STK3 higher expression association with clinical characteristics in intrinsic subtypes of breast cancer invasive ductal carcinoma patients.

PURPOSE: STK3 has a central role in maintaining cell homeostasis, proliferation, growth, and apoptosis. Previously, we investigated the functional link between STK3/MST2, and estrogen receptor in MCF-7 breast cancer cells. To expand the investigation, this study evaluated STK3's higher expression and associated genes in breast cancer intrinsic subtypes using publicly available data. METHODS: The relationship between clinical pathologic features and STK3 high expression was analyzed using descriptive and multivariate analysis. RESULTS: Increased STK3 expression in breast cancer was significantly associated with higher pathological cancer stages, and a different expression level was observed in the intrinsic subtypes of breast cancer. Kaplan-Meier analysis showed that breast cancer with high STK3 had a lower survival rate in IDC patients than that with low STK3 expression (p < 0.05). The multivariate analysis unveiled a strong correlation between STK3 expression and the survival rate among IDC patients, demonstrating hazard ratios for lower expression. In the TCGA dataset, the hazard ratio was 0.56 (95% CI 0.34-0.94, p = 0.029) for patients deceased with tumor, and 0.62 (95% CI 0.42-0.92, p = 0.017) for all deceased patients. Additionally, in the METABRIC dataset, the hazard ratio was 0.76 (95% CI 0.64-0.91, p = 0.003) for those deceased with tumor. From GSEA outcomes 7 gene sets were selected based on statistical significance (FDR < 0.25 and p < 0.05). Weighted Sum model (WSM) derived top 5% genes also have higher expression in basal and lower in luminal A in association with STK3. CONCLUSION: By introducing a novel bioinformatics approach that combines GSEA and WSM, the study successfully identified the top 5% of genes associated with higher expression of STK3.

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets.

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly used and state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete ($\gt 95\%$ pure, $\gt 90\%$ complete) and high-quality ($\gt 90\%$ pure, $\gt 70\%$ complete) genomes from simulated datasets from the Critical Assessment of Metagenome Interpretation initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method.

A primer for generating and using transcriptome data and gene sets.

Transcriptomic approaches have provided a growing set of powerful tools with which to study genome-wide patterns of gene expression. Rapidly evolving technologies enable analysis of transcript abundance data from particular tissues and even single cells. This Primer discusses methods that can be used to collect and profile RNAs from specific tissues or cells, process and analyze high-throughput RNA-sequencing data, and define sets of genes that accurately represent a category, such as tissue-enriched or tissue-specific gene expression.

HCC subtypes based on the activity changes of immunologic and hallmark gene sets in tumor and nontumor tissues.

The prognostic role of adjacent nontumor tissue in hepatocellular carcinoma (HCC) patients is still not clear. The activity changes of immunologic and hallmark gene sets in adjacent nontumor tissues may substantially impact on prognosis by affecting proliferation of liver cells and colonization of circulating tumor cells after HCC treatment measures such as hepatectomy. We aimed to identify HCC subtypes and prognostic gene sets based on the activity changes of gene sets in tumor and nontumor tissues, to improve patient outcomes. We comprehensively revealed the activity changes of immunologic and hallmark gene sets in HCC and nontumor samples by gene set variation analysis (GSVA), and identified three clinically relevant subtypes of HCC by nonnegative matrix factorization method (NMF). Patients with subtype 1 had good overall survival, whereas those with subtype 2 and subtype 3 had poor prognosis. Patients with subtype 1 in the validation group also tended to live longer. We also identified three prognostic gene sets in tumor and four prognostic gene sets in nontumor by least absolute shrinkage and selection operator method (LASSO). Interestingly, functional enrichment analysis revealed that in nontumor tissues, genes from four gene sets correlated with immune reaction, cell adhesion, whereas in tumor tissue, genes from three gene sets closely correlated with cell cycle. Our results offer new insights on accurately evaluating prognosis-the important role of gene sets in both tumor and adjacent nontumor tissues, suggesting that when selecting for HCC treatment modality, changes in tumor and nontumor tissues should also be considered, especially after hepatectomy.

Identification of a novel immune-related transcriptional regulatory network in sarcopenia.

BACKGROUND: Sarcopenia is highly prevalent in elderly individuals and has a significant adverse effect on their physical health and quality of life, but the mechanisms remain unclear. Studies have indicated that transcription factors (TFs) and the immune microenvironment play a vital role in skeletal muscle atrophy. METHODS: RNA-seq data of 40 muscle samples were downloaded from the GEO database. Then, differentially expressed genes (DEGs), TFs(DETFs), pathways(DEPs), and the expression of immune gene sets were identified with limma, edgeR, GO, KEGG, ORA, GSVA, and ssGSEA. Furthermore, the results above were integrated into coexpression analysis by Pearson correlation analysis (PCA). Significant coexpression patterns were used to construct the immune-related transcriptional regulatory network by Cytoscape and potential medicine targeting the network was screened by Connectivity Map. Finally, the regulatory mechanisms and RNA expression of DEGs and DETFs were identified by multiple online databases and RT‒qPCR. RESULTS: We screened 808 DEGs (log2 fold change (FC) > 1 or < - 1, p < 0.05), 4 DETFs (log2FC > 0.7 or < - 0.7, p < 0.05), 304 DEPs (enrichment scores (ES) > 1 or < - 1, p < 0.05), and 1208 differentially expressed immune genes sets (DEIGSs) (p < 0.01). Based on the results of PCA (correlation coefficient (CC) > 0.4 or < - 0.4, p < 0.01), we then structured an immune-related network with 4 DETFs, 9 final DEGs, 11 final DEPs, and 6 final DEIGSs. Combining the results of online databases and in vitro experiments, we found that PAX5-SERPINA5-PI3K/Akt (CC ≤ 0.444, p ≤ 0.004) was a potential transcriptional regulation axis, and B cells (R = 0.437, p = 0.005) may play a vital role in this signal transduction. Finally, the compound of trichostatin A (enrichment = -0.365, specificity = 0.4257, p < 0.0001) might be a potential medicine for sarcopenia based on the PubChem database and the result of the literature review. CONCLUSIONS: We first identified immune-related transcriptional regulatory network with high-throughput RNA-seq data in sarcopenia. We hypothesized that PAX5-SERPIAN5-PI3K/Akt axis is a potential mechanism in sarcopenia and that B cells may play a vital role in this signal transduction. In addition, trichostatin A might be a potential medicine for sarcopenia.

A Ribosome-Related Prognostic Signature of Breast Cancer Subtypes Based on Changes in Breast Cancer Patients' Immunological Activity.

Background and Objectives. The prognostic role of adjacent nontumor tissue in patients with breast cancer (BC) is still unclear. The activity changes in immunologic and hallmark gene sets in normal tissues adjacent to BC may play a crucial role in predicting the prognosis of BC patients. The aim of this study was to identify BC subtypes and ribosome-associated prognostic genes based on activity changes of immunologic and hallmark gene sets in tumor and adjacent nontumor tissues to improve patient prognosis. Materials and Methods. Gene set variation analysis (GSVA) was applied to assess immunoreactivity changes in the overall sample and three immune-related BC subtypes were identified by non-negative matrix factorization (NMF). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses were after determining the prognostic gene set using the least absolute shrinkage and selection operator (LASSO) method. Ribosome-related genes were identified by PPI (protein-protein interaction) analysis, and finally a prognostic risk model was constructed based on the expression of five ribosomal genes (RPS18, RPL11, PRLP1, RPL27A, and RPL38). Results. A comprehensive analysis of immune and marker genomic activity changes in normal breast tissue and BC tissue identified three immune-related BC subtypes. BC subtype 1 has the best prognosis, and subtype 3 has the worst overall survival rate. We identified a prognostic gene set in nontumor tissue by the least absolute shrinkage and selection operator (LASSO) method. We found that the results of both KEGG and GO analyses were indistinguishable from those of ribosome-associated genes. Finally, we determined that genes associated with ribosomes exhibit potential as a reliable predictor of overall survival in breast cancer patients. Conclusions. Our research provides an important guidance for the treatment of BC. After a mastectomy, the changes in gene set activity of both BC tissues and the nontumor tissues adjacent to it should be thoroughly evaluated, with special attention to changes in ribosome-related genes in the nontumor tissues.

Benchmarker: An Unbiased, Association-Data-Driven Strategy to Evaluate Gene Prioritization Algorithms.

Genome-wide association studies (GWASs) are valuable for understanding human biology, but associated loci typically contain multiple associated variants and genes. Thus, algorithms that prioritize likely causal genes and variants for a given phenotype can provide biological interpretations of association data. However, a critical, currently missing capability is to objectively compare performance of such algorithms. Typical comparisons rely on "gold standard" genes harboring causal coding variants, but such gold standards may be biased and incomplete. To address this issue, we developed Benchmarker, an unbiased, data-driven benchmarking method that compares performance of similarity-based prioritization strategies to each other (and to random chance) by leave-one-chromosome-out cross-validation with stratified linkage disequilibrium (LD) score regression. We first applied Benchmarker to 20 well-powered GWASs and compared gene prioritization based on strategies employing three different data sources, including annotated gene sets and gene expression; genes prioritized based on gene sets had higher per-SNP heritability than those prioritized based on gene expression. Additionally, in a direct comparison of three methods, DEPICT and MAGMA outperformed NetWAS. We also evaluated combinations of methods; our results indicated that combining data sources and algorithms can help prioritize higher-quality genes for follow-up. Benchmarker provides an unbiased approach to evaluate any similarity-based method that provides genome-wide prioritization of genes, variants, or gene sets and can determine the best such method for any particular GWAS. Our method addresses an important unmet need for rigorous tool assessment and can assist in mapping genetic associations to causal function.

Identifying psychiatric disorder-associated gut microbiota using microbiota-related gene set enrichment analysis.

Psychiatric disorders are a group of complex psychological syndromes with high prevalence. It has been reported that gut microbiota has a dominant influence on the risks of psychiatric disorders through gut microbiota-brain axis. We extended the classic gene set enrichment analysis (GSEA) approach to detect the association between gut microbiota and complex diseases using published genome-wide association study (GWAS) and GWAS of gut microbiota summary data. We applied our approach to real GWAS data sets of five psychiatric disorders, including attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (AUT), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD). To evaluate the performance of our approach, we also tested the genetic correlations of obesity and type 2 diabetes with gut microbiota. We identified several significant associations between psychiatric disorders and gut microbiota, such as ADHD and genus Desulfovibrio (P = 0.031), order Clostridiales (P = 0.034). For AUT, association signals were observed for genera Bacteroides (P = 0.012) and Desulfovibrio (P = 0.033). Genus Desulfovibrio (P = 0.005) appeared to be associated with BD. For MDD, association signals were observed for genus Desulfovibrio (P = 0.003), order Clostridiales (P = 0.004), family Lachnospiraceae (P = 0.007) and genus Bacteroides (P = 0.007). Genus Desulfovibrio (P = 0.012) and genus Bacteroides (P = 0.038) appeared to be associated with SCZ. Our study results provide novel clues for revealing the roles of gut microbiota in psychiatric disorders. This study also illustrated the good performance of GSEA approach for exploring the relationships between gut microbiota and complex diseases.

massiveGST: A Mann-Whitney-Wilcoxon Gene-Set Test Tool That Gives Meaning to Gene-Set Enrichment Analysis.

Gene-set enrichment analysis is the key methodology for obtaining biological information from transcriptomic space's statistical result. Since its introduction, Gene-set Enrichment analysis methods have obtained more reliable results and a wider range of application. Great attention has been devoted to global tests, in contrast to competitive methods that have been largely ignored, although they appear more flexible because they are independent from the source of gene-profiles. We analyzed the properties of the Mann-Whitney-Wilcoxon test, a competitive method, and adapted its interpretation in the context of enrichment analysis by introducing a Normalized Enrichment Score that summarize two interpretations: a probability estimate and a location index. Two implementations are presented and compared with relevant literature methods: an R package and an online web tool. Both allow for obtaining tabular and graphical results with attention to reproducible research.

Polygenic risk scores for neuropsychiatric, inflammatory, and cardio-metabolic traits highlight possible genetic overlap with suicide attempt and treatment-emergent suicidal ideation.

Suicide is the second cause of death among youths. Genetics may contribute to suicidal phenotypes and their co-occurrence in other neuropsychiatric and medical conditions. Our study aimed to investigate the association of polygenic risk scores (PRSs) for 24 neuropsychiatric, inflammatory, and cardio-metabolic traits/diseases with suicide attempt (SA) or treatment-worsening/emergent suicidal ideation (TWESI). PRSs were computed based on summary statistics of genome-wide association studies. Regression analyses were performed between PRSs and SA or TWESI in four clinical cohorts. Results were then meta-analyzed across samples, including a total of 688 patients with SA (Neff  = 2,258) and 214 with TWESI (Neff  = 785). Stratified genetic covariance analyses were performed to investigate functionally cross-phenotype PRS associations. After Bonferroni correction, PRS for major depressive disorder (MDD) was associated with SA (OR = 1.24; 95% CI = 1.11-1.38; p = 1.73 × 10-4 ). Nominal associations were shown between PRSs for coronary artery disease (CAD) (p = 4.6 × 10-3 ), loneliness (p = .009), or chronic pain (p = .016) and SA, PRSs for MDD or CAD and TWESI (p = .043 and p = .032, respectively). Genetic covariance between MDD and SA was shown in 86 gene sets related to drugs having antisuicidal effects. A higher genetic liability for MDD may underlie a higher SA risk. Further, but milder, possible modulatory factors are genetic risk for loneliness and CAD.

Novel microbiota-related gene set enrichment analysis identified osteoporosis associated gut microbiota from autoimmune diseases.

INTRODUCTION: Gut microbiota is now considered to be a hidden organ that interacts bidirectionally with cellular responses in numerous organs belonged to the immune, bone, and nervous systems. Here, we aimed to investigate the relationships between gut microbiota and complex diseases by utilizing multiple publicly available genome-wide association. MATERIALS AND METHODS: We applied a novel microbiota-related gene set enrichment analysis approach to detect the associations between gut microbiota and complex diseases by processing genome-wide association studies (GWASs) data sets of six autoimmune diseases (including celiac disease (CeD), inflammatory bowel diseases (IBD), multiple sclerosis (MS), primary biliary cirrhosis (PBC), type 1 diabetes (T1D) and primary sclerosing cholangitis (PSC)) and osteoporosis (OP). RESULTS: The family Oxalobacteraceae and genus Candidatus_Soleaferrea were found to be correlated with all of the six autoimmune diseases (FDR adjusted P < 0.05). Moreover, we observed that the six autoimmune diseases except PBC shared 3 overlapping features (including family Peptostreptococcaceae, order Gastranaerophilales and genus Romboutsia). For all of the six autoimmune diseases and BMDs (LS-BMD and TB-BMD), an association signal was observed for genus Candidatus_Soleaferrea (FDR adjusted P < 0.05). Notably, FA / FN-BMD shared the maximum number of overlapping microbial features (e.g., genus Ruminococcaceae_UCG009, Erysipelatoclostridium and Ruminococcaceae_UCG013). CONCLUSION: Our study found that part of the gut microbiota could be novel regulators of BMDs and autoimmune diseases via the effects of its metabolites and may lead to a better understanding of the role played by gut microbiota in the communication of the microbiota-skeletal/immune-gut axis.

Comparing tuberculosis gene signatures in malnourished individuals using the TBSignatureProfiler.

BACKGROUND: Gene expression signatures have been used as biomarkers of tuberculosis (TB) risk and outcomes. Platforms are needed to simplify access to these signatures and determine their validity in the setting of comorbidities. We developed a computational profiling platform of TB signature gene sets and characterized the diagnostic ability of existing signature gene sets to differentiate active TB from LTBI in the setting of malnutrition. METHODS: We curated 45 existing TB-related signature gene sets and developed our TBSignatureProfiler software toolkit that estimates gene set activity using multiple enrichment methods and allows visualization of single- and multi-pathway results. The TBSignatureProfiler software is available through Bioconductor and on GitHub. For evaluation in malnutrition, we used whole blood gene expression profiling from 23 severely malnourished Indian individuals with TB and 15 severely malnourished household contacts with latent TB infection (LTBI). Severe malnutrition was defined as body mass index (BMI) < 16 kg/m2 in adults and based on weight-for-height Z scores in children < 18 years. Gene expression was measured using RNA-sequencing. RESULTS: The comparison and visualization functions from the TBSignatureProfiler showed that TB gene sets performed well in malnourished individuals; 40 gene sets had statistically significant discriminative power for differentiating TB from LTBI, with area under the curve ranging from 0.662-0.989. Three gene sets were not significantly predictive. CONCLUSION: Our TBSignatureProfiler is a highly effective and user-friendly platform for applying and comparing published TB signature gene sets. Using this platform, we found that existing gene sets for TB function effectively in the setting of malnutrition, although differences in gene set applicability exist. RNA-sequencing gene sets should consider comorbidities and potential effects on diagnostic performance.

Search and visualization of gene-drug-disease interactions for pharmacogenomics and precision medicine research using GeneDive.

BACKGROUND: Understanding the relationships between genes, drugs, and disease states is at the core of pharmacogenomics. Two leading approaches for identifying these relationships in medical literature are: human expert led manual curation efforts, and modern data mining based automated approaches. The former generates small amounts of high-quality data, and the latter offers large volumes of mixed quality data. The algorithmically extracted relationships are often accompanied by supporting evidence, such as, confidence scores, source articles, and surrounding contexts (excerpts) from the articles, that can be used as data quality indicators. Tools that can leverage these quality indicators to help the user gain access to larger and high-quality data are needed. APPROACH: We introduce GeneDive, a web application for pharmacogenomics researchers and precision medicine practitioners that makes gene, disease, and drug interactions data easily accessible and usable. GeneDive is designed to meet three key objectives: (1) provide functionality to manage information-overload problem and facilitate easy assimilation of supporting evidence, (2) support longitudinal and exploratory research investigations, and (3) offer integration of user-provided interactions data without requiring data sharing. RESULTS: GeneDive offers multiple search modalities, visualizations, and other features that guide the user efficiently to the information of their interest. To facilitate exploratory research, GeneDive makes the supporting evidence and context for each interaction readily available and allows the data quality threshold to be controlled by the user as per their risk tolerance level. The interactive search-visualization loop enables relationship discoveries between diseases, genes, and drugs that might not be explicitly described in literature but are emergent from the source medical corpus and deductive reasoning. The ability to utilize user's data either in combination with the GeneDive native datasets or in isolation promotes richer data-driven exploration and discovery. These functionalities along with GeneDive's applicability for precision medicine, bringing the knowledge contained in biomedical literature to bear on particular clinical situations and improving patient care, are illustrated through detailed use cases. CONCLUSION: GeneDive is a comprehensive, broad-use biological interactions browser. The GeneDive application and information about its underlying system architecture are available at http://www.genedive.net. GeneDive Docker image is also available for download at this URL, allowing users to (1) import their own interaction data securely and privately; and (2) generate and test hypotheses across their own and other datasets.

Integrative Genomic Enrichment Analysis Identified the Brain Regions and Development Stages Related to Anorexia Nervosa and Obsessive-Compulsive Disorder.

Our aim is to explore the spatial and temporal features of anorexia nervosa (AN) and obsessive-compulsive disorder (OCD) considering different brain regions and development stages. The gene sets related to 16 brain regions and nine development stages were obtained from a brain spatial and temporal transcriptomic dataset. Using the genome-wide association study data, transcriptome-wide association study (TWAS) was conducted to identify the genes whose imputed expressions were associated with AN and OCD, respectively. The mRNA expression profiles were analyzed by GEO2R to obtain differentially expressed genes. Gene set enrichment analysis was conducted to detect the spatial and temporal features related to AN and OCD using the TWAS and mRNA expression analysis results. We observed multiple common association signals shared by TWAS and mRNA expression analysis of AN, such as the primary auditory cortex vs. cerebellar cortex in fetal development and earlier vs. later fetal development in the somatosensory cortex. For OCD, we also detected multiple common association signals, such as medial prefrontal cortex vs. amygdala in adulthood and fetal development vs. infancy in mediodorsal nucleus of thalamus. Our study provides novel clues for describing the spatial and temporal features of brain development in the pathogenesis of AN and OCD.

GeneSetCluster: a tool for summarizing and integrating gene-set analysis results.

BACKGROUND: Gene-set analysis tools, which make use of curated sets of molecules grouped based on their shared functions, aim to identify which gene-sets are over-represented in the set of features that have been associated with a given trait of interest. Such tools are frequently used in gene-centric approaches derived from RNA-sequencing or microarrays such as Ingenuity or GSEA, but they have also been adapted for interval-based analysis derived from DNA methylation or ChIP/ATAC-sequencing. Gene-set analysis tools return, as a result, a list of significant gene-sets. However, while these results are useful for the researcher in the identification of major biological insights, they may be complex to interpret because many gene-sets have largely overlapping gene contents. Additionally, in many cases the result of gene-set analysis consists of a large number of gene-sets making it complicated to identify the major biological insights. RESULTS: We present GeneSetCluster, a novel approach which allows clustering of identified gene-sets, from one or multiple experiments and/or tools, based on shared genes. GeneSetCluster calculates a distance score based on overlapping gene content, which is then used to cluster them together and as a result, GeneSetCluster identifies groups of gene-sets with similar gene-set definitions (i.e. gene content). These groups of gene-sets can aid the researcher to focus on such groups for biological interpretations. CONCLUSIONS: GeneSetCluster is a novel approach for grouping together post gene-set analysis results based on overlapping gene content. GeneSetCluster is implemented as a package in R. The package and the vignette can be downloaded at https://github.com/TranslationalBioinformaticsUnit.

Upregulated KPNA2 promotes hepatocellular carcinoma progression and indicates prognostic significance across human cancer types.

Hepatocellular carcinoma (HCC) is one of the most aggressive cancers worldwide. Identification of the molecular mechanisms underlying the development and progression of HCC is particularly important. Here, we demonstrated the expression pattern, clinical significance, and function of Karyopherin α2 (KPNA2) in HCC. The expression of KPNA2 was upregulated in tumor tissue and negatively associated with the survival time, and a significant correlation between KPNA2 expression and aggressive clinical characteristics was established. Both in vitro and in vivo experiments demonstrated that knockdown of KPNA2 reduced migration and proliferation capacities of HCC cells, while over-expression of KPNA2 increased these malignant characteristics. The analysis of the Cancer Genome Atlas cohorts also reveals that high-KPNA2 expression is associated with poor outcome in multiple cancer types. In addition, gene sets enrichment analysis exhibited cell cycle and DNA replication as the top altered pathways in the high-KPNA2 expression group in HCC and other two cancer types. Overall, this study identified KPNA2 as a potential diagnostic and prognostic biomarker in HCC and other neoplasms, probably by regulating cell cycle and DNA replication.

Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes.

BACKGROUND: Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways. RESULTS: We developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward's method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes. CONCLUSION: We validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive 'Fuzzy Inference of Gene-sets (FIGS)' package (GitHub: https://github.com/Thakar-Lab/FIGS ) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.

Empirical pathway analysis, without permutation.

Resampling-based expression pathway analysis techniques have been shown to preserve type I error rates, in contrast to simple gene-list approaches that implicitly assume the independence of genes in ranked lists. However, resampling is intensive in computation time and memory requirements. We describe accurate analytic approximations to permutations of score statistics, including novel approaches for Pearson's correlation, and summed score statistics, that have good performance for even relatively small sample sizes. Our approach preserves the essence of permutation pathway analysis, but with greatly reduced computation. Extensions for inclusion of covariates and censored data are described, and we test the performance of our procedures using simulations based on real datasets. These approaches have been implemented in the new R package safeExpress.

Correcting transcription factor gene sets for copy number and promoter methylation variations.

Gene set analysis provides a method to generate statistical inferences across sets of linked genes, primarily using high-throughput expression data. Common gene sets include biological pathways, operons, and targets of transcriptional regulators. In higher eukaryotes, especially when dealing with diseases with strong genetic and epigenetic components such as cancer, copy number loss and gene silencing through promoter methylation can eliminate the possibility that a gene is transcribed. This, in turn, can adversely affect the estimation of transcription factor or pathway activity from a set of target genes, as some of the targets may not be responsive to transcriptional regulation. Here we introduce a simple filtering approach that removes genes from consideration if they show copy number loss or promoter methylation, and demonstrate the improvement in inference of transcription factor activity in a simulated dataset based on the background expression observed in normal head and neck tissue.

Elucidating sleep disorders: a comprehensive bioinformatics analysis of functional gene sets and hub genes.

Background: Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood. Methods: The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses. Results: The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis. Conclusion: In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results.