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Hi, thanks for sharing the reproducible notebooks for the paper. Could you please upload the data used in the notebooks to make them actually reproducible?
Hi!
I have posted an issue in the snap-stanford github repo of the final GEARS model. Since I didn't know which repo is more likely to catch your attention, I simply want to link it here: snap-stanford/GEARS#50 . Feel free to answer in any one of the two repos.
Thank you for your time.
Hi, I am trying to reproduce the analysis in figure 3, particularly the UMAPs. Could you let me know how to do it?
Some related questions:
Should I use the script https://github.com/yhr91/gears_misc/blob/main/paper/Fig3_UMAP_predict.py and https://github.com/yhr91/gears_misc/blob/main/paper/Fig3_UMAP_train.py?
It seems the Fig3_UMAP_train.py
only saved the model, so how did you generate the embeddings used in figure 3b?
Similarly, what embedding vectors you used to generate figure 3c? Is it related to this line https://github.com/yhr91/gears_misc/blob/73a026049d22107ef1a31c81cb677f646b62dea1/paper/Fig3_UMAP_predict.py#L69
Thank you!
Hi Yusuf and Kexin!
Thanks for developing the great tool! I'm trying to reproduce the result in Fig. 2f of the manuscript. According to the instructions, I'm trying to run the paper/fig2_train.py
script, but the following error was thrown:
Traceback (most recent call last):
File "fig2_train.py", line 63, in <module>
gears_model.model_initialize(hidden_size = 64, no_perturb = no_perturb, go_path = go_path)
File "<REDACTED>/GEARS_misc/paper/../gears/gears.py", line 147, in model_initialize
edge_list = get_similarity_network(network_type = 'go', adata = self.adata, threshold = coexpress_threshold, k = num_similar_genes_go_graph, gene_list = self.pert_list, data_path = self.data_path, data_name = self.dataset_name, split = self.split, seed = self.seed, train_gene_set_size = self.train_gene_set_size,
set2conditions = self.set2conditions, gi_go = self.gi_go, dataset = go_path)
File "<REDACTED>/GEARS_misc/paper/../gears/utils.py", line 169, in get_similarity_network
df_jaccard = pd.read_csv('/dfs/user/kexinh/gears2/go_essential_all.csv')
File "<REDACTED>/GEARS_misc/paper/conda/lib/python3.8/site-packages/pandas/io/parsers/readers.py", line 912, in read_csv
return _read(filepath_or_buffer, kwds)
File "<REDACTED>/GEARS_misc/paper/conda/lib/python3.8/site-packages/pandas/io/parsers/readers.py", line 577, in _read
parser = TextFileReader(filepath_or_buffer, **kwds)
File "<REDACTED>/GEARS_misc/paper/conda/lib/python3.8/site-packages/pandas/io/parsers/readers.py", line 1407, in __init__
self._engine = self._make_engine(f, self.engine)
File "<REDACTED>/GEARS_misc/paper/conda/lib/python3.8/site-packages/pandas/io/parsers/readers.py", line 1661, in _make_engine
self.handles = get_handle(
File "<REDACTED>/GEARS_misc/paper/conda/lib/python3.8/site-packages/pandas/io/common.py", line 859, in get_handle
handle = open(
FileNotFoundError: [Errno 2] No such file or directory: '/dfs/user/kexinh/gears2/go_essential_all.csv'
This I believe is the line where the error was raised. It seems that there are some hard-coded file paths that have not been provided in either yhr91/GEARS_misc
or snap-stanford/GEARS
. Could you help point me to where I can obtain these files so that I might be able to reproduce the result in the paper?
Best!
Zhi-Jie
Hi Yusuf and Kexin,
I'm trying to reproduce SI Figure 17 of predictions for the Horlbeck dataset from this notebook: https://github.com/yhr91/GEARS_misc/blob/main/paper/SI_Fig_17_Horlbeck_Jurkat.ipynb
So far I've encountered a couple of issues.
horlbeck_gi_jurkat
./dfs/user/kexinh/gears2/data/pert_genes_gi.pkl
is hard-coded into pertdata.py
.Neither file is included in the repo, nor can I locate the files from the main GEARS repository. Could you help point me to where I could access these files?
Thank you!
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