Overview#

GitHub Documentation Status PyPI

VAMPIRE (Visually Aided Morpho-Phenotyping Image Recognition) quantifies and visualizes shape modes of cell and nucleus morphology [1]. VAMPIRE has been used to analyze morphological changes of

  1. in vitro cancer cells in cancer metastasis [2],

  2. ex vivo rat microglia in response to oxygen-glucose deprivation [3],

  3. ex vivo ferret microglia in response to oxygen-glucose deprivation [4],

  4. ex vivo rat microglia in response to brain-derived extravellular vescicle treatment [5],

  5. in vivo MGluR5 rat model’s microglia at different ages and sexes [6].

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 (GitHub source) [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 fitting a VAMPIRE model and transforming a dataset using a VAMPIRE model. 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.quickstart.fit_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.quickstart.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, K.-S.; Chen, W.-C.; Wirtz, D.; Wu, P.-H. A Robust Unsupervised Machine-Learning Method to Quantify the Morphological Heterogeneity of Cells and Nuclei. Nat Protoc 2021, 16 (2), 754–774. https://doi.org/10.1038/s41596-020-00432-x.

  2. Wu, P.-H.; Gilkes, D. M.; Phillip, J. M.; Narkar, A.; Cheng, T. W.-T.; Marchand, J.; Lee, M.-H.; Li, R.; Wirtz, D. Single-Cell Morphology Encodes Metastatic Potential. Science Advances 2020, 6 (4), eaaw6938.

  3. Joseph, A.; Liao, R.; Zhang, M.; Helmbrecht, H.; McKenna, M.; Filteau, J. R.; Nance, E. Nanoparticle-Microglial Interaction in the Ischemic Brain Is Modulated by Injury Duration and Treatment. Bioengineering & Translational Medicine 2020, 5 (3), e10175. https://doi.org/10.1002/btm2.10175.

  4. Wood, T. R.; Hildahl, K.; Helmbrecht, H.; Corry, K. A.; Moralejo, D. H.; Kolnik, S. E.; Prater, K. E.; Juul, S. E.; Nance, E. A Ferret Brain Slice Model of Oxygen–Glucose Deprivation Captures Regional Responses to Perinatal Injury and Treatment Associated with Specific Microglial Phenotypes. Bioengineering & Translational Medicine 2022, 7 (2), e10265. https://doi.org/10.1002/btm2.10265.

  5. Nguyen, N. P.; Helmbrecht, H.; Ye, Z.; Adebayo, T.; Hashi, N.; Doan, M.-A.; Nance, E. Brain Tissue-Derived Extracellular Vesicle Mediated Therapy in the Neonatal Ischemic Brain. International Journal of Molecular Sciences 2022, 23 (2), 620. https://doi.org/10.3390/ijms23020620.

  6. Dahl, V.; Helmbrecht, H.; Rios Sigler, A.; Hildahl, K.; Sullivan, H.; Janakiraman, S.; Jasti, S.; Nance, E. Characterization of a MGluR5 Knockout Rat Model with Hallmarks of Fragile X Syndrome. Life 2022, 12 (9), 1308. https://doi.org/10.3390/life12091308.