recipe bioconductor-scope

A normalization and copy number estimation method for single-cell DNA sequencing






Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background.

package bioconductor-scope

(downloads) docker_bioconductor-scope



depends bioconductor-biocgenerics:


depends bioconductor-biostrings:


depends bioconductor-bsgenome:


depends bioconductor-bsgenome.hsapiens.ucsc.hg19:


depends bioconductor-dnacopy:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicranges:


depends bioconductor-iranges:


depends bioconductor-rsamtools:


depends bioconductor-s4vectors:


depends r-base:


depends r-desctools:

depends r-doparallel:

depends r-foreach:

depends r-gplots:

depends r-rcolorbrewer:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-scope

and update with::

   mamba update bioconductor-scope

To create a new environment, run:

mamba create --name myenvname bioconductor-scope

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-scope/tags`_ for valid values for ``<tag>``)

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