PyGenePlexus Data

Preprocessed Data

PyGenePlexus comes with pre-processed data that can be downloaded using geneplexus.download.download_select_data() or directly from Zenodo.

All options:

Networks	[BioGRID], [STRING], [IMP]
Species	Human, Mouse, Zebrafish, Worm, Fly, Yeast
Features	SixSpeciesN2V
GSCs	[GO], [Monarch], [Mondo], Combined

Detailed species info:

	Specifc Name	Taxon Id
Human	Homo sapiens	9606
Mouse	Mus musculus	10090
Fly	Drosophila melanogaster	7227
Zebrafish	Danio rerio	7955
Worm	Caenorhabditis elegans	6239
Yeast	Saccharomyces cerevisiae	4932

Due to the availability of the data, the following combinations are supported:

Available Network Options

	Human	Mouse	Fly	Zebrafish	Worm	Yeast
STRING	✅	✅	✅	✅	✅	✅
IMP	✅	✅	✅	✅	✅	✅
BioGRID	✅	✅	✅	❌	✅	✅

Available Gene Set Collection Options

	Human	Mouse	Fly	Zebrafish	Worm	Yeast
GO	✅	✅	✅	✅	✅	✅
Monarch	✅	✅	❌	✅	✅	✅
Mondo	✅	❌	❌	❌	❌	❌

Note

The Combined option for the GSC selection utilizes all available GSC options for a given species.

Custom Data

PyGenePlexus uses a strict naming convention rto read in the files. Users can supply there own data by generating files in the following formats:

1. NodeOrder__{Species}__{Network Name}.txt Network node ordering

   A text file with a single column containing all genes present in the network for a given species. The ordering of nodes in this file serves as the index map for thenetwork feature data.

2. Data__{Species}__{Feature Name}__{Network Name}.npy Data array

   A numpy array of the chosen network representation (rows are genes ordered by NodeOrder file, columns are features).

3. GSC__{Species}__{GSC Name}__{Network Name}.json Filtered GSC for the network

   A subsetted GSC where only the genes present in the network are considered.

   {
     "{Term ID}" # ID of the term : {
        "Name"  : # returns string of term name
        "Genes" : # returns list of genes annotated to term
        "Task"  : # returns type of GSC the term is from
        }
     "Universe" : # returns list of all genes in GSC
     "Term Order" : # returns list of all term IDs in GSC
   }
   

4. PreTrainedWeights__{Species}__{GSC Name}__{Network Name}__{Feature Name}.json Pretrained weights

   Model weights for model trained on terms is selected GSC

   {
     "{Term ID}" # ID of the term : {
        "Name"  : # returns string of term name
        "Weights" : # returns list of model weights
        "PosGenes" : # returns list genes used as positives in the model
        "Task"  : # returns type of GSC the term is from
        }
   }
   

5. Edgelist__{Species}__{Network Name}.edg Network edgelist

   Edgelist for the given network.

6. IDconversion__{Species}__{ID Type}-to-{ID Type}.json Gene ID conversions

   These files convert from one gene ID convention to another

   {
     "{Gene ID}" : # returns list of how Gene ID is converted to other gene ID type
   }
   

7. BioMart__{Species}__{Species}.json One-to-one ortholog conversions

   These files convert genes from one species into the corresponding one-to-one ortholog.

   {
     "{Gene ID}" : # returns string of how gene is converted to its one-to-one ortholog
   }