recipe bioconductor-sconify

A toolkit for performing KNN-based statistics for flow and mass cytometry data






This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold.

package bioconductor-sconify

(downloads) docker_bioconductor-sconify



depends bioconductor-flowcore:


depends r-base:


depends r-dplyr:

depends r-fnn:

depends r-ggplot2:

depends r-magrittr:

depends r-readr:

depends r-rtsne:

depends r-tibble:



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-sconify

and update with::

   mamba update bioconductor-sconify

To create a new environment, run:

mamba create --name myenvname bioconductor-sconify

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-sconify/tags`_ for valid values for ``<tag>``)

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