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A comparison of RNA-Seq data preprocessing pipelines for transcriptomic predictions across independent studies
BackgroundRNA sequencing combined with machine learning techniques has provided a modern approach to the molecular classification of cancer. Class...
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Orthogonal multimodality integration and clustering in single-cell data
Multimodal integration combines information from different sources or modalities to gain a more comprehensive understanding of a phenomenon. The...
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Representation and quantification of module activity from omics data with rROMA
The efficiency of analyzing high-throughput data in systems biology has been demonstrated in numerous studies, where molecular data, such as...
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A minimal metadata set (MNMS) to repurpose nonclinical in vivo data for biomedical research
Although biomedical research is experiencing a data explosion, the accumulation of vast quantities of data alone does not guarantee a primary...
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Efficient and Reliable Data Management for Biomedical Applications
This chapter discusses the challenges and requirements of modern Research Data Management (RDM), particularly for biomedical applications in the...
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RNA-clique: a method for computing genetic distances from RNA-seq data
BackgroundAlthough RNA-seq data are traditionally used for quantifying gene expression levels, the same data could be useful in an integrated...
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Domain adaptation for supervised integration of scRNA-seq data
Large-scale scRNA-seq studies typically generate data in batches, which often induce nontrivial batch effects that need to be corrected. Given the...
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Introduction to R for Microbiome Data
This chapter first introduces some useful R functions and R packages for microbiome data. Then it illustrates some specifically designed R packages...
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The effect of data transformation on low-dimensional integration of single-cell RNA-seq
BackgroundRecent developments in single-cell RNA sequencing have opened up a multitude of possibilities to study tissues at the level of cellular...
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The 15-minute city quantified using human mobility data
Amid rising congestion and transport emissions, policymakers are embracing the ‘15-minute city’ model, which envisions neighbourhoods where basic...
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Greengenes2 unifies microbial data in a single reference tree
Studies using 16S rRNA and shotgun metagenomics typically yield different results, usually attributed to PCR amplification biases. We introduce...
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Modeling spatial patterns of longleaf pine needle dispersal using long-term data
BackgroundPredicting patterns of fire behavior and effects in frequent fire forests relies on an understanding of fine-scale spatial patterns of...
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Evaluating spatially variable gene detection methods for spatial transcriptomics data
BackgroundThe identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data...
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Optimized model architectures for deep learning on genomic data
The success of deep learning in various applications depends on task-specific architecture design choices, including the types, hyperparameters, and...
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Characterizing human postprandial metabolic response using multiway data analysis
IntroductionAnalysis of time-resolved postprandial metabolomics data can improve our understanding of the human metabolism by revealing similarities...
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ATAC-seq Data Processing
ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) has gained wide popularity as a fast, straightforward, and efficient way of...
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Brain age prediction across the human lifespan using multimodal MRI data
Measuring differences between an individual’s age and biological age with biological information from the brain have the potential to provide...
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Graph embedding on mass spectrometry- and sequencing-based biomedical data
Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction,...
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Clustering on hierarchical heterogeneous data with prior pairwise relationships
BackgroundClustering is a fundamental problem in statistics and has broad applications in various areas. Traditional clustering methods treat...
