recipe tirank

A comprehensive analysis tool for transferring phenotype of bulk transcriptomic data to single-cell or spatial transcriptomic data.

Homepage:

https://github.com/LenisLin/TiRank

License:

MIT

Recipe:

/tirank/meta.yaml

TiRank integrates deep learning and statistical analysis to infer phenotype transfer from bulk transcriptomic data to single-cell or spatial transcriptomic data. GPU-accelerated modules rely on PyTorch (>=2.1) and torchvision (>=0.16), which should be installed manually according to the user's CUDA environment.

package tirank

versions:

0.1.3-0

depends dash:

>=2.14

depends dash-bootstrap-components:

>=1.5

depends gseapy:

>=1.1

depends imbalanced-learn:

>=0.11

depends lifelines:

>=0.28

depends matplotlib-base:

>=3.8

depends numpy:

>=1.26

depends optuna:

>=3.5

depends pandas:

>=2.1

depends pillow:

>=10.2

depends python:

>=3.9

depends scanpy:

>=1.9

depends scikit-learn:

>=1.3

depends scipy:

>=1.11

depends seaborn-base:

>=0.13

depends statsmodels:

>=0.14

depends timm:

>=0.9

depends tqdm:

requirements:

additional platforms:

Installation

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 tirank

and update with::

   mamba update tirank

To create a new environment, run:

mamba create --name myenvname tirank

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 quay.io/biocontainers/tirank:<tag>

(see `tirank/tags`_ for valid values for ``<tag>``)

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