def gwas_variation_to_genes(meth, workspaceID=None, gwasObjectID=None, num2snps=None, pmin=None, distance=None, gl_out=None, fs_out=None):
"""This method takes the top SNPs obtained after GWAS analysis as input
(TopVariations) object, -log (pvalue) cutoff and a distance parameter as input.
For each significant SNP that passes the p-value cutoff, genes are searched in the
window specified by the distance parameter.
:param workspaceID: Workspace (use current if empty)
:type workspaceID: kbtypes.Unicode
:param gwasObjectID: GWAS analysis MLM result object
:type gwasObjectID: kbtypes.KBaseGwasData.GwasTopVariations
:param num2snps: Number to snps
:type num2snps: kbtypes.Numeric
:default num2snps: 100
:param pmin: Minimum pvalue (-log10)
:type pmin: kbtypes.Numeric
:default pmin: 4
:param distance: Distance in bp around SNP to look for genes
:type distance: kbtypes.Numeric
:default distance: 10000
:param gl_out: Output GwasGeneLint workspace object name
:type gl_out: kbtypes.KBaseGwasData.GwasGeneList
:param fs_out: Output FeatureSet workspace object name
:type fs_out: kbtypes.KBaseSearch.FeatureSet
:return: Workspace objectID of gwas results
:rtype: kbtypes.Unicode
:output_widget: ValueListWidget
"""
meth.stages = 3
if not workspaceID:
workspaceID = meth.workspace_id
meth.advance("init GWAS service")
gc = GWAS(URLS.gwas, token=meth.token)
meth.advance("Running Variations to Genes")
argsx = {"ws_id" : meth.workspace_id, "variation_id" : gwasObjectID, "out_id": gl_out, "num2snps" : num2snps, "pmin": pmin, "distance" : distance, "comment" : "comment"}
try:
gl_oid = gc.variations_to_genes(argsx)
except Exception as err:
raise GWASException("submit job failed: {}".format(err))
ws = Workspace2(token=meth.token, wsid=meth.workspace_id)
raw_data = ws.get(gl_out)
gl = [ gr[2] for gr in raw_data['genes']]
fs = genelist2fs(gl)
ws.save_objects({'workspace' : meth.workspace_id, 'objects' :[{'type' : 'KBaseSearch.FeatureSet', 'data' : fs, 'name' : fs_out, 'meta' : {'original' : gl_out}}]})
meth.advance("Returning object")
return json.dumps({'values': [
["Workspace GwasGeneList object", gl_out],
["Workspace FeatureSet object", fs_out]
]})
def gwas_variation_to_genes(meth,
workspaceID=None,
gwasObjectID=None,
num2snps=None,
pmin=None,
distance=None,
gl_out=None,
fs_out=None):
"""This method takes the top SNPs obtained after GWAS analysis as input
(TopVariations) object, -log (pvalue) cutoff and a distance parameter as input.
For each significant SNP that passes the p-value cutoff, genes are searched in the
window specified by the distance parameter.
:param workspaceID: Workspace (use current if empty)
:type workspaceID: kbtypes.Unicode
:param gwasObjectID: GWAS analysis MLM result object
:type gwasObjectID: kbtypes.KBaseGwasData.GwasTopVariations
:param num2snps: Number to snps
:type num2snps: kbtypes.Numeric
:default num2snps: 100
:param pmin: Minimum pvalue (-log10)
:type pmin: kbtypes.Numeric
:default pmin: 4
:param distance: Distance in bp around SNP to look for genes
:type distance: kbtypes.Numeric
:default distance: 10000
:param gl_out: Output GwasGeneLint workspace object name
:type gl_out: kbtypes.KBaseGwasData.GwasGeneList
:param fs_out: Output FeatureSet workspace object name
:type fs_out: kbtypes.KBaseSearch.FeatureSet
:return: Workspace objectID of gwas results
:rtype: kbtypes.Unicode
:output_widget: ValueListWidget
"""
meth.stages = 3
if not workspaceID:
workspaceID = meth.workspace_id
meth.advance("init GWAS service")
gc = GWAS(URLS.gwas, token=meth.token)
meth.advance("Running Variations to Genes")
argsx = {
"ws_id": meth.workspace_id,
"variation_id": gwasObjectID,
"out_id": gl_out,
"num2snps": num2snps,
"pmin": pmin,
"distance": distance,
"comment": "comment"
}
try:
gl_oid = gc.variations_to_genes(argsx)
except Exception as err:
raise GWASException("submit job failed: {}".format(err))
ws = Workspace2(token=meth.token, wsid=meth.workspace_id)
raw_data = ws.get(gl_out)
gl = [gr[2] for gr in raw_data['genes']]
fs = genelist2fs(gl)
ws.save_objects({
'workspace':
meth.workspace_id,
'objects': [{
'type': 'KBaseSearch.FeatureSet',
'data': fs,
'name': fs_out,
'meta': {
'original': gl_out
}
}]
})
meth.advance("Returning object")
return json.dumps({
'values': [["Workspace GwasGeneList object", gl_out],
["Workspace FeatureSet object", fs_out]]
})
