Integration of spatial and single-cell data across modalities with weakly linked features
Chen, S., Zhu, B., Huang, S. et al. Integration of spatial and single-cell data across modalities with weakly linked features. Nat Biotechnol 42, 1096–1106 (2024). https://doi.org/10.1038/s41587-023-01935-0
"Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori ‘linked’ features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section."
The tidyomics ecosystem: enhancing omic data analyses
Hutchison, W.J., Keyes, T.J., The tidyomics Consortium. et al. The tidyomics ecosystem: enhancing omic data analyses. Nat Methods 21, 1166–1170 (2024). https://doi.org/10.1038/s41592-024-02299-2
"Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations."
Dictionary of immune responses to cytokines at single-cell resolution
Cui, A., Huang, T., Li, S. et al. Dictionary of immune responses to cytokines at single-cell resolution. Nature 625, 377–384 (2024). https://doi.org/10.1038/s41586-023-06816-9
Large language model GPT-4 for cell type annotation
Hou, W., Ji, Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02235-4
A foundation model for single-cell multi-omics using generative AI
Cui, H., Wang, C., Maan, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02201-0
Harmonized single-cell perturbation data
Peidli, S., Green, T.D., Shen, C. et al. scPerturb: harmonized single-cell perturbation data. Nat Methods (2024). https://doi.org/10.1038/s41592-023-02144-y
"The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations."
Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis
Mitra, S., Malik, R., Wong, W. et al. Single-cell multi-ome regression models identify functional and disease-associated enhancers and enable chromatin potential analysis. Nat Genet (2024). https://doi.org/10.1038/s41588-024-01689-8
Multimodal spatiotemporal phenotyping of human retinal organoid development
Wahle, P., Brancati, G., Harmel, C. et al. Multimodal spatiotemporal phenotyping of human retinal organoid development. Nat Biotechnol 41, 1765–1775 (2023). https://doi.org/10.1038/s41587-023-01747-2
"In this study, we generated multiplexed protein maps over a retinal organoid time course and primary adult human retinal tissue...In addition, we generated a single-cell transcriptome and chromatin accessibility timecourse dataset and inferred a gene regulatory network underlying organoid development. We integrated genomic data with spatially segmented nuclei into a multimodal atlas to explore organoid patterning and retinal ganglion cell (RGC) spatial neighborhoods, highlighting pathways involved in RGC cell death and showing that mosaic genetic perturbations in retinal organoids provide insight into cell fate regulation."
MultiVI creates a joint representation allowing an analysis of all single cell omics modalities, even for cells for which one or more modalities are missing
Ashuach, T., Gabitto, M.I., Koodli, R.V. et al. MultiVI: deep generative model for the integration of multimodal data. Nat Methods 20, 1222–1231 (2023). https://doi.org/10.1038/s41592-023-01909-9
scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics
Song, D., Wang, Q., Yan, G. et al. scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01772-1
"We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools."
MISAR-seq for spatially resolved joint profiling of chromatin accessibility and gene expression
Jiang, F., Zhou, X., Qian, Y. et al. Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development. Nat Methods 20, 1048–1057 (2023). https://doi.org/10.1038/s41592-023-01884-1
"By applying MISAR-seq to the developing mouse brain, we study tissue organization and spatiotemporal regulatory logics during mouse brain development."
GPSA aligns spatial genomics data, allowing for downstream spatially aware analyses
Jones, A., Townes, F.W., Li, D. et al. Alignment of spatial genomics data using deep Gaussian processes. Nat Methods 20, 1379–1387 (2023). https://doi.org/10.1038/s41592-023-01972-2
"It remains difficult to precisely align spatial observations across slices, samples, scales, individuals and technologies. Here, we propose a probabilistic model that aligns spatially-resolved samples onto a known or unknown common coordinate system (CCS) with respect to phenotypic readouts (for example, gene expression). Our method, Gaussian Process Spatial Alignment (GPSA), consists of a two-layer Gaussian process: the first layer maps observed samples’ spatial locations onto a CCS, and the second layer maps from the CCS to the observed readouts. Our approach enables complex downstream spatially aware analyses that are impossible or inaccurate with unaligned data, including an analysis of variance, creation of a dense three-dimensional (3D) atlas from sparse two-dimensional (2D) slices or association tests across data modalities."
Accurately spot cell-free droplets, and provide noise-free scRNAseq quantification
Fleming, S.J., Chaffin, M.D., Arduini, A. et al. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat Methods 20, 1323–1335 (2023). https://doi.org/10.1038/s41592-023-01943-7
WGS and RNA-seq based machine learning pipeline for biomarker discovery and predictive analysis
William DeGroat, Dinesh Mendhe, Atharva Bhusari, Habiba Abdelhalim, Saman Zeeshan, Zeeshan Ahmed, IntelliGenes: A novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles, Bioinformatics, 2023;, btad755, https://doi.org/10.1093/bioinformatics/btad755
Identify single-nucleotide variant in scRNA-seq, snRNA-seq, snATAC-seq and scDNA-seq data
Dou, J., Tan, Y., Kock, K.H. et al. Single-nucleotide variant calling in single-cell sequencing data with Monopogen. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01873-x
??? Develop algorithms capable of detecting intronic variants for scRNA-seq, snRNA-seq, snATAC-seq and scDNA-seq data
SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors and links these enhancers to candidate target genes
Bravo González-Blas, C., De Winter, S., Hulselmans, G. et al. SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks. Nat Methods 20, 1355–1367 (2023). https://doi.org/10.1038/s41592-023-01938-4
"We exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. We use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state."
Seperate variations of gene expression enriched in treated single cells from those shared with controls
Weinberger, E., Lin, C. & Lee, SI. Isolating salient variations of interest in single-cell data with contrastiveVI. Nat Methods 20, 1336–1345 (2023). https://doi.org/10.1038/s41592-023-01955-3
GEARS integrates deep learning with a knowledge graph of gene–gene relationships to predict transcriptional responses to both single and multigene perturbations
Roohani, Y., Huang, K. & Leskovec, J. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat Biotechnol (2023). https://doi.org/10.1038/s41587-023-01905-6
Map single-cell fates in regeneration, reprogramming and disease without known directions
Lange, M., Bergen, V., Klein, M. et al. CellRank for directed single-cell fate mapping. Nat Methods 19, 159–170 (2022). https://doi.org/10.1038/s41592-021-01346-6
Expression quantitative trait locus analysis using single cell RNA-Seq data
Bryois, J., Calini, D., Macnair, W. et al. Cell-type-specific cis-eQTLs in eight human brain cell types identify novel risk genes for psychiatric and neurological disorders. Nat Neurosci 25, 1104–1112 (2022). https://doi.org/10.1038/s41593-022-01128-z