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|
# Case studies
## The Hp1bp3 transcript
Investigate Hp1bp3, which has a cis-QTL in hippocampus and is associated with cognitive ageing.
___
Search for the dataset:
https://genenetwork.org/api/v2/Mus_musculus/BXD/datasets?search=hippocampus
[API v1]: # https://genenetwork.org/api/v_pre1/datasets/bxd
```
[
{
"AvgID": 1,
"CreateTime": "Mon, 24 Oct 2005 00:00:00 GMT",
"DataScale": "log2",
"FullName": "Hippocampus Consortium M430v2 (Oct05) MAS5",
"Id": 86,
"Long_Abbreviation": "Hippocampus_M430_V2_BXD_MAS5_Oct05",
"ProbeFreezeId": 24,
"ShortName": "Hippocampus M430v2 BXD 10/05 MAS5",
"Short_Abbreviation": "HC_M2_1005_M",
"confidentiality": 0,
"public": 0
},
{
"AvgID": 3,
"CreateTime": "Mon, 24 Oct 2005 00:00:00 GMT",
"DataScale": "log2",
"FullName": "Hippocampus Consortium M430v2 (Oct05) RMA",
"Id": 87,
"Long_Abbreviation": "Hippocampus_M430_V2_BXD_RMA_Oct05",
"ProbeFreezeId": 24,
"ShortName": "Hippocampus M430v2 BXD 10/05 RMA",
"Short_Abbreviation": "HC_M2_1005_R",
"confidentiality": 0,
"public": 0
},
{
"AvgID": 2,
"CreateTime": "Mon, 24 Oct 2005 00:00:00 GMT",
"DataScale": "log2",
"FullName": "Hippocampus Consortium M430v2 (Oct05) PDNN",
"Id": 88,
"Long_Abbreviation": "Hippocampus_M430_V2_BXD_PDNN_Oct05",
"ProbeFreezeId": 24,
"ShortName": "Hippocampus M430v2 BXD 10/05 PDNN",
"Short_Abbreviation": "HC_M2_1005_P",
"confidentiality": 0,
"public": 0
}
]
```
This should return a list of all hippocampal _datasets_ containing the phrase 'hippocampus' (or its lemma).
The user can then look through the descriptions and decide which one they need.
In this case the appropriate key is `HC_M2_0606_P`.
We could also just get a listing of all datasets and work through them locally (by eye or with a local grep).
https://genenetwork.org/api/v2/Mus_musculus/BXD/datasets
In all cases, giving the generic term (`species`, `populations`, `datasets`, `traits`) will return a listing of all descendent options.
Just using the instance keys as the endpoint (e.g. `api/v2/Mus_musculus`, `api/v2/Mus_musculus/BXD`, `api/v2/Mus_musculus/BXD/HC_M2_0606_P`) will return metadata about the level (about the species 'mouse', the population 'BXD' or the dataset 'HC_M2_0606_P' respectively in the above examples).
___
To continue, we dig down and search the dataset for the desired gene name:
https://genenetwork.org/api/v2/Mus_musculus/BXD/HC_M2_0606_P/traits?search&symbol=Hp1bp3
```
[
{
"additive": -0.15845054446461,
"alias": "HP1BP74; HP1-BP74; Hp1bp74",
"chr": "4",
"description": "heterochromatin protein 1, binding protein 3",
"id": 78509,
"locus": "rsm10000002056",
"lrs": 57.6845496792109,
"mb": 138.242585,
"mean": 12.2393434343434,
"name": "1415751_at",
"p_value": 0.0,
"se": null,
"symbol": "Hp1bp3"
},
{
"additive": -0.489152777777777,
"alias": "HP1BP74; HP1-BP74; Hp1bp74",
"chr": "4",
"description": "heterochromatin protein 1, binding protein 3",
"id": 102578,
"locus": "rsm10000002058",
"lrs": 96.3121317863362,
"mb": 138.244118,
"mean": 8.88365656565657,
"name": "1439845_at",
"p_value": 0.0,
"se": null,
"symbol": "Hp1bp3"
},
{
"additive": -0.037382526029878,
"alias": "HP1BP74; HP1-BP74; Hp1bp74; 2310026L22Rik",
"chr": "4",
"description": "heterochromatin protein 1, binding protein 3",
"id": 110688,
"locus": "rs32937254",
"lrs": 13.2029671197265,
"mb": 138.21577,
"mean": 6.51316161616162,
"name": "1447955_at",
"p_value": 0.317,
"se": null,
"symbol": "Hp1bp3"
}
]
```
This gives us the three probesets associated with Hp1bp3 and some metadata (name, aliases, expression, precomputed QTL etc.).
We decide that `1439845_at` is the correct probeset.
___
Get more information about `1439845_at` including the metadata noted above, but also microarray platform, probe composition and mapping, chromosomal position, gene/transcript length, links to gene info (NCBI, Wikidata), homologous genes in other species, [what other datasets contain data for this gene] etc.:
https://genenetwork.org/api/v2/Mus_musculus/BXD/HC_M2_0606_P/1439845_at
[API v1]: # https://genenetwork.org/api/v_pre1/trait/HC_M2_0606_P/1439845_at
```
[
{
"additive": -0.489152777777777,
"alias": "HP1BP74; HP1-BP74; Hp1bp74",
"chr": "4",
"description": "heterochromatin protein 1, binding protein 3",
"id": 102578,
"locus": "rsm10000002058",
"lrs": 96.3121317863362,
"mb": 138.244118,
"mean": 8.88365656565657,
"name": "1439845_at",
"p_value": 0.0,
"se": null,
"symbol": "Hp1bp3",
"wikidata": "Q18251298",
"homologene", "7774",
}
]
```
*Should include all of the data shown at https://genenetwork.org/show_trait?trait_id=1439845_at&dataset=HC_M2_0606_P*
___
Get the expression data for this trait:
https://genenetwork.org/api/v2/Mus_musculus/BXD/HC_M2_0606_P/1439845_at/data
```
[
{
"data_id": 23426549,
"sample_name": "129S1/SvImJ",
"sample_name_2": "129S1/SvImJ",
"se": 0.219,
"value": 6.61
},
{
"data_id": 23426549,
"sample_name": "A/J",
"sample_name_2": "A/J",
"se": 0.158,
"value": 6.536
},
{
"data_id": 23426549,
"sample_name": "AKR/J",
"sample_name_2": "AKR/J",
"se": 0.076,
"value": 6.486
},
{
"data_id": 23426549,
"sample_name": "B6D2F1",
"sample_name_2": "B6D2F1",
"se": 0.09,
"value": 6.561
},
.
.
.
]
```
This is a data endpoint, so the returned JSON includes a vector of the transcript expression values for this probeset.
If we wanted to grab the whole microarray dataset, then we can just use the data keyword one level up.
Here, a return type can also be specified
https://genenetwork.org/api/v2/Mus_musculus/BXD/HC_M2_0606_P/BXD/data.tsv
This returns a tab-delimited table of data (probesets in columns, strains/individuals in rows) for download.
___
Get the QTL vector:
https://genenetwork.org/api/v2/Mus_musculus/BXD/HC_M2_0606_P/1439845_at/qtl?method=GEMMA&genotype=mm10
[API v1]: # https://genenetwork.org/api/v_pre1/mapping?trait_id=1447955_at&db=HC_M2_0606_P&method=gemma&use_loco=FALSE&use_loco=0.01
```
[
[
{
"Mb": 3.00149,
"additive": -0.0017764785,
"chr": 1,
"lod_score": 0.06055383480931299,
"name": "rsm10000000001",
"p_value": 0.8698536
},
{
"Mb": 3.010274,
"additive": -0.0017764785,
"chr": 1,
"lod_score": 0.06055383480931299,
"name": "rs31443144",
"p_value": 0.8698536
},
{
"Mb": 3.492195,
"additive": -0.0017764785,
"chr": 1,
"lod_score": 0.06055383480931299,
"name": "rs6269442",
"p_value": 0.8698536
},
{
"Mb": 3.511204,
"additive": -0.0017764785,
"chr": 1,
"lod_score": 0.06055383480931299,
"name": "rs32285189",
"p_value": 0.8698536
},
{
"Mb": 3.659804,
"additive": -0.0017764785,
"chr": 1,
"lod_score": 0.06055383480931299,
"name": "rs258367496",
"p_value": 0.8698536
},
{
"Mb": 3.777023,
"additive": -0.0017764785,
"chr": 1,
"lod_score": 0.06055383480931299,
"name": "rs32430919",
"p_value": 0.8698536
},
.
.
.
]
```
This is also a data endpoint, so we get a vector of p-values together with a vector of chromosomal positions.
___
Correlate with all phenotypes:
https://genenetwork.org/api/v2/Mus_musculus/BXD/HC_M2_0606_P/1439845_at/correlations?method=spearmann&dataset=phenotypes&n_results=10
[API v1]: # https://genenetwork.org/api/v_pre1/correlation?trait_id=1447955_at&db=HC_M2_0606_P&target_db=BXDPublish&type=sample&method=spearman&return=10
[Error]: # This returns 500 results.
```
[
{
"#_strains": 7,
"p_value": 0.0025194724037946874,
"sample_r": 0.9285714285714288,
"trait": "12562"
},
{
"#_strains": 13,
"p_value": 2.4445741031329683e-05,
"sample_r": 0.9023392305243964,
"trait": "12889"
},
{
"#_strains": 7,
"p_value": 0.01369732661532562,
"sample_r": -0.8571428571428573,
"trait": "19087"
},
{
"#_strains": 13,
"p_value": 0.00039102596905431295,
"sample_r": 0.8342668763658431,
"trait": "20884"
},
{
"#_strains": 8,
"p_value": 0.01017554012345675,
"sample_r": -0.8333333333333335,
"trait": "10409"
},
{
"#_strains": 8,
"p_value": 0.01017554012345675,
"sample_r": -0.8333333333333335,
"trait": "10410"
},
{
"#_strains": 6,
"p_value": 0.04156268221574334,
"sample_r": 0.8285714285714287,
"trait": "20393"
},
{
"#_strains": 6,
"p_value": 0.04156268221574334,
"sample_r": -0.8285714285714287,
"trait": "20595"
},
{
"#_strains": 10,
"p_value": 0.0038149200825507135,
"sample_r": -0.8181818181818182,
"trait": "16177"
},
{
"#_strains": 15,
"p_value": 0.000219365827727102,
"sample_r": 0.8142857142857142,
"trait": "27198"
}
]
```
It is not necessary to specify the target at any level above dataset as correlations can only be performed within a population.
___
Correlate with a specific trait:
https://genenetwork.org/api/v2/Mus_musculus/BXD/HC_M2_0606_P/1439845_at/correlations?method=pearson&dataset=HC_M2_0606_P&traits=1415751_at,1447955_at
Here, we have correlated against the two other Hs1bp3 probesets, which are specified by a comma-delimited list of trait IDs.
Correlation across different datasets would be achieved by multiple API calls.
Although there may be a way to line up a series of calls and have them run as a batch (I presume more complicated queries like this would be done via a POST interface though).
___
More advanced searches could allow restricting the search to certain fields:
https://genenetwork.org/api/v2/Mus_musculus/BXD/datasets?search&type=transcript&tag=hippocampus
I would support using tags to associate keywords with items at all levels.
Here, the `search` parameter was left empty as we are looking for a phrase in a particular field.
If all parameters are empty, this should not fail but return the same as the `datasets` query without the parameters (i.e. return a listing of all available datasets).
|
