Data integration

Many methods for single cell data integration have been developed, though mosaic integration remains challenging. Here the authors present scMoMaT, a mosaic integration method for single cell multi-modality data from multiple batches, that jointly learns cell representations and marker features across modalities for different cell clusters, to interpret the cell clusters from different modalities.

Methods that perform data integration are needed to analyse spatial transcriptomics data from multiple tissue slides. Here, the authors present PRECAST, an efficient data integration method for multiple spatial transcriptomics datasets with complex batch or biological effects between slides.

The mechanism by which DNA methylation might affect complex traits is not well understood. Here, the authors use Mendelian randomization to reveal a substantial role of transcript levels in mediating DNA methylation effects on complex traits and diseases.

Methods for jointly analysing the different spatial data modalities in 3D are lacking. Here the authors report the computational framework STACI (Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data) which they apply to an Alzheimer’s disease mouse model.

Predicting topological structures from Hi-C data provides insight into comprehending gene expression and regulation. Here, the authors present RefHiC, an attention-based deep learning framework that leverages a reference panel of Hi-C datasets to assist topological structure annotation from a given study sample.

Integrating heterogeneous single-cell multi-omics as well as spatially resolved transcriptomic data remains a major challenge. Here the authors report a unified single-cell data integration framework using an unbalanced optimal transport-based deep network.

As the throughput of single-cell RNA-seq studies increases, there is a need for tools that can make the data analysis steps more streamlined and convenient. Here, the authors develop UniverSC, a tool that unifies single-cell RNA-seq analysis workflows and also facilitates their use for non-experts.

The 1+ million publicly-available human –omics samples currently remain acutely underused. Here the authors present an approach combining natural language processing and machine learning to infer the source tissue of public genomics samples based on their plain text descriptions, making these samples easy to discover and reuse.

The hypoxia response pathway can counter pathological damage caused by low oxygen availability. Here the authors employ a multiomics approach to show how the pathway reprograms metabolism towards gluconeogenesis to combat oxidative stress.

Integrative analyses of single-cell datasets are facing new challenges as data size and complexity grow. Here the authors present SCALEX, which projects cells from different datasets into a common latent space, allowing accurate online integration as well as cross-referencing with atlas-scale data.

Multi-view graph approaches could enhance the analysis of tissue heterogeneity in spatial transcriptomics. Here, the authors develop the Spatial Transcriptomics data analysis by Multiple View Collaborative-learning - stMVC - framework, and apply it to detect spatial domains and cell states in brain and tumor tissues.

Protein kinase-mediated phosphorylation plays a critical role in many biological processes. Here the authors develop a trans-omics-based algorithm called Central Kinase Inference to integrate quantitative transcriptomic and phosphoproteomic data, finding that PIM1 promotes hepatic conversion by suppressing reprogramming-induced ferroptosis and cell cycle arrest.

The thymus generates all T cells, including those that underly autoimmune diseases. Here, using deep sequencing, the authors profile human medullary thymic epithelial cells and establish a web portal to query their transcriptome, which may serve as a tool to help identify the drivers of autoimmunity.

Myasthenia Gravis and thymoma are frequently associated with patients suffering from both diseases. Here the authors perform single cell sequencing of thymoma and find that there are autoimmune antigens such as neuromuscular proteins expressed aberrantly in neuromuscular mTECs in patients with both diseases.

Some cancer patients with impaired HLA-I still respond to immunotherapy. Here the authors combine a cytotoxic gene signature from CD4+ and CD8+ T cells with tumor mutational burden to predict immunotherapy response in NSCLC patients, including those with HLA-LOH.

Many transcriptomic profiles have been deposited in public archives but are underused for the interpretation of experiments. Here the authors report GenomicSuperSignature for interpreting new transcriptomic datasets through comparison to public archives, without high-performance computing requirements.

Dataset integration is common practice to overcome limitations in statistically underpowered omics datasets. Here the authors present “HarmonizR”, a tool for missing data tolerant experimental variance reduction in large, integrated but independently generated datasets without data imputation, adjustable for individual dataset modalities, correction algorithm, and user preferences.

Cerebral organoids can be used to gain insights into neuropsychiatric disorders. Here the authors carry out RNAseq characterization from organoids derived from donors with autism spectrum disorder to identify associated cell type specific driver genes.

Here the authors provide a multi-omic study of the nucleosome landscape in LNCaP cells and observe nine functional nucleosome states each with characteristic nucleosome footprints. Upon androgen stimulation, they observed changes in these nucleosome states accompanied by changes in binding and function of pioneer factors, including GATA2.

High-resolution reconstruction of spatial chromosome organisation is in demand. Here the authors report FLAMINGO, for reconstructing high-resolution 3D Genome Organisation from HiC data which they use to generate both 5 kb and 1 kb-resolution 3D chromosomal structures for the human genome.

3’ untranslated regions (3’UTRs) play a crucial role in regulating gene expression, but our 3’UTR catalogue is incomplete. Here, the authors develop a machine learning-based framework to predict previously unannotated 3’UTRs in 39 human tissues.

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. Here, the authors present a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques.

A lossless, one-shot and privacy-preserving distributed algorithm was revealed for fitting linear mixed models on multi-site data. The algorithm was applied to a study of 120,609 COVID-19 patients using only minimal aggregated data from each of 14 sites.

Challenges in batch normalization and data integration limit the comparison of existing mass cytometry datasets. Here, the authors report CytofIn that can integrate mass cytometry datasets from the public domain and reveal cellular features associated with immune oncology by analyzing five public cancer datasets.

The interpretation of somatic variants in cancer is challenging due to the scale and complexity of sequencing data. Here, the authors present PORI, an open-source framework for interpreting somatic variants in cancer using graph knowledge base tools, automated reporting, and manual curation.

Single-cell genomic technologies present unique data integration challenges. Here the authors introduce an integrative nonnegative matrix factorization algorithm that incorporates features unshared between datasets when performing dataset integrations, improving integration results for spatial transcriptomic, cross-modality, and cross-species data.

Sex modifies Alzheimer’s Disease vulnerability, but the reasons for this are largely unknown. Here, the authors utilize two independent electronic medical record systems to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations.

Ageing is a known risk factor in the development of cancers, but its association with molecular alterations is not fully explored. Here, the authors analyse pan-cancer age-associated molecular alterations in datasets from the TCGA, PCAWG and AACR-GENIE projects and identify prognostic biomarkers.

A lot of cancer patients are not responsive to anti-PD1 therapy. Here, the authors develop a network approach to identify genes, pathways and potential therapeutic combinations and develop an MHC-I gene immunoscore associated with tumour response to anti-PD1.

Prioritizing drug repurposing candidates for downstream studies remains challenging. Here, the authors present a high-throughput approach to identify and validate drug repurposing candidates, integrating human gene expression, drug perturbation, and clinical data from publicly available resources.

Individual studies have been underpowered to draw clear associations between clonal heterogeneity and response to therapy in acute myeloid leukemia (AML). Here, the authors aggregate multiple AML cohorts and are able to correlate the clonal abundance of somatic mutations with clinical outcomes and drug sensitivity.

There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. Here, the authors close this gap with NeDRex, an integrative and interactive platform.

The location and timing of metastasis are still fundamentally unpredictable. Here the authors present the Metastatic Network model, a machine learning framework that integrates clinical data and DNA alterations to predict the risk of metastasis to specific organs as well as clinical outcomes

Calcium imaging is valuable for understanding neuro and cell biology, but is challenging to analyze, organize, and access. Here, the authors present an efficient, expandable and user-friendly platform, which encapsulates the entire analysis process all to way to interactive visualizations.

Despite the clear causal relationship between genotype and phenotype in rare diseases, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, the authors introduce a network approach for evaluating the impact of rare gene defects across biological scales.

Triple negative breast cancer can be divided into additional subtypes. Here, using omics analyses, the authors show that in the mesenchymal subtype expression of MHC-1 is repressed and that this can be restored by using drugs that target subunits of the epigenetic modifier PRC2.

While transcriptomics have enhanced our understanding for cancer, spatial transcriptomics enable the characterisation of cellular interactions. Here, the authors integrate single cell data with spatial information for HER2 + tumours and develop tools for the prediction of interactions between tumour-infiltrating cells.

The number of single-cell RNA-seq datasets generated is increasing rapidly, making methods that map cell types to well-curated references increasingly important. Here, the authors propose an accurate method for mapping single cells onto a reference atlas in seconds.

The increasing scale and scope of biomedical data is generating tremendous opportunities for improving health outcomes, but also raises new challenges ranging from data acquisition and storage to data analysis and utilization. To meet these challenges, the authors develop the Personal Health Dashboard, which provides an end-to-end solution for deep biomedical data analytics.

The vertebrate limb bud is a paradigm to uncover the fundamental mechanisms that govern embryogenesis and evolutionary diversification. Here the authors compare mouse and chicken limb bud development to study the impact of genome evolution on conserved and divergent gene regulatory interactions.

Identifying the molecular mechanisms of response to systemic therapy in prostate cancer remains crucial. Here, the authors apply single cell-ATAC and RNAseq to models of early treatment response and resistance to enzalutamide and identify chromatin and gene expression patterns that can predict treatment response.

Patient-derived xenograft models (PDX) have been extensively used to study the molecular and clinical features of cancers. Here the authors present a cohort of 536 PDX models from 25 cancers, as well as their genomic and evolutionary profiles and their suitability for clinical trials.

Alcohol use disorder and drinks per week both have been studied genetically and have different correlations with psychiatric diseases. Here the authors integrate multi-omics data to identify unique and shared variants, genes and pathways for alcohol use disorder and drinks per week.

Local gene co-expression is found throughout the genome, but systematic analysis of these co-expressed genes is needed. Here, the authors identify local co-expressed genes in 49 tissues and characterize the genetic variants which may affect their expression and contribute to disease.

Our ability to interpret single-cell multivariate signaling responses is still limited. Here the authors introduce fractional response analysis (FRA), involving fractional cell counting, capable of deconvoluting heterogeneous multivariate responses of cellular populations.

Single-nucleotide variants in enhancers or promoters may affect gene transcription by altering transcription factor binding sites. Here the authors present a meta-analysis empowered by a new statistical method covering thousands of ChIP-Seq experiments resulting in the identification of more than 500 thousand allele-specific binding (ASB) events in the human genome.

The integration and interpretation of different omics data types is an ongoing challenge for biologists. Here, the authors present a web-based, interactive tool called multiSLIDE for the visualization of protein, phosphoprotein, and RNA data presented as interlinked heatmaps.

Epigenetic and transcriptional dynamics are critical for both tissue homeostasis and injury response in the kidney. Leveraging a single cell multiomics atlas of the developing mouse kidney, the authors reveal key events in chromatin regulation and gene expression dynamics during postnatal development.