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PubMed2001

Transcriptome of channel catfish (Ictalurus punctatus): initial analysis of genes and expression profiles of the head kidney.

Analysis of expressed sequence tags (ESTs) is an efficient approach for gene discovery, expression profiling, and development of resources useful for functional genomics studies. As part of the transcriptome analysis in channel catfish (Ictalurus punctatus), we have conducted EST analysis using a cDNA library made from the head kidney. We analysed 2228 EST clones. Orthologues were established for 1495 (67.1%) clones representing 748 genes, of which 545 (36.5%) clones were singletons. The remaining 733 (32.9%) clones represent unknown gene clones, for which the number of genes has not yet been determined.

Animals↗

PubMed2024

Histopathologic Features and Transcriptomic Signatures Do Not Solve the Issue of Magnetic Resonance Imaging-Invisible Prostate Cancers: A Matched-Pair Analysis.

BACKGROUND: Multiparametric magnetic resonance imaging (mpMRI) is pivotal in prostate cancer (PCa) diagnosis, but some clinically significant (cs) PCa remain undetected. This study aims to understand the pathological and molecular basis for csPCa visibility at mpMRI. METHODS: We performed a retrospective matched-pair cohort study, including patients undergoing radical prostatectomy (RP) for csPCa (i.e., ISUP grade group ≥ 2) from 2015 to 2020, in our tertiary-referral center. We screened for inclusion in the "mpMRI-invisible" cohort all consecutive men (N = 45) having a negative preoperative mpMRI. The "mpMRI-visible" cohort was matched based on age, PSA, prostate volume, ISUP grade group. Included patients underwent radiological and pathological open-label revisions and characterization of the tumor mRNA expression profile (analyzing 780 gene transcripts, signaling pathways, and cell-type profiling). We compared the clinical-pathological variables and the gene expression profile between matched pairs. The analysis was stratified according to histological characteristics and lesion diameter. RESULTS: We included 34 patients (17 per cohort); mean age at RP and PSA were 70.5 years (standard deviation [SD] = 7.7), 7.1 ng/mL (SD = 3.3), respectively; 65% of men were ISUP 2. Overall, no significant differences in histopathological features, tumor diameter and location, mRNA profile, pathways, and cell-type scores emerged between cohorts. In the stratified analysis, an upregulation of cell adhesion and motility, of extracellular matrix remodeling and of metastatic process pathways was present in specific subgroups of mpMRI-invisible cancers. CONCLUSIONS: No PCa pathological or gene-expression hallmarks explaining mp-MRI invisibility were identified. Aggressive features can be present both in mpMRI-invisible and -visible tumors.

Humans↗

PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗

PubMed2002

Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs.

Only a small proportion of the mouse genome is transcribed into mature messenger RNA transcripts. There is an international collaborative effort to identify all full-length mRNA transcripts from the mouse, and to ensure that each is represented in a physical collection of clones. Here we report the manual annotation of 60,770 full-length mouse complementary DNA sequences. These are clustered into 33,409 'transcriptional units', contributing 90.1% of a newly established mouse transcriptome database. Of these transcriptional units, 4,258 are new protein-coding and 11,665 are new non-coding messages, indicating that non-coding RNA is a major component of the transcriptome. 41% of all transcriptional units showed evidence of alternative splicing. In protein-coding transcripts, 79% of splice variations altered the protein product. Whole-transcriptome analyses resulted in the identification of 2,431 sense-antisense pairs. The present work, completely supported by physical clones, provides the most comprehensive survey of a mammalian transcriptome so far, and is a valuable resource for functional genomics.

Alternative Splicing↗

PubMed2003

Analysis of the mouse transcriptome for genes involved in the function of the nervous system.

We analyzed the mouse Representative Transcript and Protein Set for molecules involved in brain function. We found full-length cDNAs of many known brain genes and discovered new members of known brain gene families, including Family 3 G-protein coupled receptors, voltage-gated channels, and connexins. We also identified previously unknown candidates for secreted neuroactive molecules. The existence of a large number of unique brain ESTs suggests an additional molecular complexity that remains to be explored.A list of genes containing CAG stretches in the coding region represents a first step in the potential identification of candidates for hereditary neurological disorders.

Adenine↗

PubMed2026

Multi-omics integration and colocalization analyses prioritize candidate molecular loci associated with hypothermia.

BACKGROUND: Hypothermia is a life-threatening condition lacking specific pharmacological treatments. This study aimed to prioritize genetically supported molecular loci associated with hypothermia and to explore their pharmacological tractability using multi-omics data. METHODS: Initially, 2532 druggable genes were curated from the Drug-Gene Interaction Database and established literature. These were cross-referenced with cis-eQTL and cis-pQTL datasets, encompassing 870,655 and 114,281 SNPs for blood, respectively, alongside 2379 shared SNPs across adipose, skeletal muscle, and heart tissues. Matched instrumental variables were integrated with hypothermia GWAS summary statistics for two-sample Mendelian randomization (MR) and Bayesian colocalization. Transcriptomic differential expression analysis (DEA) was subsequently conducted as an exploratory analysis of cold-exposure-associated expression changes. Database-derived compound annotations were systematically re-evaluated according to target specificity, established pharmacological mechanism, and concordance with the direction of the MR estimates. RESULTS: Among 671 gene-level MR tests, 36 genes reached nominal significance, whereas only ABCC8 remained significant after FDR correction. Colocalization was evaluable for 8 of these 36 genes, and 4 loci (COL18A1, SLC1A7, ADIPOQ, and MERTK) met the prespecified PP.H4>0.90 threshold. The remaining 28 loci were not evaluable because sufficient overlapping regional variants were unavailable after harmonization. Transcriptomic analysis identified altered expression of SLC1A3 and SLCO4A1 under cold exposure, although these findings did not directly validate the colocalization-supported loci. Re-evaluation of database-derived compound annotations did not identify any direct, selective, and directionally concordant drug-repurposing candidate for hypothermia. CONCLUSIONS: COL18A1, SLC1A7, ADIPOQ, and MERTK showed colocalization support among the 8 evaluable nominal MR-associated loci. Because colocalization coverage was limited, these genes should be regarded as preliminary candidate loci rather than established therapeutic targets. The pharmacological annotations were indirect, non-selective, unsupported, or directionally inconsistent and should be interpreted solely as hypothesis-generating information.

Bayesian colocalization↗

PubMed2025

Stereo-cell: Spatial enhanced-resolution single-cell sequencing with high-density DNA nanoball-patterned arrays.

Single-cell sequencing technologies have advanced our understanding of cellular heterogeneity and biological complexity. However, existing methods face limitations in throughput, capture uniformity, cell size flexibility, and technical extensibility. We present Stereo-cell, a spatial enhanced-resolution single-cell sequencing platform based on high-density DNA nanoball (DNB)-patterned arrays, which enables scalable and unbiased cell capture at a wide input range and supports high-fidelity transcriptome profiling. Stereo-cell further allows integration with imaging-based modalities and multiomics strategies, including immunofluorescence and epitope profiling. This platform is also compatible with profiling extracellular vesicles, microstructures, and large cells, whereas its spatial resolution facilitates in situ analysis of cell-cell interactions, cellular microenvironments, and subcellular transcript localization. Together, Stereo-cell provides a flexible framework for expanding single-cell research applications.

Animals↗

PubMed2026

Single-cell and spatial transcriptomics define a progenitor subpopulation and fibroinflammatory niche at the leading edge of parathyroid carcinoma.

Parathyroid carcinoma (PC) is a rare but clinically aggressive endocrine malignancy with limited treatment options and a poorly defined tumor microenvironment (TME). To elucidate its cellular heterogeneity and spatial architecture, we integrated single-cell and spatial transcriptomic profiling with whole-exome sequencing and multiplex immunohistochemistry on eight parathyroid neoplasm specimens, including PC, parathyroid adenoma, and atypical parathyroid tumor. We identified a distinct progenitor-like endocrine subpopulation (Ca-1) enriched in CDC73-mutant PC, exhibiting stem-like properties, elevated cell cycle activity, and pronounced genomic instability. Spatial mapping revealed that Ca-1 cells preferentially localize at the leading edge, forming a fibroinflammatory niche characterized by the enrichment of inflammatory cancer-associated fibroblasts (iCAFs) and SPP1+ macrophages. Within this niche, the dipeptidyl peptidase 4 (DPP4) is selectively expressed in Ca-1 cells and iCAFs, implicating a potential paracrine axis driving stromal remodeling and immunosuppression. These findings suggest that a spatially organized ecosystem may promote PC progression through TME remodeling and highlight the DPP4-CXCL2 axis as a candidate pathway for future investigation in aggressive parathyroid neoplasms.

Humans↗

PubMed2003

Optimized protocol for linear RNA amplification and application to gene expression profiling of human renal biopsies.

Gene expression analysis using high-density cDNA or oligonucleotide arrays is a rapidly emerging tool for transcriptomics, the analysis of the transcriptional state of a cell or organ. One of the limitations of current methodologies is the requirement of a relatively large amount of total or polyadenylated RNA as starting material. Standard array hybridization protocols require 5-15 micrograms labeled RNA. To obtain these quantities from small amounts of starting RNA material, RNA can be amplified in a linear fashion. Here we introduce an optimized protocol for rapid and easy-to-use amplification of as little as 1 ng total RNA. Our analysis shows that this method is linear and highly reproducible and that it preserves similarities as well as dissimilarities between normal and disease-related samples. We applied this technique to the RNA expression profiling of human renal allograft biopsies with normal histology and compared them to the profiles of renal biopsies with histological evidence of chronic transplant nephropathy or chronic rejection. Among others, complement component C1r was found to be significantly up-regulated in chronic rejection and chronic transplant nephropathy biopsies compared to normal samples, while fructose-1,6-biphosphatase showed lower-than-normal expression.

Gene Expression Profiling↗

PubMed2026

Multi-omics integration uncovers epigenetic control of metabolic reprogramming in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by the absence of estrogen, progesterone, and HER2 receptors, limiting effective targeted therapies. Increasing evidence suggests that metabolic reprogramming, a hallmark of TNBC progression, is driven by underlying epigenetic mechanisms such as DNA methylation. The represented study performed an integrative analysis of transcriptomic (RNA-seq) and methylome data to uncover the metabolic-epigenetic interplay in TNBC. Differential gene expression analysis using DESeq2 revealed significant dysregulation of key metabolic genes, including upregulation of genes encoding glycolytic and serine biosynthesis enzymes and downregulation of metabolic tumor suppressors. Genome-wide methylation profiling identified extensive cytosine-phosphate-guanine (CpG) hypermethylation events associated with transcriptional repression, particularly in promoter regions. Integrative analysis pinpointed a subset of metabolism-related genes exhibiting both differential expression and methylation, such as FBP1, RASSF1A, and PHGDH. Pathway enrichment analysis highlighted aberrations in glycolysis/gluconeogenesis, fatty acid metabolism, and one-carbon pathways (adjusted p < 0.01). Importantly, TNBC patients with hypermethylated metabolic gene signatures displayed significantly shorter overall survival (log-rank p < 0.05). These findings reveal that DNA methylation-driven metabolic dysregulation contributes to TNBC aggressiveness and may provide novel biomarkers and therapeutic targets at the metabolic-epigenetic interface.

Humans↗

PubMed2002

Bacillus subtilis functional genomics: genome-wide analysis of the DegS-DegU regulon by transcriptomics and proteomics.

The DegS-DegU two-component regulatory system of Bacillus subtilis controls various processes that characterize the transition from the exponential to the stationary growth phase, including the induction of extracellular degradative enzymes, expression of late competence genes and down-regulation of the sigma(D) regulon. The degU32(Hy) mutation stabilizes the phosphorylated form of DegU (DegU-P), resulting in overproduction of several extracellular degradative enzymes. In this study, the pleiotropic DegS-DegU regulon was characterized by combining proteomic and transcriptomic approaches. A comparative analysis of wild-type B. subtilis and the degU32(Hy) mutant grown in complex medium was performed during the exponential and in the stationary growth phase. Besides genes already known to be under the control of DegU-P, novel putative members of this regulon were identified. Although the degU32(Hy) mutant is assumed to contain high levels of phosphorylated DegU in the exponential as well as in the stationary growth phase, many genes known to be positively regulated by DegU-P did not show enhanced expression in the mutant strain during exponential growth. This is consistent with the fact that most genes belonging to the DegS-DegU regulon are subject to multiple regulation; this is also reflected in the strong stationary-phase induction of these genes in the mutant strain. As expected, during the exponential growth phase, the sigma(D) regulon was expressed at significantly lower levels in the degU32(Hy) mutant than in the wild type.

Bacillus subtilis↗

PubMed2026

scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.

BACKGROUND: Existing virtual perturbation methods can often infer directional changes by comparing predicted post-perturbation expression profiles with control cells. However, workflows that directly return direction-specific downstream candidate genes together with confidence scores, evidence support and interpretable summaries remain limited. We developed scGPA, an LLM-assisted workflow system for directional single-cell virtual gene perturbation analysis. METHODS: scGPA starts from raw single-cell RNA sequencing data and performs quality control, normalization, dimensionality reduction, clustering and cell-group selection. It then constructs cell-group-specific wild-type regulatory networks using repeated subsampling, principal component regression (PCR)/Ridge-based network inference and CP tensor denoising. Based on these networks, scGPA simulates dose-aware virtual knockdown of the target gene and applies signed perturbation propagation to estimate the magnitude and direction of downstream transcriptional responses. LLM assistance is used for marker-based cell-type annotation, evidence-guided candidate prioritization and user-facing biological summarization. RESULTS: We benchmarked scGPA across five public Perturb-seq datasets and compared its performance with GEARS, scGPT and a random baseline. The overall correct prediction rate of scGPA was 23.0%, exceeding those of GEARS (20.7%), scGPT (15.1%) and the random baseline (13.6%). These results indicate that scGPA achieved a higher correct prediction rate than the two comparator models and the random baseline. We subsequently evaluated scGPA using a public osteosarcoma single-cell dataset and performed qRT-PCR validation in 143B osteosarcoma cells. Among genes with significant experimental changes, scGPA achieved a directional concordance of 76.9%. When all tested downstream genes were counted, 37.0% were directionally correct, 51.9% showed no significant change and 11.1% changed in the opposite direction. CONCLUSIONS: scGPA provides a practical workflow system for predicting and prioritizing direction-specific downstream transcriptional responses after target-gene perturbation. By integrating single-cell regulatory network inference, signed virtual perturbation and LLM-assisted interpretation, scGPA supports target-gene function inference and downstream mechanistic investigation from single-cell transcriptomic data.

Single-Cell Gene Expression Analysis↗

PubMed2025

Genome-wide identification of potassium transporters and channels in Malus domestica genome.

Potassium (K+) is an essential nutrient for plants. It contributes to most physiological and biochemical pathways for plant metabolism, growth, and development. It is the most available plant nutrient, comprising 10–15% of plant weight. Plants have a sophisticated system of K+ transporters and channels for distribution in plant body. Apple is one of the most consumed fruits in the world. Its fruit quality and yield are positively affected by K+. However, limited information is available about K+ transport systems in Apple. In this study, 47 candidate genes (26 K+ transporters and 21 K+ channels) have been identified in Apple (Malus domestica) genome. The phylogenetic comparisons with other plants (Glycine max, Arabidopsis thaliana, and Oryza sativa) indicated that the K+ transport system is much conserved among different plants. The analysis of Gene structure showed the presence of specific introns and exon patterns for these gene families. Transcriptomic data analysis and RT-qPCR demonstrated significant variations in the transcript abundance of these genes in response to abiotic stresses. The current project represents the first report about the K+ transport system in Apple. Therefore, it may act as a starting point for further functional characterizations.

Malus↗

PubMed2026

Integrative cross-tissue transcriptome-wide association and metabolomic analysis reveals novel genetic risk loci for aortic aneurysm.

BACKGROUND: Aortic aneurysm (AA) is a life-threatening cardiovascular condition with a strong genetic component, however, its molecular mechanisms remain poorly understood. Although genome-wide association studies (GWAS) have identified numerous risk loci, most prior studies have investigated genetic and metabolic factors separately, leaving the causal pathways from genetic variants to disease largely unexplored. METHODS: We established an integrative framework combining cross-tissue transcriptome-wide association studies (TWAS) with metabolomic mediation analysis. First, we integrated GWAS data from FinnGen R12 with multi-tissue expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression Project (GTEx) V8, then performed cross-tissue TWAS using the Unified Test for MOlecular SignaTures (UTMOST) and single-tissue validation with the Functional Summary-based Imputation (FUSION) to prioritize susceptibility genes. Second, we applied Mendelian randomization (MR), colocalization, and Fine-mapping Of CaUsal gene Sets (FOCUS) to assess causality and identify high-confidence genes. Third, we performed metabolite mediation analysis to uncover metabolic pathways linking genetic variants to disease risk. Finally, we validated key findings in mouse models of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) and Western blotting. RESULTS: We identified multiple novel susceptibility genes for AA and its subtypes. Key genes included ADH family members (ADH1A, ADH1B, ADH4, ADH6) and ZNF827, which showed cross-subtype associations with strong colocalization evidence in vascular tissues. Metabolite mediation analysis revealed significant pathways involving N-acetylphenylalanine and methionine sulfoxide. Functional enrichment revealed distinct biological mechanisms: AA and AAA were primarily associated with metabolic pathways, whereas TAA-related genes were enriched in developmental and contractile processes. PheWAS indicated no significant off-target associations. Critically, experimental validation in mouse models confirmed significant upregulation of ZNF827 in TAA and ADH6 in AAA at both mRNA and protein levels, corroborating the genetic predictions. CONCLUSION: This integrated cross-omics analysis identifies novel genetic loci and, crucially, uncovers specific nutrient-related metabolic pathways that mediate genetic risk. These findings provide a mechanistic basis for future nutritional and metabolic intervention studies in AA and its subtypes.

MAGMA↗

PubMed2026

Single-Cell Transcriptome-Wide Mendelian Randomization and Colocalization Uncover Potential Immunocytes-Related Therapeutic Targets for Obesity.

Weight-loss treatment is crucial for individuals with obesity to prevent various complications. The role of Immune cells in obesity has been recently recognized, whereas its translation into therapy requires identifying key target genes. We performed Mendelian randomization (MR) analysis to assess causal relationships between expression quantitative trait loci (eQTL) of 14 immune cells and obesity-related traits (obesity, body mass index and body fat percentage), and validated the results in colocalization analysis. For the putative causal genes identified by the MR and colocalization analyses, we conducted pathway enrichment, differential expressed gene (DEG) analysis and search of druggable evidence, and utilized a Tier system to prioritize drug targets for obesity. MR and colocalization evidence was observed for 1630 genes associated with one or more obesity-related traits, mainly expressed in CD4+ naive/central memory T cells and enriched in antigen processing and presentation pathways. Forty-one genes showed causal relationship with all three outcomes, among which 19 genes have not been reported for obesity previously. DEG analysis using single-cell RNA sequencing data of blood or adipose tissue indicated that the differential expression of UBE2Z in monocytes, ZCCHC7 in T cells, and FNBP4 in B cells between lean and obese individuals were consistent with the MR results. By searching drug-gene interaction databases, we found targeted drugs for PYGB and PRUNE1, and PYGB was the top gene ranked in the Tier system. This study provides evidence for the involvement of immune cells in obesity, and the potential cell-specific, immune-related targets for obesity treatment.

Obesity↗

PubMed2026

An immune exhaustion signature predicts prognosis and identifies patients with diffuse large B-cell lymphoma (DLBCL) who derive preferential benefit from chimeric antigen receptor (CAR)-T cell therapy.

BACKGROUND: The tumor microenvironment (TME) is a key determinant of prognosis in diffuse large B-cell lymphoma (DLBCL). While T-cell exhaustion is implicated in therapeutic failure, its precise molecular hallmarks and utility for predicting response to modern immunotherapies, such as chimeric antigen receptor (CAR)-T cell therapy, remain unclear. METHODS: We performed an integrative analysis of transcriptomic and clinical data from multiple DLBCL cohorts (The Cancer Genome Atlas [TCGA], GSE181063, GSE10846, GSE248835, GSE182434). We used unsupervised clustering, exploratory analysis of single-cell RNA sequencing data, and the least absolute shrinkage and selection operator for variable selection (LASSO-Cox) regression to characterize the exhausted TME, construct a prognostic model, and evaluate its predictive value for CAR-T cell therapy. The model's dynamic behavior was assessed in a proof-of-concept longitudinal cohort of patients treated with the T-cell-engaging bispecific antibody glofitamab. RESULTS: We identified a "high-exhaustion" subtype associated with significantly poorer overall survival (OS; log-rank P = 0.016). Based on this, we developed a five-gene immune exhaustion-Related Prognostic Score (IERPS) that served as a robust independent predictor of poor OS across multiple cohorts. Critically, in a cohort of 256 relapsed/refractory patients, the IERPS was strongly prognostic for event-free survival (EFS) in the standard-of-care (SOC) arm (HR = 2.02, 95% confidence interval [95% CI]: 1.07-3.81, P = 0.029) but lost prognostic significance in the CAR-T arm (HR = 0.70, 95 % CI: 0.35-1.40, P = 0.314). This significant interaction suggests that CAR-T cell therapy may abrogate the poor prognosis associated with a high IERPS. Biologically, exploratory single-cell analysis (n = 4 samples) defined the high-IERPS state by hallmarks of classical T-cell exhaustion, and a descriptive case study showed the score dynamically tracked clinical response to glofitamab. CONCLUSIONS: A state of active T-cell exhaustion and a suppressive TME drive the adverse immune phenotype in DLBCL. Our IERPS model captures this dysfunctional state, acting as a powerful prognostic tool and, more importantly, as a potential predictive biomarker to identify high-risk patients who appear to overcome their inherently poor prognosis through CAR-T cell therapy.

Biomarkers↗

PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗

PubMed2002

Applications for microarrays in renal biology and medicine.

Groundbreaking recent developments, such as the near completion of human and mouse genome sequencing efforts and the emergence of robust microarray (gene chip) technologies, enabling comprehensive analysis of transcriptomes, provide new opportunities of unprecedented scale for researchers of kidney biology and disease. Combined with advanced computational and mathematical approaches for microarray data analysis, microarray applications promise to revolutionize our understanding of molecular mechanisms of kidney development and renal pathogenesis. New knowledge in this field will facilitate new approaches for molecular diagnostics, drug discovery, and eventually "personalized" renal medicine. In this review, we outline current and future research applications of microarray and computational approaches in renal biology and disease. We describe basic steps in microarray data analysis and introduce advanced computational approaches to optimize data mining of vast microarray datasets.

Genomics↗