Thanks for the work setting up Hetionet!

Is it possible to add the cancer drug Ceritinib (https://www.drugbank.ca/drugs/DB09063)?

A similar compound Crizotinib (https://www.drugbank.ca/drugs/DB08865) is in the database.

They both target the same protein for cancer treatment and it would be great to see how they differentiate within Hetionet.

Also add url to data. Possible URL formats include:

• http://www.ncbi.nlm.nih.gov/gene/23274
• http://identifiers.org/ncbigene/23274

How much RAM is required to run the neo4j-import.ipynb script? On a 32 GB RAM AWS instance, the RAM gets maxed out and the cell is terminated. Based on this post, it seems that your machine had 256 GB of RAM, and I was wondering if it needed that much, or if there was a way to limit RAM usage.

Knockdown and overexpression LINCS perturbations should by definition down/up-regulate the manipulated gene. Thus the self-loop edges here are superfluous.

Implement by ensuring that source_id != target_id.

How are we supposed to install the hetio package which is part of the repo's dependencies? The environment.yml file has a hard coded path (/home/dhimmels/Documents/github/hetio)==0.1.0.

I tried downloading version 0.1.0 of the hetio package from https://github.com/dhimmel/hetio/releases and changing the hard coded path to the local path instead, but received the following error when trying to run conda env create -f environment.yml:

Extracting packages ...
[      COMPLETE      ]|###################################################| 100%
Linking packages ...
[      COMPLETE      ]|###################################################| 100%
Invalid requirement: 'hetio (/home/ubuntu/hetio)==0.1.0'
It looks like a path. Does it exist ?
CondaValueError: Value error: pip returned an error.

Any ideas? I have also tried to install from the github directly instead of using a local path, but I get a dependency error with pypandoc instead (i.e. in environment.yml have the following line: - "--editable=git+https://github.com/dhimmel/hetio.git#egg=hetio") see this issue.

Hi Daniel,

If I understand it correctly, you used the information about over-/under-expression from Bgee to create your Anatomy–downregulates–Gene and Anatomy–upregulates–Gene edges [1]. These values provided by Bgee refer to over-/under-expression across anatomy but also over-/under-expression across life stages [2]. However, you are using only data from adults and therefore do not provide the life stage in your anatomy nodes. Could this in your opinion produce a bias in your network or affect it or predictions etc in any other way?

Hi Daneil,

For Hetionet, I have two brief questions and would like to hear about your insights:

1. on metabolomics side, why didn't you use the HMDB database for linking metabolites, diseases, variants, genes etc?

2. For sepsis and chronic fatigue, why they are categorized as side effects rather than diseases?

Choose which PPI sources to include in the network. Also, partition PPIs into biased and unbiased subsets.

As a metaedge kind

AEOLUS provides a curated and standardized version of FAERS. It

• removed duplicate case records

• applied standardized vocabularies with drug names mapped to RxNorm concepts and outcomes mapped to SNOMED-CT concepts

• pre-computed summary statistics about drug-outcome relationships for general consumption

The paper can be accessed at https://doi.org/10.1038/sdata.2016.26

Hi Daniel,

Just wanted to note that there are still duplicate edges in hetionet in the newest integrate.ipynb. Specifically, the following two types of relationships give duplicate edge errors when the notebook is run:

Disease-gene differential expression edges

commit = '1a11633b5e0095454453335be82012a9f0f482e4'
url = rawgit('dhimmel', 'stargeo', commit, 'data/diffex.tsv')
stargeo_df = pandas.read_table(url)
# Filter to at most 250 up and 250 down-regulated genes per disease
stargeo_df = stargeo_df.groupby(['slim_id', 'direction']).apply(
    lambda df: df.nsmallest(250, 'p_adjusted')).reset_index(drop=True)
stargeo_df.head(2)

for row in stargeo_df.itertuples():
    source_id = 'Disease', row.slim_id
    target_id = 'Gene', row.entrez_gene_id
    kind = row.direction + 'regulates'
    data = {
        'source': 'STARGEO',
        'log2_fold_change': round(row.log2_fold_change, 5),
        'unbiased': True,
        'license': 'CC0 1.0'
    }
    graph.add_edge(source_id, target_id, kind, 'both', data)

LINCS Compound-gene dysregulation edges

url = rawgit('dhimmel', 'lincs', commit, 'data/consensi/signif/dysreg-drugbank.tsv')
l1000_df = pandas.read_table(url)
l1000_df = l1000_df.query("perturbagen in @compound_df.drugbank_id and entrez_gene_id in @coding_genes")
l1000_df = filter_l1000_df(l1000_df, n=125)
l1000_df.tail(2)

mapper = {'up': 'upregulates', 'down': 'downregulates'}
for row in l1000_df.itertuples():
    source_id = 'Compound', row.perturbagen
    target_id = 'Gene', row.entrez_gene_id
    data = {
        'source': 'LINCS L1000',
        'z_score': round(row.z_score, 3),
        'method': row.status,
        'unbiased': True,
    }
    kind = mapper[row.direction]
    graph.add_edge(source_id, target_id, kind, 'both', data)

Also, is the metaedge generation supposed to be exponential with the number of metapaths in the network? I noticed that if I don't include these types of metapaths in the network, but include everything else, then the number of metapaths drops from 1200 to only 130

['Compound', 'Disease', 'palliates', 'both']
['Compound', 'Gene', 'downregulates', 'both']
['Compound', 'Gene', 'upregulates', 'both']
['Disease', 'Gene', 'downregulates', 'both']
['Disease', 'Gene', 'upregulates', 'both']

The four regulation metapaths were not included due to the edge import errors, and the palliates one due to my excluding them for testing purposes.

These are creating problems for the neo4j conversion

All pathways are currently CC-BY 3.0, however only WikiPathways should be CC-BY 3.0.

Currently metaedge is disease - causation - symptom. Do diseases cause symptoms or vice versa? Presence is a more neutral term.

Hi Daniel,

Do you remember what you needed to do to increase the number of concurrent open files allowable by neo4j? At the moment I get a warning that says:

WARNING: Max 1024 open files allowed, minimum of 40 000 recommended. See the Neo4j manual.
Starting Neo4j Server...WARNING: not changing user
process [6305]... waiting for server to be ready..... OK.
http://localhost:7500/ is ready.

Also, I think they way that neo4j is started with a subprocess call in Python is giving me issues, since I trying to start neo4j 3.1.1 in the same manner causes the service to shut down immediately before any Python code can be executed.