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Single-Cell Analysis

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SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning.

Proteomics
SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure from expression data observed for heterogenous samples, is presented here. SIMLR can be...

Machine learning and deep analytics for biocomputing: call for better explainability.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The goals of this workshop are to discuss challenges in explainability of current Machine Leaning and Deep Analytics (MLDA) used in biocomputing and to start the discussion on ways to improve it. We define explainability in MLDA as easy to use inform...

Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.

Nucleic acids research
The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large n...

Gating mass cytometry data by deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve autom...

Using neural networks for reducing the dimensions of single-cell RNA-Seq data.

Nucleic acids research
While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. These include ...

Removal of batch effects using distribution-matching residual networks.

Bioinformatics (Oxford, England)
MOTIVATION: Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument...

Automated cell type discovery and classification through knowledge transfer.

Bioinformatics (Oxford, England)
MOTIVATION: Recent advances in mass cytometry allow simultaneous measurements of up to 50 markers at single-cell resolution. However, the high dimensionality of mass cytometry data introduces computational challenges for automated data analysis and h...

High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton.

Optics express
Time-stretch imaging has been regarded as an attractive technique for high-throughput imaging flow cytometry primarily owing to its real-time, continuous ultrafast operation. Nevertheless, two key challenges remain: (1) sufficiently high time-stretch...

Rapid acquisition of mean Raman spectra of eukaryotic cells for a robust single cell classification.

The Analyst
Raman spectroscopy has previously been used to identify eukaryotic and prokaryotic cells. While prokaryotic cells are small in size and can be assessed by a single Raman spectrum, the larger size of eukaryotic cells and their complex organization req...

Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis.

Cell
Proteomics has proved invaluable in generating large-scale quantitative data; however, the development of systems approaches for examining the proteome in vivo has lagged behind. To evaluate protein abundance and localization on a proteome scale, we ...