SparseDC – a sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data

November 20, 2017 Leave a comment 4,509 Views

Cell types in cell populations change as the condition changes: some cell types die out, new cell types may emerge and surviving cell types evolve to adapt to the new condition. Using single-cell RNA-sequencing data that measure the gene expression ...

ACMS Colloquium – Statistical modeling of RNA sequencing data

April 24, 2015 Leave a comment 2,606 Views

University of Notre Dame – College of Science Time: Mon May 4, 2015, 4:15PM – 5:15PM Location: 127 Hayes-Healy Center Hui Jiang Department of Biostatistics University of Michigan Statistical modeling of RNA sequencing data Ultra high-throughput sequencing of transcriptomes (RNA-Seq) ...

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Tag Archives: University of Notre Dame

SparseDC – a sparse differential clustering algorithm for tracing cell type changes via single-cell RNA-sequencing data

ACMS Colloquium – Statistical modeling of RNA sequencing data

A 3’-end capture sequencing method for high-throughput targeted gene expression profiling

Effects of Key RNA-sequencing Technical Elements on Toxicity Assessment using Three Biological Replicates

RNA-Scoop – interactive visualization of transcripts in single-cell transcriptomes

SCHNAPPs – Single Cell sHiNy APPlication(s)

CircAID-p-seq – a PCR-free library preparation for selective 3′ phospho-RNA sequencing

Nanopore ReCappable Sequencing (NRCeq) – a new technique that can identify capped full-length RNAs

CLEARER – a machine learning approach for predicting essential genes across eukaryotes