Nonclassical A+B-->0 batch reactions: Effect of mobility on rate, order, aggregation and segregation.
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Journal Article↗
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A statistical multiprobe model for analyzing cis and trans genes in genetical genomics experiments with short-oligonucleotide arrays.
Short-oligonucleotide arrays typically contain multiple probes per gene. In genetical genomics applications a statistical model for the individual probe signals can help in separating "true" differential mRNA expression from "ghost" effects caused by polymorphisms, misdesigned probes, and batch effects. It can also help in detecting alternative splicing, start, or termination.
Animals↗
OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.
Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.
Humans↗
Drinking water source and reproductive outcomes in Sprague-Dawley rats.
As part of our work on the influence of water source on reproductive outcome, Sprague-Dawley rats were randomized to tap water, bottled water, or deionized water treatment groups, utilizing 160 animals per treatment; animals received the water prior to and during pregnancy. Rats were shipped in four batches (A-D). Batch effects were seen for several reproductive parameters. Because the tap water supply was interrupted by an earthquake resulting in an unbalanced design, primary analyses utilized only batches C and D, which included most of the tap water-treated rats. A treatment effect with respect to resorption frequency was seen that was marginally significant using a fixed-effects analysis of variance (P = 0.053), but not when batch was entered as a random effect (P = 0.36). The data were modeled by logistic regression, controlling for batch, litter size, and batch-treatment interaction. The odds ratio comparing tap to bottled water was 1.8 (95% CI 1.0 to 3.3, P = 0.05), which was similar to the epidemiologic result that prompted this study. The magnitude of this association varied by batch, and the difference in resorption frequency was within the range of variation seen for control animals. Although these findings do not justify public health action at this time, further investigation is warranted.
Animals↗
Comparison of the effects of Viscum album lectin ML-1 and fresh plant extract (Isorel) on the cell growth in vitro and tumorigenicity of melanoma B16F10.
Numerous findings indicate that specific plant lectins acting against cancer could be major active components of Viscum album extracts, although activity of low molecular weight components (peptides, carbohydrates and alkaloids) might be as essential for the beneficial activity of the plain plant extracts, too. Thus, active principle of Viscum album extracts is still not understood, and is difficult to be analysed because of the complex composition of the extracts and uncertainty of the standardised effectiveness (batch consistency) of the extracts. The aims of this study were to compare the concentration dependent effects of the pure mistletoe lectin (ML-1) with the fresh plant Viscum album extract (Isorel) and its different MW components on the in vitro growth of ConA stimulated lymphocytes, on the growth and tumorigenicity (artificial lung metastases development) of murine melanoma B16F10 cells, and to compare concentration dependent effects of the different types of the Viscum album extracts in vitro (applying novel type of MTT assay). The results obtained indicate that the effects of Isorel used at high dose could be result of toxic activity of the mistletoe lectins ("ML-1 like" activity). Unlike ML-1, if used at low concentrations, Isorel selectively inhibited tumor cells, due the activity of the low MW components. On the other hand, the number of tumor nodules was reduced (in comparison to the control) equally in the lungs of mice injected with B16F10 cells pre-treated in vitro with the plain Viscum album extract or any of its modifications or ML-1. Hence, it is supposed that the beneficial therapeutic effects of Isorel might result from the combined biological activity of the high and the low MW components not lectins only. Similarly, in MTT assay low concentrations of all types of the Viscum album extract showed stronger inhibiting activity for B16F10 and HeLa cells than pure ML-1. According to these results we propose a standardisation of aqueous Viscum album extracts by comparing their and ML-1 concentration dependent activity on the tumor cells in vitro applying MTT bioassay described which should be relevant for further evaluation of their active principle and for improvement of biotherapy of cancer.
Adjuvants, Immunologic↗
Development of methodology to support molecular endotype discovery from synovial fluid of individuals with knee osteoarthritis: The STEpUP OA consortium.
OBJECTIVES: To develop a protocol for largescale analysis of synovial fluid proteins, for the identification of biological networks associated with subtypes of osteoarthritis. METHODS: Synovial Fluid To detect molecular Endotypes by Unbiased Proteomics in Osteoarthritis (STEpUP OA) is an international consortium utilising clinical data (capturing pain, radiographic severity and demographic features) and knee synovial fluid from 17 participating cohorts. 1746 samples from 1650 individuals comprising OA, joint injury, healthy and inflammatory arthritis controls, divided into discovery (n = 1045) and replication (n = 701) datasets, were analysed by SomaScan Discovery Plex V4.1 (>7000 SOMAmers/proteins). An optimised approach to standardisation was developed. Technical confounders and batch-effects were identified and adjusted for. Poorly performing SOMAmers and samples were excluded. Variance in the data was determined by principal component (PC) analysis. RESULTS: A synovial fluid standardised protocol was optimised that had good reliability (<20% co-efficient of variation for >80% of SOMAmers in pooled samples) and overall good correlation with immunoassay. 1720 samples and >6290 SOMAmers met inclusion criteria. 48% of data variance (PC1) was strongly correlated with individual SOMAmer signal intensities, particularly with low abundance proteins (median correlation coefficient 0.70), and was enriched for nuclear and non-secreted proteins. We concluded that this component was predominantly intracellular proteins, and could be adjusted for using an 'intracellular protein score' (IPS). PC2 (7% variance) was attributable to processing batch and was batch-corrected by ComBat. Lesser effects were attributed to other technical confounders. Data visualisation revealed clustering of injury and OA cases in overlapping but distinguishable areas of high-dimensional proteomic space. CONCLUSIONS: We have developed a robust method for analysing synovial fluid protein, creating a molecular and clinical dataset of unprecedented scale to explore potential patient subtypes and the molecular pathogenesis of OA. Such methodology underpins the development of new approaches to tackle this disease which remains a huge societal challenge.
Humans↗
Patterns of brain activation associated with contextual conditioning to methamphetamine in mice.
Classical conditioning is thought to play a key role in addiction. The authors used c-Fos immunohistochemistry to demonstrate a conditioned physiological response to methamphetamine (meth) in mice. Male outbred mice were placed into an environment where they had previously experienced 2 mg/kg meth or saline. The meth-paired mice displayed increased c-Fos in several brain regions, including the nucleus accumbens, prefrontal cortex, orbitofrontal cortex, basolateral amygdala, and bed nucleus of the stria terminalis. No conditioned locomotor activity was observed, but individual activity levels strongly correlated with c-Fos in many regions. A batch effect among immunohistochemical assays was demonstrated. Results implicate specific brain regions in classical conditioning to meth and demonstrate the importance of considering locomotor activity and batch in a c-Fos study.
Analysis of Variance↗
Variation between batches in clotting factor assays.
Differences in factor V, VII and VIII potency between the standards used in the Northwick Park Heart Study (NPHS) are described in the previous paper. This paper discusses the variation from batch to batch within each standard. In every case there was significant variation between batches, the standard deviation of batch means being between a third and a half of that within batches. Some of this variation could be attributed to drifts over time, but most of the "batch effect" was not easily accounted for. It is relatively simple to correct for steady drift, but there is no satisfactory way of allowing for unexplained batch variation.
Adolescent↗
Multifactor analysis of intermediate cells from the uterine cervix. The importance of slide effects on variance components.
The ability to retrospectively examine cytologic material with digital image analysis is often desirable, particularly when long-term follow-up information is available for correlation with cell parameters. In an effort to characterize the magnitude of slide-age and staining-batch effects on cell parameters from digital image analysis, the cell features in 32 Papanicolaoustained cervical smears that varied in age from 10 to 16 years were analyzed. The slides were selected from eight patients sampled on four different occasions approximately one year apart. The findings indicate that the variance of cell features from an intermediate cell population within a slide is somewhat greater than is the variance among slides from the same woman or the variance among women. Aging effects were not detectable. The significant differences observed among replications of slides from the same woman as well as between women with no evidence of cervical disease should caution other researchers to account for this potential source of random error in their statistical models.
Cell Nucleus↗
Diagnosing scientific replicability through probabilistic distinguishability.
MOTIVATION: Despite the widely recognized importance of replicability in biological research, computational methods to quantify irreplicability and identify irreplicable instances remain underdeveloped. This article presents an efficient and robust computational framework to address this gap. RESULTS: To tackle the challenge of defining an acceptable level of intrinsic heterogeneity among replicable studies, we introduce a distinguishability criterion, ensuring that replicable effects, while potentially heterogeneous, can be distinguished from zero effects and maintain consistent directions with high probability. We implement a Bayesian model criticism approach, reporting a Bayesian P-value to identify potential irreplicable instances. Through numerical experiments, we demonstrate the efficacy of the proposed methods in detecting batch effects in high-throughput experiments and identifying instances of the publication bias. Finally, we apply the framework to multi-tissue eQTL data from the GTEx consortium, uncovering tissue-specific eQTLs that represent biological heterogeneity across tissues. AVAILABILITY AND IMPLEMENTATION: An R package DiscRep implementing our method is available on GitHub (https://github.com/PengWang96/DiscRep).
Bayes Theorem↗
Analysis of variance of microarray data.
Analysis of variance (ANOVA) is an approach used to identify differentially expressed genes in complex experimental designs. It is based on testing for the significance of the magnitude of effect of two or more treatments taking into account the variance within and between treatment classes. ANOVA is a highly flexible analytical approach that allows investigators to simultaneously assess the contributions of multiple factors to gene expression variation, including technical (dye, batch) effects and biological (sex, genotype, drug, time) ones, as well as interactions between factors. This chapter provides an overview of the theory of linear mixture modeling and the sequence of steps involved in fitting gene-specific models and discusses essential features of experimental design. Commercial and open-source software for performing ANOVA is widely available.
Analysis of Variance↗
Multi-model statistical process monitoring and diagnosis of a sequencing batch reactor.
Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and so on. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearity; (2) multiple models with a posterior probability for modeling different operating regions; (3) local batch monitoring by the T(2)- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then, these local regions can be supervised separately, leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.
Biodegradation, Environmental↗
Meta-Merging the Transcriptomes of Gastric Tumors Redefines the Connections among Molecular and Clinical Subtypes.
INTRODUCTION: The availability of a large number of cancer expression profiles presents an excellent opportunity to re-investigate various biological and clinical questions. While several expression profiles have been established for different cancers, merging them may provide a more powerful platform for extensively extrapolating molecular and clinical features across multiple cohorts. MATERIALS AND METHODS: In this study, five gastric tumor expression profiles from the Gene Expression Omnibus [GEO] and one in-house cohort comprising a total of 1,060 samples were merged. The batch effect was removed using non-parametric ComBat analysis, and the seamless merging of datasets was confirmed through various parameters. RESULTS: Extrapolation of ACRG [Asian Cancer Research Group] and TCGA [The Cancer Genome Atlas] molecular subtypes in the merged cohort of 1,060 gastric tumors revealed nine distinct clusters. Notably, the following patterns were observed: [i] mutual exclusivity between Epithelial to Mesenchymal Transition [EMT] and Microsatellite Instability [MSI] subtypes in 90% of tumors; [ii] overlapping occurrence of EMT and MSI subtypes in the remaining tumors; [iii] overlap between MSI and Epstein-Barr Virus [EBV] subtype tumors; [iv] both commonalities and differences between EMT and Genomically Stable [GS] subtypes; and [v] an association between EBV positivity and PI3K mutation. CONCLUSION: The current study demonstrates that compiling a larger expression profile is valuable for revisiting the molecular features and epidemiology associated with molecular subtypes, thereby aiding in the development of novel diagnostics and targeted therapeutics.
Humans↗
Human Systems Immunology in the Omics Era: Challenges, Methods, and Emerging Directions.
The human immune system is a highly complex, dynamic, and heterogeneous network shaped by genetic, environmental, and temporal influences. Advances in high-throughput omics technologies have transformed our ability to study this complexity directly and comprehensively in human cohorts. These developments have positioned systems immunology as a powerful framework for investigating coordinated immune responses, identifying regulatory mechanisms, and linking molecular patterns to clinical phenotypes. However, the analytical challenges inherent to large-scale, multimodal datasets-including batch effects, small sample sizes, high dimensionality, and substantial interindividual heterogeneity-require rigorous study design, robust statistical modeling, and thoughtful data analysis strategies. In this review, we summarize key technological foundations enabling modern human systems immunology, outline common analytical pitfalls and effective mitigation approaches, discuss data integration concepts, and highlight emerging opportunities in the field. Together, these technological and analytical advances are redefining how immune function is measured and interpreted in real-world human biology and hold significant promise for enhancing mechanistic insight, biomarker discovery, and precision medicine across immunological diseases and interventions.
Humans↗
Bioinformatics Analysis and Experimental Validation of Key Genes Associated With Hypoxia and Ischemia in Myocardial Infarction.
BACKGROUND: This study aimed to screen and identify core hypoxia-ischemia-related genes associated with myocardial infarction (MI). METHOD: Two transcriptomic datasets, GSE97320 and GSE48060, were retrieved from the Gene Expression Omnibus (GEO) database. After data integration and batch effect elimination, differential expression analysis was performed to screen differentially expressed genes (DEGs), and the corresponding visualization analysis was conducted. Hypoxia-ischemia-related genes were acquired from the GeneCards database; hypoxia-ischemia related genes (HIRGs) were subsequently identified by intersecting the retrieved genes with screened DEGs. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented to explore the biological functions and underlying signaling pathways of HIRGs. A combination of protein-protein interaction (PPI) network analysis and random forest (RF) algorithm was applied to screen hub genes from HIRGs. The external GEO dataset GSE66360 was utilized to validate the expression patterns of candidate hub genes. Furthermore, an acute myocardial infarction (AMI) mouse model was established, and quantitative real-time polymerase chain reaction (qPCR) was performed to detect the mRNA expression levels of hub genes in myocardial tissues for in vivo validation. RESULTS: A total of 633 DEGs and 308 hypoxia-ischemia-related genes were screened in the present study, among which 21 overlapping HIRGs were obtained. PLAUR and IL1B were finally identified as two hub genes from HIRGs based on PPI network and random forest algorithm. The qPCR results revealed that the expression levels of PLAUR and IL1B were significantly upregulated in the AMI group compared with the sham operation group (p < 0.05). CONCLUSION: The present findings demonstrated that PLAUR and IL1B serve as pivotal genes involved in the pathological hypoxia-ischemia process of AMI. These two genes may act as novel biomarkers and promising therapeutic targets for the recognition and clinical intervention of hypoxia-ischemia injury following AMI.
Myocardial Infarction↗
Modelling infectious disease transmission with complex exposure pattern and sparse outcome data.
We present a regression modelling framework to analyse infectious disease transmission during a time period where extensive exposure data are available, but where the outcome data are sparse. A latent variable model is used for each exposure time, allowing a straight-forward accumulation of risk for a collection of exposures for which outcome data are available. We describe an analysis of HIV infection from blood products among a cohort of haemophiliacs in Ireland between 1980 and 1985. The analysis provides estimates of the time pattern and batch effects; we show how analytical complexity such as smoothly varying coefficients or random coefficient models can be accommodated by the model. Finally, we discuss other problems where the model is applicable.
Cohort Studies↗
Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.
We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.
Humans↗
Stage-Independent Real-Time Subtype Classification and Comprehensive Biopsy Profiling of Urothelial Carcinomas by the Lund Taxonomy System.
Bladder cancer is a heterogeneous malignancy with diverse clinical outcomes, and conventional pathological assessment alone is insufficient to capture its underlying biology. Gene expression profiling can stratify tumors into molecular subtypes with prognostic and predictive potential, but the reliability of transcriptomic classification and its clinical utility remains to be established. The translational/observational UROSCANSEQ study (ISRCTN15459149) prospectively evaluates RNA-based Lund Taxonomy (LundTax) molecular subtype classification in a clinical setting. Among 784 consecutive biopsies collected between 2018 and 2022, RNA sequencing was successful for 90% of all biopsies, encompassing 662 bladder cancer patients with a stage distribution of 48% Ta, 27% T1, 24% ≥T2, and 1% CIS. We demonstrate that the LundTax subtype classification algorithm, applied to individual samples, accurately identifies cancer cell phenotypes with characteristic gene and protein expression patterns in a manner robust to RNA quality, data preprocessing strategies, and batch effects, supporting its clinical feasibility across both non-muscle-invasive and muscle-invasive disease. We further extend the LundTax framework by incorporating single-sample molecular risk scores reflecting tumor grade, proliferation, and progression risk, as well as tumor microenvironment signatures. Both risk scores and overall immune and stromal content in biopsies were significantly associated with an increased risk of clinical progression in noninvasive disease. In a separate analysis of the relative cellular composition of the tumor microenvironment, however, only the fraction of natural killer cells remained significant. Together, the expanded LundTax system provides a comprehensive molecular portrait of individual tumor biopsies. By explicitly separating cancer cell-intrinsic phenotypes, prognostic indexes, and microenvironmental signals, the framework minimizes biological confounding and establishes a strong foundation for future studies evaluating clinical outcomes and treatment responses.
Humans↗