Batch Effect Removal Research Articles

Integrated transcriptomic analysis reveals dysregulated immune infiltration and pro-inflammatory cytokines in the secretory endometrium of recurrent implantation failure patients

Abstract Recurrent implantation failure (RIF) is a leading impediment to assisted reproductive technology, yet the underlying pathogenesis of RIF remains elusive. Recent studies have sought to uncover novel biomarkers and etiological factors of RIF by profiling transcriptomes of endometrial samples. Nonetheless, the inherent heterogeneity among published studies and a scarcity of experimental validations hinder the identification of robust markers of RIF. Hence, we integrated six publicly accessible datasets with 209 samples, including microarray profiles of endometrial samples in the secretory phase. After removing batch effects, we identified 175 differentially expressed genes. Gene set enrichment analysis identified dysregulation of immunological pathways in RIF. We also observed altered immune infiltration and pro-inflammatory cytokines in RIF. Protein–protein interaction network analysis identified ten hub genes, representing two co-expression modules significantly related to RIF. Knockdown of ENTPD3, one of the hub genes, promoted the epithelial-mesenchymal transition (EMT) process and resulted in elevated levels of pro-inflammatory cytokines. Collectively, our study reveals abnormal gene expressions involving the regulation of EMT and immune status in RIF, providing valuable insights into its pathogenesis.

Identifying critical modules and biomarkers of intervertebral disc degeneration by using weighted gene co-expression network.

Intervertebral disc degeneration (IVDD) is an age-related orthopedic degenerative disease characterized by recurrent episodes of lower back pain, the pathogenesis of which is not fully understood. This study aimed to identify key biomarkers of IVDD and its causes. We acquired three gene expression profiles from the Gene Expression Omnibus (GEO) database, GSE56081, GSE124272, and GSE153761, and used limma fast differential analysis to identify differentially expressed genes (DEGs) between normal and IVDD samples after removing batch effects. We applied weighted gene co-expression network (WGCNA) to identify the key modular genes in GSE124272 and intersected these with DEGs. Next, A protein-protein interaction network (PPI) was constructed, and Cytoscape was used to identify the Top 10 hub genes. Functional enrichment analyses were performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Three key genes were validated using Western Blot (WB) and qRT-PCR. Additionally, we predicted miRNAs involved in hub gene co-regulation and analyzed miRNA microarray data from GSE116726 to identify four differentially expressed miRNAs. We identified 10 hub genes using bioinformatics analysis, gene function enrichment analysis revealed that they were primarily enriched in pathways, such as the TNF signaling pathway. We chose JUNB, SOCS3, and CEBPB as hub genes and used WB and qRT-PCR to confirm their expression. All three genes were overexpressed in the IVDD model group compared to the control group. Furthermore, we identified four miRNAs involved in the co-regulation of the hub genes using miRNet prediction: mir-191-5p, mir-20a-5p, mir-155-5p, and mir-124-3p. Using limma difference analysis, we discovered that mir-191-5p, mir-20a-5p, and mir-155-5p were all down-regulated and expressed in IVDD samples, but mir-124-3p showed no significant change. JUNB, SOCS3, and CEBPB were identified as key genes in IVDD, regulated by specific miRNAs, providing potential biomarkers for early diagnosis and therapeutic targets.

GEDI: An R Package for Integration of Transcriptomic Data from Multiple Platforms for Bioinformatics Applications.

Transcriptomic data is often expensive and difficult to generate in large cohorts relative to genomic data; therefore, it is often important to integrate multiple transcriptomic datasets from both microarray- and next generation sequencing (NGS)-based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including reannotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically reannotating the data and removing the batch effect. The removal of the batch effect is verified with principal component analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. These transcriptomic datasets were from multiple high-throughput platforms, namely, array-based Affymetrix and Agilent platforms, and NGS-based Illumina paired-end RNA-seq platform. Furthermore, we compared the GEDI package to existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration for downstream genomic and bioinformatics applications. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: ReadGE, a function to import gene expression datasets Basic Protocol 2: GEDI, a function to reannotate and merge gene expression datasets Basic Protocol 3: BatchCorrection, a function to remove batch effects from gene expression data Basic Protocol 4: VerifyGEDI, a function to confirm successful integration of gene expression data.

Single-cell mosaic integration and cell state transfer with auto-scaling self-attention mechanism.

The integration of data from multiple modalities generated by single-cell omics technologies is crucial for accurately identifying cell states. One challenge in comprehending multi-omics data resides in mosaic integration, in which different data modalities are profiled in different subsets of cells, as it requires simultaneous batch effect removal and modality alignment. Here, we develop Multi-omics Mosaic Auto-scaling Attention Variational Inference (mmAAVI), a scalable deep generative model for single-cell mosaic integration. Leveraging auto-scaling self-attention mechanisms, mmAAVI can map arbitrary combinations of omics to the common embedding space. If existing well-annotated cell states, the model can perform semisupervised learning to utilize existing these annotations. We validated the performance of mmAAVI and five other commonly used methods on four benchmark datasets, which vary in cell numbers, omics types, and missing patterns. mmAAVI consistently demonstrated its superiority. We also validated mmAAVI's ability for cell state knowledge transfer, achieving balanced accuracies of 0.82 and 0.97 with less 1% labeled cells between batches with completely different omics. The full package is available at https://github.com/luyiyun/mmAAVI.

Potential brain biomarkers in patients with Autism spectrum syndrome

Autism spectrum disorder (ASD) is referred as a cluster of neurodevelopmental disorders with relatively high incidence. ASD is believed to be a multifactorial condition, and genetics is one of the most important factors in its formation. Therefore, profiling gene expression in ASD patients can lead to the identification of new molecular insights. To evaluate gene expression patterns, we have utilized NCBI GEO microarray data. The dataset of ASD patients (GSE28475, GSE28521, GSE38322 and GSE113834) were defined as two meta-data, Total brain meta-data and Lobe specified meta-data. Meta-analysis and batch effect removal was conducted by the SVA package. Microarray data analysis was performed using the LIMMA package under R 4.2.1 software. Total Meta-Analysis (TMA) identified 525 significantly differentially expressed genes (DEGs) in ASD patient’s brain. The temporal and frontal lobes of ASD patients showed 96 and 23 DEGs respectively. Among the mentioned DEGs, there were 11 common DEGs between the temporal and frontal lobes that were also dysregulated in TMA except for UTP4 which was only dysregulated in the temporal and frontal lobes. However, the occipital and cerebellum lobes did not show any significant DEGs. Enrichment analysis pointed out the vital roles of identified DEGs in transmembrane transportation, ATP production, and cellular respiration. According to our findings, gene expression profile in the temporal and frontal lobes of ASD patients are significantly different than a control group. This aberrant gene expression potentially leads to crucial complications in nerve signal transmission and defects energy production in neurons. Therefore, potential therapeutic targets may be suggested based on these findings.

Graspot: a graph attention network for spatial transcriptomics data integration with optimal transport.

Spatial transcriptomics (ST) technologies enable the measurement of mRNA expression while simultaneously capturing spot locations. By integrating ST data, the 3D structure of a tissue can be reconstructed, yielding a comprehensive understanding of the tissue's intricacies. Nevertheless, a computational challenge persists: how to remove batch effects while preserving genuine biological structure variations across ST data. To address this, we introduce Graspot, a graph attention network designed for spatial transcriptomics data integration with unbalanced optimal transport. Graspot adeptly harnesses both gene expression and spatial information to align common structures across multiple ST datasets. It embeds multiple ST datasets into a unified latent space, facilitating the partial alignment of spots from different slices. Demonstrating superior performance compared to existing methods on four real ST datasets, Graspot excels in ST data integration, including tasks that require partial alignment. In particular, Graspot efficiently integrates multiple ST slices and guides coordinate alignment. In addition, Graspot accurately aligns the spatio-temporal transcriptomics data to reconstruct human heart developmental processes. Graspot software is available at https://github.com/zhan009/Graspot.

Variational inference of single cell time series.

Time course single-cell RNA sequencing (scRNA-seq) enables researchers to probe genome-wide expression dynamics at the the single cell scale. However, when gene expression is affected jointly by time and cellular identity, analyzing such data - including conducting cell type annotation and modeling cell type-dependent dynamics - becomes challenging. To address this problem, we propose SNOW (SiNgle cell flOW map), a deep learning algorithm to deconvolve single cell time series data into time-dependent and time-independent contributions. SNOW has a number of advantages. First, it enables cell type annotation based on the time-independent dimensions. Second, it yields a probabilistic model that can be used to discriminate between biological temporal variation and batch effects contaminating individual timepoints, and provides an approach to mitigate batch effects. Finally, it is capable of projecting cells forward and backward in time, yielding time series at the individual cell level. This enables gene expression dynamics to be studied without the need for clustering or pseudobulking, which can be error prone and result in information loss. We describe our probabilistic framework in detail and demonstrate SNOW using data from three distinct time course scRNA-seq studies. Our results show that SNOW is able to construct biologically meaningful latent spaces, remove batch effects, and generate realistic time-series at the single-cell level. By way of example, we illustrate how the latter may be used to enhance the detection of cell type-specific circadian gene expression rhythms, and may be readily extended to other time-series analyses.

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

NDMNN: A novel deep residual network based MNN method to remove batch effects from scRNA-seq data.

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has generated vast amounts of data. However, these data often exhibit batch effects due to various factors such as different time points, experimental personnel, and instruments used, which can obscure the biological differences in the data itself. Based on the characteristics of scRNA-seq data, we designed a dense deep residual network model, referred to as NDnetwork. Subsequently, we combined the NDnetwork model with the MNN method to correct batch effects in scRNA-seq data, and named it the NDMNN method. Comprehensive experimental results demonstrate that the NDMNN method outperforms existing commonly used methods for correcting batch effects in scRNA-seq data. As the scale of single-cell sequencing continues to expand, we believe that NDMNN will be a valuable tool for researchers in the biological community for correcting batch effects in their studies. The source code and experimental results of the NDMNN method can be found at https://github.com/mustang-hub/NDMNN.

PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

Rank Matrix Approach for Endometriosis: Integrating Data and Constructing Diagnostic Models

Background: Endometriosis is a debilitating gynecological disorder characterized by chronic pain, infertility, and the growth of endometrial tissue outside the uterus. Accurate and early detection of this condition is crucial for effective management and treatment. Methods: We developed a gene rank matrix-based model to integrate endometriosis cohorts across multiple platforms. After removing batch effects, we identified 83 genes associated with endometriosis and further refined a diagnostic model using 11 of these genes. The model was trained on two platforms and validated on two others using SVM, Random Forest, Logistic Regression, and gradient-boosting machine learning algorithms. Results: The integration via the gene rank matrix effectively mitigated batch effects. Utilizing a gradient boosting classifier with a subset of 11 genes, the model demonstrated commendable diagnostic efficacy, achieving an Area Under the Curve (AUC) of 0.77, an accuracy of 0.72, and an F1 score of 0.72 for the training dataset. When subjected to validation, the model maintained its performance, yielding an AUC of 0.769, an accuracy of 0.719, and an F1 score of 0.732. These 11 genes were found to be associated with immunosuppression. Conclusion: Our approach to integrating gene rank matrices effectively consolidates endometriosis data across diverse platforms. The diagnostic model, harnessing the predictive power of 11 specific genes, surpasses alternative models, thereby offering promising prospects for aiding clinical diagnosis of endometriosis. Further validation is imperative to elucidate the functional significance of these 11 genes. Our study underscores the potential of data integration coupled with machine learning techniques in advancing the diagnosis of intricate diseases, such as endometriosis.

A Metric to Characterize Differentially Methylated Region Sets Detected from Methylation Array Data

Background: Identifying differentially methylated region (DMR) is a basic but important task in epigenomics, which can help investigate the mechanisms of diseases and provide methylation biomarkers for screening diseases. A set of methods have been proposed to identify DMRs from methylation array data. However, it lacks effective metrics to characterize different DMR sets and enable a straight way for comparison. Methods: In this study, we introduce a metric, DMRn, to characterize DMR sets detected by different methods from methylation array data. To calculate DMRn, firstly, the methylation differences of DMRs are recalculated by incorporating the correlations between probes and their represented CpGs. Then, DMRn is calculated based on the number of probes and the dense of CpGs in DMRs with methylation differences falling in each interval. Result & Discussion: By comparing the DMRn of DMR sets predicted by seven methods on four scenario, the results demonstrate that DMRn can make an efficient guidance for selecting DMR sets, and provide new insights in cancer genomics studies by comparing the DMR sets from the related pathological states. For example, there are many regions with subtle methylation alteration in subtypes of prostate cancer are altered oppositely in the benign state, which may indicate a possible revision mechanism in benign prostate cancer. Conclusion: Futhermore, when applied to datasets that underwent different runs of batch effect removal, the DMRn can help to visualize the bias introduced by multi-runs of batch effect removal. The tool for calculating DMRn is available in the GitHub repository(https://github.com/xqpeng/DMRArrayMetric).

Integrated bioinformatics combined with machine learning to analyze shared biomarkers and pathways in psoriasis and cervical squamous cell carcinoma.

Psoriasis extends beyond its dermatological inflammatory manifestations, encompassing systemic inflammation. Existing studies have indicated a potential risk of cervical cancer among patients with psoriasis, suggesting a potential mechanism of co-morbidity. This study aims to explore the key genes, pathways, and immune cells that may link psoriasis and cervical squamous cell carcinoma (CESC). The cervical squamous cell carcinoma dataset (GSE63514) was downloaded from the Gene Expression Omnibus (GEO). Two psoriasis-related datasets (GSE13355 and GSE14905) were merged into one comprehensive dataset after removing batch effects. Differentially expressed genes were identified using Limma and co-expression network analysis (WGCNA), and machine learning random forest algorithm (RF) was used to screen the hub genes. We analyzed relevant gene enrichment pathways using GO and KEGG, and immune cell infiltration in psoriasis and CESC samples using CIBERSORT. The miRNA-mRNA and TFs-mRNA regulatory networks were then constructed using Cytoscape, and the biomarkers for psoriasis and CESC were determined. Potential drug targets were obtained from the cMAP database, and biomarker expression levels in hela and psoriatic cell models were quantified by RT-qPCR. In this study, we identified 27 key genes associated with psoriasis and cervical squamous cell carcinoma. NCAPH, UHRF1, CDCA2, CENPN and MELK were identified as hub genes using the Random Forest machine learning algorithm. Chromosome mitotic region segregation, nucleotide binding and DNA methylation are the major enrichment pathways for common DEGs in the mitotic cell cycle. Then we analyzed immune cell infiltration in psoriasis and cervical squamous cell carcinoma samples using CIBERSORT. Meanwhile, we used the cMAP database to identify ten small molecule compounds that interact with the central gene as drug candidates for treatment. By analyzing miRNA-mRNA and TFs-mRNA regulatory networks, we identified three miRNAs and nine transcription factors closely associated with five key genes and validated their expression in external validation datasets and clinical samples. Finally, we examined the diagnostic effects with ROC curves, and performed experimental validation in hela and psoriatic cell models. We identified five biomarkers, NCAPH, UHRF1, CDCA2, CENPN, and MELK, which may play important roles in the common pathogenesis of psoriasis and cervical squamous cell carcinoma, furthermore predict potential therapeutic agents. These findings open up new perspectives for the diagnosis and treatment of psoriasis and squamous cell carcinoma of the cervix.

Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.

Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.

Analysis of common differential gene expression in synovial cells of osteoarthritis and rheumatoid arthritis.

To elucidate potential molecular mechanisms differentiating osteoarthritis (OA) and rheumatoid arthritis (RA) through a bioinformatics analysis of differentially expressed genes (DEGs) in patient synovial cells, aiming to provide new insights for clinical treatment strategies. Gene expression datasets GSE1919, GSE82107, and GSE77298 were downloaded from the Gene Expression Omnibus (GEO) database to serve as the training groups, with GSE55235 being used as the validation dataset. The OA and RA data from the GSE1919 dataset were merged with the standardized data from GSE82107 and GSE77298, followed by batch effect removal to obtain the merged datasets of differential expressed genes (DEGs) for OA and RA. Intersection analysis was conducted on the DEGs between the two conditions to identify commonly upregulated and downregulated DEGs. Enrichment analysis was then performed on these common co-expressed DEGs, and a protein-protein interaction (PPI) network was constructed to identify hub genes. These hub genes were further analyzed using the GENEMANIA online platform and subjected to enrichment analysis. Subsequent validation analysis was conducted using the GSE55235 dataset. The analysis of differentially expressed genes in the synovial cells from patients with Osteoarthritis (OA) and Rheumatoid Arthritis (RA), compared to a control group (individuals without OA or RA), revealed significant changes in gene expression patterns. Specifically, the genes APOD, FASN, and SCD were observed to have lower expression levels in the synovial cells of both OA and RA patients, indicating downregulation within the pathological context of these diseases. In contrast, the SDC1 gene was found to be upregulated, displaying higher expression levels in the synovial cells of OA and RA patients compared to normal controls.Additionally, a noteworthy observation was the downregulation of the transcription factor PPARG in the synovial cells of patients with OA and RA. The decrease in expression levels of PPARG further validates the alteration in lipid metabolism and inflammatory processes associated with the pathogenesis of OA and RA. These findings underscore the significance of these genes and the transcription factor not only as biomarkers for differential diagnosis between OA and RA but also as potential targets for therapeutic interventions aimed at modulating their expression to counteract disease progression. The outcomes of this investigation reveal the existence of potentially shared molecular mechanisms within Osteoarthritis (OA) and Rheumatoid Arthritis (RA). The identification of APOD, FASN, SDC1, TNFSF11 as key target genes, along with their downstream transcription factor PPARG, highlights common potential factors implicated in both diseases. A deeper examination and exploration of these findings could pave the way for new candidate targets and directions in therapeutic research aimed at treating both OA and RA. This study underscores the significance of leveraging bioinformatics approaches to unravel complex disease mechanisms, offering a promising avenue for the development of more effective and targeted treatments.

BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techniques, significantly impacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of omics research, but current methods are not optimal for the removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. A comparison of batch effect correction methods across five diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that the overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

ITGAM is a critical gene in ischemic stroke.

Globally, ischemic stroke (IS) is ranked as the second most prevailing cause of mortality and is considered lethal to human health. This study aimed to identify genes and pathways involved in the onset and progression of IS. GSE16561 and GSE22255 were downloaded from the Gene Expression Omnibus (GEO) database, merged, and subjected to batch effect removal using the ComBat method. The limma package was employed to identify the differentially expressed genes (DEGs), followed by enrichment analysis and protein-protein interaction (PPI) network construction. Afterward, the cytoHubba plugin was utilized to screen the hub genes. Finally, a ROC curve was generated to investigate the diagnostic value of hub genes. Validation analysis through a series of experiments including qPCR, Western blotting, TUNEL, and flow cytometry was performed. The analysis incorporated 59 IS samples and 44 control samples, revealing 226 DEGs, of which 152 were up-regulated and 74 were down-regulated. These DEGs were revealed to be linked with the inflammatory and immune responses through enrichment analyses. Overall, the ROC analysis revealed the remarkable diagnostic potential of ITGAM and MMP9 for IS. Quantitative assessment of these genes showed significant overexpression in IS patients. ITGAM modulation influenced the secretion of critical inflammatory cytokines, such as IL-1β, IL-6, and TNF-α, and had a distinct impact on neuronal apoptosis. The inflammation and immune response were identified as potential pathological mechanisms of IS by bioinformatics and experiments. In addition, ITGAM may be considered a potential therapeutic target for IS.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

The human infertility single-cell testis atlas (HISTA): an interactive molecular scRNA-Seq reference of the human testis.

Single-cell RNA-seq (scRNA-Seq) has been widely adopted to study gene expression of the human testis. Several datasets of scRNA-Seq from human testis have been generated from different groups processed with different informatics pipelines. An integrated atlas of scRNA-Seq expression constructed from multiple donors, developmental ages, and fertility states would be widely useful for the testis research community. To describe the generation and use of the human infertility single-cell testis atlas (HISTA), an interactive web tool for understanding human spermatogenesis through scRNA-Seq analysis. We obtained scRNA-Seq datasets derived from 12 donors, including healthy adult controls, juveniles, and several infertility cases, and reprocessed these data using methods to remove batch effects. Using Shiny, an open-source environment for data visualization, we created numerous interactive tools for exploring the data, some of which support simple statistical hypothesis testing. We used the resulting HISTA browser and its underlying data to demonstrate HISTA's value for testis researchers. A primary application of HISTA is to search by a single gene or a set of genes; thus, we present various analyses that quantify and visualize gene expression across the testis cells and pathology. HISTA also contains machine-learning-derived gene modules ("components") that capture the entire transcriptional landscape of the testis tissue. We show how the use of these components can simplify the highly complex data in HISTA and assist with the interpretation of genes with unknown functions. Finally, we demonstrate the diverse ways HISTA can be used for new data analysis, including hypothesis testing. HISTA is a research environment that can help scientists organize and understand the high-dimensional transcriptional landscape of the human testis. HISTA has already contributed to published testis research and can be updated as needed with input from the research community or downloaded and modified for individual needs.