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Update requirements.txt #9

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f01fb61
Update requirements.txt
maarten-devries May 11, 2023
525bc38
Update README.md
maarten-devries May 11, 2023
44a08a8
Update README.md
maarten-devries May 11, 2023
42796d2
Update README.md
maarten-devries May 11, 2023
0c93df9
fix typo
maarten-devries May 11, 2023
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5 changes: 2 additions & 3 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,7 @@ GeneVector makes use of Scanpy anndata objects and requires that the raw count d

```
from genevector.data import GeneVectorDataset
from genevector.model import GeneVectorTrainer
from genevector.model import GeneVectorModel, GeneVector
from genevector.embedding import GeneEmbedding, CellEmbedding

import scanpy as sc
Expand All @@ -50,7 +50,6 @@ After loading the expression, creating a GeneVector object will compute the mutu
cmps = GeneVector(dataset,
output_file="genes.vec",
emb_dimension=100,
threshold=1e-6,
device="cuda")
cmps.train(1000, threshold=1e-6) # run for 1000 iterations or loss delta below 1e-6.
```
Expand All @@ -69,7 +68,7 @@ gembed = GeneEmbedding("genes.vec", dataset, vector="average")
```

#### 1. Computing gene similarities
A pandas dataframe can be generated using ```compute_similarities``` that includes the most similar genes and their cosine similarities for a given gene query. A barplot figure with a specified number of the most similar genes can be generated using ```plots_similarities```.
A pandas dataframe can be generated using ```compute_similarities``` that includes the most similar genes and their cosine similarities for a given gene query. A barplot figure with a specified number of the most similar genes can be generated using ```plot_similarities```.

```
df = gembed.compute_similarities("CD8A")
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2 changes: 1 addition & 1 deletion requirements.txt
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Expand Up @@ -10,4 +10,4 @@ tqdm==4.64.1
seaborn==0.12.1
matplotlib==3.6.2
scikit-misc==0.1.4
torch==">=1.8.0"
torch>=1.8.0