Remove protein atlas data and add additional metadata

README.md

@@ -1,5 +1,5 @@
 ---
-license: cc-by-sa-3.0
+license: cc-by-4.0
 ---
 
 # DeepLoc-2.0 Training Data
@@ -11,22 +11,22 @@ Data downloaded and processed using the following Python script:
 
 ```python
 import pandas as pd
-from sklearn.model_selection import train_test_split
 
-train = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv')
-train = train.loc[:,['Sequence','Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']]
-train = train.melt('Sequence', var_name='Location').query('value == 1.0').drop(labels='value', axis=1)
+df = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv').drop(['Unnamed: 0', 'Partition'], axis=1)
+df = df.melt(['Kingdom', 'ACC', 'Sequence','Membrane'], var_name='Location').query('value == 1.0').drop(labels='value', axis=1).astype({'Membrane': 'int8'})
 
-train, val = train_test_split(train)
-val = val.reset_index(drop=True)
+train = df.sample(frac=0.8)
+df = df.drop(train.index)
+val = df.sample(frac=0.5)
+test = df.drop(val.index)
 
-test = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/hpa_testset.csv')
-test = test.loc[:,['fasta','Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome']].rename(columns={'fasta':'Sequence'})
-test = test.melt('Sequence', var_name='Location').query('value == 1.0').drop(labels='value', axis=1).sample(frac=1).reset_index(drop=True)
+train = train.reset_index(drop=True)
+val = val.reset_index(drop=True)
+test = test.reset_index(drop=True)
 
-train.to_parquet('data/deeploc/deeploc-train.parquet', index=False)
-val.to_parquet('data/deeploc/deploc-val.parquet', index=False)
-test.to_parquet('data/deeploc/deeploc-test.parquet', index=False)
+train.to_parquet('deeploc-train.parquet', index=False)
+val.to_parquet('deploc-val.parquet', index=False)
+test.to_parquet('deeploc-test.parquet', index=False)
 ```
 
 ## Citation
@@ -37,7 +37,7 @@ test.to_parquet('data/deeploc/deeploc-test.parquet', index=False)
 Vineet Thumuluri and others, DeepLoc 2.0: multi-label subcellular localization prediction using protein language models, Nucleic Acids Research, Volume 50, Issue W1, 5 July 2022, Pages W228–W234, https://doi.org/10.1093/nar/gkac278
 ```
 
-The DeepLoc data is a derivative of the following datasets:
+The DeepLoc data is a derivative of the UniProt dataset:
 
 **UniProt**
 
@@ -46,9 +46,3 @@ The UniProt Consortium
 UniProt: the Universal Protein Knowledgebase in 2023
 Nucleic Acids Res. 51:D523–D531 (2023)
 ```
-
-**The Human Protein Atlas**
-
-```
-Mathias Uhlén et al.,Tissue-based map of the human proteome.Science347,1260419(2015).DOI:10.1126/science.1260419
-```

deeploc-test.parquet

@@ -1,3 +1,3 @@
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-oid sha256:9c3ba63829261f73c3f898b243151afb15bd7acb8764625cc6880869ccc90724
-size 505764
+oid sha256:a1e282132ec5d2681ca04ba6f131a4d13cf231b61c47186e3a53691791dcc036
+size 2016514

deeploc-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ab860ba3bb4b0f5cccd607d8b8f327b823cb115efb5687d0d6155f9b18dc20cc
-size 15201778
+oid sha256:4e60d7303b0cac6bfc2bb37d6671304b19f0846a197e58dc125143d3cacfc6ee
+size 16422156

deploc-val.parquet

@@ -1,3 +1,3 @@
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-oid sha256:42f78d31d98b7c8c645ec45e86fc34f6ad627d475994982891b3080229fc4b1f
-size 5031310
+oid sha256:3d0a708c9338df6177c58fa5ee70c02f1e4fe1b47f79a5a36ec7ed9ad1ca8d0a
+size 2045512

prep.py

@@ -1,17 +1,17 @@
 import pandas as pd
-from sklearn.model_selection import train_test_split
 
-train = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv')
-train = train.loc[:,['Sequence','Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Extracellular', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome', 'Plastid']]
-train = train.melt('Sequence', var_name='Location').query('value == 1.0').drop(labels='value', axis=1)
+df = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/Swissprot_Train_Validation_dataset.csv').drop(['Unnamed: 0', 'Partition'], axis=1)
+df = df.melt(['Kingdom', 'ACC', 'Sequence','Membrane'], var_name='Location').query('value == 1.0').drop(labels='value', axis=1).astype({'Membrane': 'int8'})
 
-train, val = train_test_split(train)
-val = val.reset_index(drop=True)
+train = df.sample(frac=0.8)
+df = df.drop(train.index)
+val = df.sample(frac=0.5)
+test = df.drop(val.index)
 
-test = pd.read_csv('https://services.healthtech.dtu.dk/services/DeepLoc-2.0/data/hpa_testset.csv')
-test = test.loc[:,['fasta','Cell membrane', 'Cytoplasm','Endoplasmic reticulum', 'Golgi apparatus', 'Lysosome/Vacuole', 'Mitochondrion', 'Nucleus', 'Peroxisome']].rename(columns={'fasta':'Sequence'})
-test = test.melt('Sequence', var_name='Location').query('value == 1.0').drop(labels='value', axis=1).sample(frac=1).reset_index(drop=True)
+train = train.reset_index(drop=True)
+val = val.reset_index(drop=True)
+test = test.reset_index(drop=True)
 
-train.to_parquet('data/deeploc/deeploc-train.parquet', index=False)
-val.to_parquet('data/deeploc/deploc-val.parquet', index=False)
-test.to_parquet('data/deeploc/deeploc-test.parquet', index=False)
+train.to_parquet('deeploc-train.parquet', index=False)
+val.to_parquet('deploc-val.parquet', index=False)
+test.to_parquet('deeploc-test.parquet', index=False)