GEASS: Neural causal feature selection for high-dimensional biological data
Authors: Mingze Dong, Yuval Kluger
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of GEASS in several synthetic datasets and real biological data from single-cell RNA sequencing and spatial transcriptomics. |
| Researcher Affiliation | Academia | Mingze Dong Yale University mingze.dong@yale.edu Yuval Kluger Yale University yuval.kluger@yale.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the GEASS methodology described. |
| Open Datasets | Yes | The data preprocessing is consistent with the sc Velo tutorial: https://scvelo.readthedocs.io/ Velocity Basics/ (Bergen et al., 2020). The data is downloaded from Dryad and preprocessed with the standard Scanpy pipeline (Wolf et al., 2018). |
| Dataset Splits | No | We use the same training parameters in all time-series settings, with the key sparsity regularization parameter λ1 set with 0.04/0.05 based on a validation set, and the rest parameter settings are consistent with default. The paper mentions using a 'validation set' but does not specify the explicit split percentages, sample counts, or a citation to a predefined split for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The VAR-LINGAM algorithm is implemented in the Python package LINGAM...The PCMCI algorithm is implemented in the Python package Tigramite...The GVAR algorithm is implemented at https://github.com/i6092467/GVAR. The data is downloaded from Dryad and preprocessed with the standard Scanpy pipeline (Wolf et al., 2018). The data preprocessing is consistent with the sc Velo tutorial: https://scvelo.readthedocs.io/ Velocity Basics/ (Bergen et al., 2020). The paper mentions software packages but does not provide specific version numbers for them. |
| Experiment Setup | Yes | The parameter set: order=5, hidden_layer_size = 10, end_epoch=50, batch_size = 50, lmbd=1 is used throughout our study. We use the same training parameters in all time-series settings, with the key sparsity regularization parameter λ1 set with 0.04/0.05 based on a validation set, and the rest parameter settings are consistent with default. The parameter set: λ1 = 0.06, λ2 = 0.1. |