liver Metastases (LM) in women with primary ovarian cancer commonly results in a poorer prognosis. This retrospective population-based study aims to measure the prevalence, prognostic factors, and associated risk factors for epithelial ovarian cancer patients with liver metastases (EOCLM). Materials and Methods: The current study cohort of patients based on the Surveillance, Epidemiology, and End Results (SEER) database identified with primary ovarian cancer between the years 2010 and 2016. A chi-square test was employed to compare Metastatic differences among demographic and clinical factors. Univariable and multivariable logistic regression analysis models were used to predict related prognostic factors for LM development. 7-year Kaplan-Meier curves were applied to compare the survival patterns of patients with and without LM. The Multivariable Cox regression model was used to estimate potential risk factors associated with LM related deaths. Results: 33895 eligible primary EOC patients were identified. Among them 2635 (7.77%) patients were initially diagnosed with de novo LM, and 31260 (92.23%) without metastases disease to any site. Non-serous histology type; Malignant Brenner Carcinoma, NOS (OR 1.94; CI: 1.39-2.71; P<0.001), T3/T1 stage (OR 5.65; CI: 3.87-8.24; P<0.001), N1/N0 stage (OR 1.67; CI: 1.43-4.95; P<0.001), grade; G3/G1 (OR 2.16; CI: 1.29-3.59, P<0.001), and cancer antigen-125; Elevated/Normal (OR 1.79; CI: 1.19-2.69, P<0.001) were significantly associated with LM occurrence. The median survival of EOC patients with LM was 12.0 (95% CI: 11.0-14.0; P<0.001) months. Multivariable cox regression showed being unmarried (HR 1.16; CI: 1.04-1.30; P=0.001), non-serous histology types, Mucinous (HR 2.38; CI: 1.82-3.12; P<0.001), Clear cell (HR 1.83; CI: 1.32-2.55; P<0.001), Malignant Brenner Carcinoma, NOS (HR 1.44; CI:1.23-1.66; P<0.001), Carcinosarcoma NOS, (HR 1.44; CI: 1.11-1.88; P<0.001) and radiotherapy (HR 1.52; CI: 1.12-2.06; P<0.001), were positively related to death. Chemotherapy (HR 0.30; CI: 1.12-2.06; P<0.001) and surgery (HR 0.34; CI: 0.29-0.39; P<0.001) were related with reduced rate of death. Conclusion: The retrospective cohort study showed that women with primary EOC had some high-risk factors associated with LM. LM can intensely decrease the survival of EOC patients. The findings of our research provided estimates for LM occurrence prediction and potential prognostic factors of EOC with de novo LM development. These findings can be useful for follow-up strategies, guidelines for screening, and treatment of EOCLM.

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The Prevalence and associated Factors for Liver Metastases, Development and Prognosis in newly diagnosed Epithelial Ovarian Cancer: A large Population-Based Study from the SEER Database.

Colorectal cancer (CRC) remains an incurable disease. Previous metabolomic studies show that metabolic signatures in plasma distinguish CRC patients from healthy controls. Chronic enteritis (CE) represents a risk factor for CRC, with a 20 fold greater incidence than in healthy individuals. However, no studies have performed metabolomic profiling to investigate CRC biomarkers in CE. Our aims were to identify metabolomic signatures in CRC and CE and to search for blood-derived metabolite biomarkers distinguishing CRC from CE, especially early-stage biomarkers. In this case–control study, 612 subjects were prospectively recruited between May 2015 and May 2016, and including 539 CRC patients (stage I, 102 cases; stage II, 259 cases; stage III, 178 cases) and 73 CE patients. Untargeted metabolomics was performed to identify CRC-related metabolic signatures in CE. Five pathways were significantly enriched based on 153 differential metabolites between CRC and CE. 16 biomarkers were identified for diagnosis of CRC from CE and for guiding CRC staging. The AUC value for CRC diagnosis in the external validation set was 0.85. Good diagnostic performances were also achieved for early-stage CRC (stage I and stage II), with an AUC value of 0.84. The biomarker panel could also stage CRC patients, with an AUC of 0.72 distinguishing stage I from stage II CRC and AUC of 0.74 distinguishing stage II from stage III CRC. The identified metabolic biomarkers exhibit promising properties for CRC monitoring in CE patients and are superior to commonly used clinical biomarkers (CEA and CA19-9).

Yue Huang

Papers

WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis.

NormAE: Deep Adversarial Learning Model to Remove Batch Effects in Liquid Chromatography Mass Spectrometry-Based Metabolomics Data.

Identification immunophenotyping of lung adenocarcinomas based on the tumor microenvironment.

The Prevalence and associated Factors for Liver Metastases, Development and Prognosis in newly diagnosed Epithelial Ovarian Cancer: A large Population-Based Study from the SEER Database.

Metabolic phenotyping to monitor chronic enteritis canceration