Benchmarking algorithms for single-cell multi-omics prediction and integration
Y Hu, S Wan, Y Luo, Y Li, T Wu, W Deng, C Jiang… - Nature …, 2024 - nature.com
The development of single-cell multi-omics technology has greatly enhanced our
understanding of biology, and in parallel, numerous algorithms have been proposed to …
understanding of biology, and in parallel, numerous algorithms have been proposed to …
DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype–phenotype prediction
Background Genotypes are strongly associated with disease phenotypes, particularly in
brain disorders. However, the molecular and cellular mechanisms behind this association …
brain disorders. However, the molecular and cellular mechanisms behind this association …
Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS
Integrating single-cell datasets produced by multiple omics technologies is essential for
defining cellular heterogeneity. Mosaic integration, in which different datasets share only …
defining cellular heterogeneity. Mosaic integration, in which different datasets share only …
Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning
Z Cai, S Apolinário, AR Baião, C Pacini… - Nature …, 2024 - nature.com
Integrating diverse types of biological data is essential for a holistic understanding of cancer
biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity …
biology, yet it remains challenging due to data heterogeneity, complexity, and sparsity …
Subsample ridge ensembles: Equivalences and generalized cross-validation
We study subsampling-based ridge ensembles in the proportional asymptotics regime,
where the feature size grows proportionally with the sample size such that their ratio …
where the feature size grows proportionally with the sample size such that their ratio …
scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data
Single-cell sequencing technology has enabled the characterization of cellular
heterogeneity at an unprecedented resolution. To analyze single-cell RNA-sequencing data …
heterogeneity at an unprecedented resolution. To analyze single-cell RNA-sequencing data …
Deep Learning in Single-Cell and Spatial Transcriptomics Data Analysis: Advances and Challenges from a Data Science Perspective
The development of single-cell and spatial transcriptomics has revolutionized our capacity to
investigate cellular properties, functions, and interactions in both cellular and spatial …
investigate cellular properties, functions, and interactions in both cellular and spatial …
High-dimensional logistic regression with missing data: Imputation, regularization, and universality
KA Verchand, A Montanari - arXiv preprint arXiv:2410.01093, 2024 - arxiv.org
We study high-dimensional, ridge-regularized logistic regression in a setting in which the
covariates may be missing or corrupted by additive noise. When both the covariates and the …
covariates may be missing or corrupted by additive noise. When both the covariates and the …
Cancer molecular subtyping using limited multi-omics data with missingness
Y Bu, J Liang, Z Li, J Wang, J Wang… - PLOS Computational …, 2024 - journals.plos.org
Diagnosing cancer subtypes is a prerequisite for precise treatment. Existing multi-omics data
fusion-based diagnostic solutions build on the requisite of sufficient samples with complete …
fusion-based diagnostic solutions build on the requisite of sufficient samples with complete …
Extrapolated cross-validation for randomized ensembles
Ensemble methods such as bagging and random forests are ubiquitous in various fields,
from finance to genomics. Despite their prevalence, the question of the efficient tuning of …
from finance to genomics. Despite their prevalence, the question of the efficient tuning of …