High-dimensional data, such as those generated by single-cell RNA sequencing (scRNA-seq), present challenges in interpretation and visualization. Numerical and computational methods for dimensionality reduction allow for low-dimensional representation of...
Read More »A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis
Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of ...
Read More »Webinar Recording Available – Combining scRNA-Seq and Flow Cytometry
Learn how to use Dimensionality Reduction, Clustering and Hierarchial Gating, and Geneset Enrichment Analysis! Systems biology is poised to revolutionize our understanding of disease models and multi-omics studies as exemplified here are a key step along that exciting journey of ...
Read More »Bioconductor workflow for single-cell RNA sequencing: Normalization, dimensionality reduction, clustering, and lineage inference
Novel single-cell transcriptome sequencing assays allow researchers to measure gene expression levels at the resolution of single cells and offer the unprecendented opportunity to investigate at the molecular level fundamental biological questions, such as stem cell differentiation or the discovery ...
Read More »Adaptive Multiview Nonnegative Matrix Factorization Algorithm for Integration of Multimodal Biomedical Data
The amounts and types of available multimodal tumor data are rapidly increasing, and their integration is critical for fully understanding the underlying cancer biology and personalizing treatment. However, the development of methods for effectively integrating multimodal data in a principled ...
Read More »ZIFA – Dimensionality reduction for zero-inflated single-cell gene expression analysis
Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq ...
Read More »Biology is being inundated by an onslaught of noisy, high-dimensional data to an extent never before experienced
Dimensionality reduction techniques such as principal component analysis (PCA) are common approaches for dealing with noisy, high-dimensional data. Though these unsupervised techniques can help uncover interesting structure in high-dimensional data they give little insight into the biological and technical considerations ...
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