Dimensionality reduction and visualization methods for single-cell omics are optimized for computational efficiency and accuracy

The gap

Evidence profile

Sourced from the future work and synthesized of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 3 journals. Those papers have been cited 178 times in total.

Supporting evidence — 3 representative gaps

• Omics Data Integration: Focusing on Molecular Biomarkers for Cancers and Diseases (2026) · Biomolecules · doi

  In the future, emerging technologies such as spatial transcriptomics, single-cell multi- omics, next-generation proteomics, and multimodal machine learning will facilitate the accelerating approach to biomarker discovery and improve our understanding of disease biology. The future should make extensive use of a multi-resolution framework for syn- thesizing molecular, structural, cellular, and clinical information and insights to obtain a broader perspective of disease mechanisms. Integrative omics studies will contribute to the resolution of therapeutic targets for these complex diseases by uncovering the molecular origins of such diseases. The aim is to increase the chances for patients’ recovery and improve treatment development for better personalized therapeutic strategies. Meanwhile, integrative omics is also slowly beginning to redefine translational research itself, promot- ing strong communication between clinicians, molecular biologists, bioinformaticians, and data scientists. Interdisciplinary collaboration will be key to turning massive biological registries into clinically relevant knowledge and scientific advances. Beyond biomarker discovery, incorporating multi-omics would allow for better early detection of disease, advanced patient stratification, predicting disease progression, identifying potential ther- apeutic intervention, and minimizing invasive monitoring. However, the full realization of these technologies will also rely not only on technological advancement but also on the development of solid computational workflows, standardized analytical pipelines, accessible data-sharing resources, and well-characterized validation cohorts. Long-term cross-sectional collaboration is, therefore, essential for the successful clinical translation of multi-omics findings to precision medicine. It is with gratitude that I acknowledge all authors, reviewers, and the editorial staff of Biomolecules for their dedication and contribution to this Special Issue. Their collective efforts demonstrate the transformative potential of omics integration for understanding complex diseases and advancing precision medicine. Author Contributions: Conceptualization, C.F.; writing—original draft preparation, C.F. and L.D.S.; writing—review and editing, C.F. and L.D.S.; supervision, C.F.; project administration, C.F. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflicts of interest. https://doi.org/10.3390/biom16060847 Biomolecules 2026, 16, 847 6 of 6

  generalfuture work
  Keywords: omics multi disease molecular diseases authors future technologies biomarker discovery improve understanding resolution clinical integrative
• Characterizing Fibroblast Heterogeneity in Diabetic Wounds Through Single-Cell RNA-Sequencing (2024) · Biomedicines · cited 12× · doi

  Within the last 10 years, scRNAseq technologies have improved exponentially, stim- ulating a parallel explosion of interest in using single-cell sequencing to study biological questions. The future of scRNAseq is likely to see continued efforts to make single-cell sequencing more affordable on a per cell basis, an ever-increasing number of cells se- quenced in a single study, improved transcript and isoform coverage, and a corresponding amassment of publicly available scRNAseq data. In response, we anticipate advances in bioinformatic and computational approaches to leverage the growing volume of data. This is already beginning to happen in the growth of published meta-analyses, as discussed above, and the development of interactive web apps to share and display datasets [72]. We hope that improvements in data storage and manipulation efficiency, computational power, and statistical methods will make analysis of these datasets easier and more accessible to a wider population of scientists. Finally, single-cell multi-omics is a growing trend that we expect to continue to gain traction, integrating scRNAseq with proteomic, epigenomic, and genomic data to better understand cell states and activities [73]. Empowered by rapidly advancing single-cell technologies, more studies remain to be conducted to understand fibroblasts and their roles in diabetic wounds. To isolate fibroblasts, either experimentally or in silico, further work is needed in the search for markers that define the cell type as a whole and its subpopulations. This information would aid fibroblast research broadly, generate signatures for disease diagnosis and prognosis, and deliver therapeutic targets. Though Buechler et al. have established a foundational understanding of the fibrob- last lineage, how fibroblasts achieve both general and specialized functions is unclear. Questions remain as to what intercellular signaling mechanisms and transcriptional or epigenetic programs stimulate differentiation from the universal type to specialized or activated states and what other subtypes might still be missing in existing data. Generation of more modern scRNAseq datasets that are not biased by cell sorting is needed to supply a broad search space within which yet unidentified fibroblast subtypes might be uncovered and their lineage could be traced in detail. For diabetic wound healing, amassment of sufficient numbers of scRNAseq datasets would enable more powerful meta-analyses to identify fibroblast subtypes important for dysfunctional wound healing in diabetic skin. An example workflow for scRNAseq of diabetic wounds using a droplet- based method, highlighting optional steps for cell sorting and in silico fibroblast isolation, is shown in Figure 1. Figure 1. Illustrated Workflow for scRNAseq of Diabetic Wounds. An example workflow for single- cell RNA-seq of diabetic skin wounds using a droplet-based method such as 10x Genomics Chromium, from specimen harvest to downstream analysis o

  generalfuture work
  Keywords: cell scrnaseq single diabetic datasets wounds fibroblast using fibroblasts subtypes workflow within last technologies improved
• A fast, scalable and versatile tool for analysis of single-cell omics data (2024) · Nature Methods · cited 166× · doi

  Dimensionality reduction and visualization methods for single-cell omics are optimized for computational efficiency and accuracy on individual data modalities, but none of these studies addresses how to jointly reduce and visualize multiple omics modalities (e.g., scRNA-seq and scATAC-seq from the same cells) while preserving cross-modal relationships and enabling discovery of multimodal cell states.

  generalsynthesizedevidence 5/5
  Keywords: dimensionality reduction visualization methods single-cell omics optimized computational

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