tengjuilin / vampire-analysis

VAMPIRE (Visually Aided Morpho-Phenotyping Image Recognition) analysis quantifies and visualizes heterogeneity of cell and nucleus morphology.

vampire-analysis

VAMPIRE (Visually Aided Morpho-Phenotyping Image Recognition) analysis quantifies and visualizes heterogeneity of cell and nucleus morphology [1]. It is used widely in analyzing microglial shape change in response to oxygen-glucose deprivation [2] and morphological changes in cancer metastasis [3].

vampire-analysis provides a reproducible, fully-documented, and easy-to-use Python package that is based on the method and software used the in vampireanalysis GUI [1]. Main advantages include:

• operating-system-independent package API
• full documentation with easy-to-read code
• flexible input and filtering options
• flexible plotting options

Installation

See documentation for detailed installation guide. If Python is installed on your machine, type the following line into your command prompt to install via PyPI:

pip install vampire-analysis

Getting started

See documentation for detailed guide for basics of building and applying models. If you have build.xlsx under C:\vampire containing the build image set information, you can build the model with

>>> import pandas as pd  # used to read excel files
>>> import vampire as vp  # recommended import signature

>>> build_df = pd.read_excel(r'C:\vampire\build.xlsx')
>>> vp.model.build_models(build_df, random_state=1)

If you have apply.xlsx under C:\vampire containing the apply image set information, you can apply the model with

>> > apply_df = pd.read_excel(r'C:\vampire\apply.xlsx')
>> > vp.model.transform_datasets(apply_df)

Flexible options are provided for building and applying models in the advanced section in the documentation.

References

[1] Phillip, J.M., Han, KS., Chen, WC. et al. A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei. Nat Protoc 16, 754–774 (2021). https://doi.org/10.1038/s41596-020-00432-x

[2] Joseph, A, Liao, R, Zhang, M, et al. Nanoparticle-microglial interaction in the ischemic brain is modulated by injury duration and treatment. Bioeng Transl Med. 2020; 5:e10175. https://doi.org/10.1002/btm2.10175

[3] Wu, PH., Phillip, J., Khatau, S. et al. Evolution of cellular morpho-phenotypes in cancer metastasis. Sci Rep 5, 18437 (2016). https://doi.org/10.1038/srep18437