from clustergrammer_widget import * net = Network(clustergrammer_widget)
# load data net.load_file('histology_clusters/merge_sclc.txt') merge_sclc = net.export_df() # manually set category colors for rows and columns net.set_cat_color('row', 1, 'Data-Type: phospho', 'red') net.set_cat_color('row', 1, 'Data-Type: Rme1', 'purple') net.set_cat_color('row', 1, 'Data-Type: AcK', 'blue') net.set_cat_color('row', 1, 'Data-Type: Kme1', 'grey') net.set_cat_color('row', 1, 'Data-Type: Exp', 'yellow') net.set_cat_color('col', 1, 'Histology: SCLC', 'red') net.set_cat_color('col', 1, 'Histology: NSCLC', 'blue') net.set_cat_color('col', 2, 'Sub-Histology: SCLC', 'red') net.set_cat_color('col', 2, 'Sub-Histology: NSCLC', 'blue') net.set_cat_color('col', 2, 'Sub-Histology: squamous_cell_carcinoma', 'yellow') net.set_cat_color('col', 2, 'Sub-Histology: bronchioloalveolar_adenocarcinoma', 'orange') net.set_cat_color('col', 2, 'Sub-Histology: adenocarcinoma', 'grey')
Below we will visualize the SCLC cluster. We will highlight a few of the interesting genes/proteins and sub-clusters found in this cluster of up-regulated PTMs and gene expression levels data in SCLC cell lines.
We can see that the cluster is composed of roughly for smaller clusters that are generally up-reglated in SCLC cell lines and down-regulated in NSCLC cell lines. We can also see that the cluster is composed of a mixture of phosphorylation, expression, methylation and acetylation.
NKX2-1 is a transcription factor that is known to have a role in lung development and has been used as a biomarker in lung cancer (Yang et al. 2012). In this cluster we find NKX2-1 expression and NKX2-1 methylation (R121) are both present and cluster closely together. The expression and methylation data are from independent sources and this NKX2-1 co-clustering demonstrate broad agreement between the two datasets. The data imply that variability in NKX2-1 methylation may be the result of variability in its expression level. Below is a screenshot of the immediate cluster of phophorylation, expression, and methylation including NKX2-1:
NKX2-1 levels are known to be generally high in SCLC cell lines which broadly agrees with our results. NKX2-1 also clusters with several other lung associated genes including:
This cluster also includes MARCKSL1 (MARCKS-like 1) phosphorylations: S71, T148, and S151. MARCKSL1 is thought to play a role in cytoskeletal reglation see Gene Cards. This implies that phosphorylation of MARCKSL1 phosphorylation may play a role in lung development based on its co-clustering with NKX2-1 expression/methylation.
Several cytoskeletal and extracellular matrix (ECM) associated genes/proteins appear in this cluster. This highlights the importance cytoskeletal/ECM function in SCLC lung cancer.
Here we are pre-calculating enrichment for biological processes from the Gene Ontology resource using the
enrichrgram method (see Clustergrammer-PY's API for more information). This will help us understand the broad biological processes occurring in this cluster of up-regulated PTMs and Genes.
net.enrichrgram('GO_Biological_Process_2015') net.cluster(views=) net.widget()
We see enrichment for mRNA processing, splicing, and gene expression. We also see similar results with KEGG 2016 enrichment, which can be run in this notebook with the Enrichrgram button.
Genes with the above ontologies are mainly distributed at the top and bottom sub-clusters - note that the largest middle sub-cluster with highly up-regulated values has relatively few genes with this association. We can investigate the biological processes in the middle sub-cluster by clicking the dedrogram crop button and re-running the enrihment analysis using the front-end Enrichrgram button.
Doing so reveals that this cluster is also enriched for gene expression and mRNA processing but also for several neuronal processes including: neuron projection, axon guidance and neuron projection morphology. This agrees with the neuronal characteristics of SCLC cell lines (see Onganar et al 2005).
Using the 'Disease Perturbations from GEO Up' Enrichr library we can find diseases that have up-regulated genes that are similar to our up-regulated genes/proteins. This can help us understand the disease-associations of the differntially expressed genes and proteins with differentially reglated PTMs.
net.enrichrgram('Disease_Perturbations_from_GEO_up') net.cluster(views=) net.widget()
Above, we see that up-regulated genes and PTMs in SCLC are similar to up-regulated genes in the following diseases: oligodendroglioma, multiple sclerosis, and other cancers.
To find phenotypes associated with our genes/proteins, we can use the 'MGI Phenotype Level 4' Enrichr library. This library will return phenotypes that are associated with gene knockout in mice. This can give us a less biased overview of gene/phenotype relationships.
net.enrichrgram('MGI_Mammalian_Phenotype_Level_4') net.cluster(views=) net.widget()
Above we see that our genes/proteins are enriched for the following broad phenotypes: