Map tissue expression

This notebook explores how you can explore whether the nodes in your network are expressed in a tissue of interest. The annotations are accessed from the Annotation object of Omnipath, and are based on expression data from the Human Protein Atlas.

%%time
from neko.core.network import Network
from neko._visual.visualize_network import NetworkVisualizer
from neko._inputs.resources import Resources
from neko._annotations.gene_ontology import Ontology
import omnipath as op

/home/eirini/anaconda3/envs/omniflow_env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

CPU times: user 1.73 s, sys: 438 ms, total: 2.17 s
Wall time: 2.18 s

1. Import genes as network nodes

genes = ["SRC", "NOTCH1", "FAK", "CDH1", "CDH2", "VIM", "MAP4K4", "LATS1", "LATS2", "PTK2B"]

new_net1 = Network(genes)

Loading deafault omnipath all interactions

#Print node dataframe
new_net1.nodes

	Genesymbol	Uniprot	Type
0	SRC	P12931	NaN
1	NOTCH1	P46531	NaN
2	PTK2	Q05397	NaN
3	CDH1	P12830	NaN
4	CDH2	P19022	NaN
5	VIM	P08670	NaN
6	MAP4K4	O95819	NaN
7	LATS1	O95835	NaN
8	LATS2	Q9NRM7	NaN
9	PTK2B	Q14289	NaN

2. Check if the genes are annotated in the tissue of interest (e.g., in colorectal cancer)

annot = Ontology()
results_df = annot.check_tissue_annotations(genes_df = new_net1.nodes, tissue = 'colorectal cancer')
print(results_df)

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  Genesymbol  in_tissue
0        SRC       True
1     NOTCH1       True
2       PTK2       True
3       CDH1       True
4       CDH2       True
5        VIM       True
6     MAP4K4       True
7      LATS1       True
8      LATS2       True
9      PTK2B       True

3. Color nodes based on their presence/absence in the tissue of interest

The tissue_mapping functions highligts as light blue all the nodes of the network that are expressed in the used-defined system.

network_visualizer = NetworkVisualizer(new_net1)
network_visualizer.tissue_mapping(results_df)
network_visualizer.render()