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Merge pull request #86 from alxndrkalinin/fix_examples
v0.5.1
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‎.gitignore

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‎README.md

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## Citation
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If you find this work useful for your research, please cite our [pre-print](https://doi.org/10.1101/2024.04.01.587631):
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Kalinin, A.A., Arevalo, J., Vulliard, L., Serrano, E., Tsang, H., Bornholdt, M., Rajwa, B., Carpenter, A.E., Way, G.P. and Singh, S., 2024. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, pp.2024-04. doi:10.1101/2024.04.01.587631
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Kalinin, A.A., Arevalo, J., Vulliard, L., Serrano, E., Tsang, H., Bornholdt, M., Muñoz, A.F., Sivagurunathan, S., Rajwa, B., Carpenter, A.E., Way, G.P. and Singh, S., 2024. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, pp.2024-04. doi:10.1101/2024.04.01.587631
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BibTeX:
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```
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@article{kalinin2024versatile,
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title={A versatile information retrieval framework for evaluating profile strength and similarity},
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author={Kalinin, Alexandr A and Arevalo, John and Vulliard, Loan and Serrano, Erik and Tsang, Hillary and Bornholdt, Michael and Rajwa, Bartek and Carpenter, Anne E and Way, Gregory P and Singh, Shantanu},
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author={Kalinin, Alexandr A and Arevalo, John and Vulliard, Loan and Serrano, Erik and Tsang, Hillary and Bornholdt, Michael and Muñoz, Alán F and Sivagurunathan, Suganya and Rajwa, Bartek and Carpenter, Anne E and Way, Gregory P and Singh, Shantanu},
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journal={bioRxiv},
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pages={2024--04},
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year={2024},

‎examples/null_size.ipynb

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"Because the full null size $d_{null}=118755$, smaller sample sizes ($<5,000$) lead to poor estimation of significance for these data, while very large values ($>100,000$) cover the whole null and do not affect perturbation ranking results.\n",
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"Because the full null size $d_{null}=118755$, smaller sample sizes ($<=1,000$) lead to poor estimation of significance for these data, while very large values ($>100,000$) cover the whole null and do not affect perturbation ranking results.\n",
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"\n",
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"## Practical consideration for choosing null size\n",
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"## Practical consideration for choosing the null size\n",
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"\n",
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"In practice, drawing a large number of samples is not always feasible, because compute time for each AP calculation grows with the higher number of perturbations of the dataset, the number of metadata constraints for profile grouping, sizes of perturbation groups (the number of perturbation replicates) and control groups (the number of control replicates), and profile dimensionality (the number of features in a profile).\n",
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"Finding a `null_size` that works for a particular dataset is balancing between test resolution (for example, being able to tell apart vary small p-values) and compute. We provided `null_size` values for each real-world dataset in Supplemental Materials to our paper—please refer to:\n",
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"Finding a `null_size` that works for a particular dataset means balancing between test resolution (for example, being able to tell apart vary small p-values) and compute. We provided `null_size` values for each real-world dataset in Supplemental Materials to our paper—please refer to:\n",
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"> Kalinin, A. A. et al. A versatile information retrieval framework for evaluating profile strength and similarity. bioRxiv, 2024-04, (2024)."
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},
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