r-bioc-dupradar
BioConductor assessment of duplication rates in RNA-Seq datasets
This BioConductor package provides assessment of duplication rate
quality control for RNA-Seq datasets.
r-bioc-affy
BioConductor methods for Affymetrix Oligonucleotide Arrays
This is part of the BioConductor GNU R suite. The package contains
functions for exploratory oligonucleotide array analysis.
r-bioc-all
Bioconductor data package used by several bioc tools
Data of T- and B-cell Acute Lymphocytic Leukemia from the Ritz
Laboratory at the DFCI (includes Apr 2004 versions)
r-bioc-basilisk
freezing Python dependencies inside Bioconductor packages
Installs a self-contained conda instance that is managed by the
R/Bioconductor installation machinery. This aims to provide a consistent
Python environment that can be used reliably by Bioconductor packages.
Functions are also provided to enable smooth interoperability of
multiple Python environments in a single R session.
r-bioc-biocviews
Categorized views of R package repositories
Infrastructure to support 'views' used to classify
Bioconductor packages. 'biocViews' are directed acyclic
graphs of terms from a controlled vocabulary. There are three
major classifications, corresponding to 'software', 'annotation',
and 'experiment data' packages.
r-bioc-hsmmsinglecell
Single-cell RNA-Seq for differentiating human skeletal muscle myoblasts
Skeletal myoblasts undergo a well-characterized sequence of
morphological and transcriptional changes during differentiation. In
this experiment, primary human skeletal muscle myoblasts (HSMM) were
expanded under high mitogen conditions (GM) and then differentiated by
switching to low-mitogen media (DM). RNA-Seq libraries were sequenced
from each of several hundred cells taken over a time-course of serum-
induced differentiation. Between 49 and 77 cells were captured at each
of four time points (0, 24, 48, 72 hours) following serum switch using
the Fluidigm C1 microfluidic system. RNA from each cell was isolated and
used to construct mRNA-Seq libraries, which were then sequenced to a
depth of ~4 million reads per library, resulting in a complete gene
expression profile for each cell.