Granger causal inference on DAGs identifies genomic loci regulating transcription
Authors: Alexander P Wu, Rohit Singh, Bonnie Berger
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We applied Gr ID-Net on multimodal single-cell assays that profile chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) in the same cell and show that it dramatically outperforms existing methods for inferring regulatory locus gene links, achieving up to 71% greater agreement with independent population genetics-based estimates. |
| Researcher Affiliation | Academia | 1 Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA 2 Department of Mathematics, MIT, Cambridge, MA 02139, USA {rsingh,alexwu,bab}@csail.mit.edu |
| Pseudocode | No | The paper describes the model architecture using mathematical equations (Eqn. 4, 5, 6, 7) and textual descriptions but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | Yes | The code for Gr ID-Net is available at https://github.com/alexw16/gridnet. |
| Open Datasets | Yes | We analyzed three single-cell multimodal datasets that characterize a range of dynamic processes, including cancer drug response and cellular differentiation (Cao et al., 2018; Chen et al., 2019; Ma et al., 2020). |
| Dataset Splits | No | The paper describes training details like learning rate, epochs, and minibatch size for the Gr ID-Net models. It also mentions comparing full and reduced models using an F-test. However, it does not explicitly define or specify standard train/validation/test splits of the datasets (e.g., percentages or counts for each subset) that are typically used to evaluate model generalization. |
| Hardware Specification | Yes | All models were implemented in Py Torch and trained on a single NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper states, “All models were implemented in Py Torch” and mentions using “Homer” and “statsmodels package,” but it does not specify version numbers for any of these software dependencies. |
| Experiment Setup | Yes | Gr ID-Net models were trained using the Adam optimizer with a learning rate of 0.001 for 20 epochs or until convergence (defined to be the point at which the relative change in the loss function is less than 0.1/|P| across consecutive epochs). A minibatch size of 1024 candidate peak gene pairs was used during training, and trainable parameters in the model were initialized using Glorot initialization (Bengio & Glorot, 2010). All Gr ID-Net models consisted of L = 10 GNN layers; the architectures of the three sub-models ( h(reduced) y , h(full) y , and h(full) x ) were identical but separate. |