356 lines
12 KiB
Python
356 lines
12 KiB
Python
import sys,os
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import webbrowser
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from fluents import logger, plots,workflow,dataset,main
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from fluents.lib import blmfuncs,nx_utils,validation,engines,cx_stats,cx_utils
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import gobrowser, geneontology
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import scipy
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import networkx as nx
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class SmallTestWorkflow(workflow.Workflow):
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name = 'Smokers'
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ident = 'smokers'
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description = 'A small test workflow for gene expression analysis.'
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def __init__(self):
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workflow.Workflow.__init__(self)
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# DATA IMPORT
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load = workflow.Stage('load', 'Data')
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load.add_function(DatasetLoadFunctionSmokerSmall())
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load.add_function(DatasetLoadFunctionSmokerMedium())
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load.add_function(DatasetLoadFunctionSmokerFull())
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load.add_function(DatasetLoadFunctionSmokerGO())
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#load.add_function(DatasetLoadFunctionCYCLE())
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self.add_stage(load)
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# PREPROCESSING
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# prep = workflow.Stage('prep', 'Preprocessing')
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# prep.add_function(LogFunction())
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# self.add_stage(prep)
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# NETWORK PREPROCESSING
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#net = workflow.Stage('net', 'Network integration')
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#net.add_function(DiffKernelFunction())
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#net.add_function(ModKernelFunction())
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#net.add_function(RandDiffKernelFunction())
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#self.add_stage(net)
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# BLM's
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model = workflow.Stage('models', 'Models')
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model.add_function(blmfuncs.PCA())
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model.add_function(blmfuncs.PLS())
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model.add_function(blmfuncs.LPLS())
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#model.add_function(bioconFuncs.SAM(app))
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self.add_stage(model)
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query = workflow.Stage('query', 'Gene Query')
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query.add_function(NCBIQuery())
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query.add_function(KEGGQuery())
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self.add_stage(query)
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# Gene Ontology
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go = workflow.Stage('go', 'Gene Ontology')
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go.add_function(gobrowser.LoadGOFunction())
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go.add_function(gobrowser.SetICFunction())
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# go.add_function(gobrowser.GOWeightFunction())
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# go.add_function(gobrowser.DistanceToSelectionFunction())
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# go.add_function(gobrowser.TTestFunction())
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go.add_function(gobrowser.PlotDagFunction())
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go.add_function(GoEnrichment())
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self.add_stage(go)
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# EXTRA PLOTS
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#plt = workflow.Stage('net', 'Network')
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#plt.add_function(nx_analyser.KeggNetworkAnalyser())
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#self.add_stage(plt)
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logger.log('debug', 'Small test workflow is now active')
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class DatasetLoadFunctionSmokerSmall(workflow.Function):
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"""Loader for all ftsv files of smokers small datasets."""
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def __init__(self):
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workflow.Function.__init__(self, 'load_small', 'Smoker (Small)')
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def run(self):
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path = 'data/smokers-small/'
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files = os.listdir(path)
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out = []
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for fname in files:
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if fname.endswith('.ftsv'):
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input_file = open(os.path.join(path, fname))
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out.append(dataset.read_ftsv(input_file))
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return out
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class DatasetLoadFunctionSmokerMedium(workflow.Function):
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"""Loader for all ftsv files of smokers small datasets."""
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def __init__(self):
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workflow.Function.__init__(self, 'load_medium', 'Smoker (Medium)')
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def run(self):
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path = 'data/smokers-medium/'
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files = os.listdir(path)
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out = []
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for fname in files:
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if fname.endswith('.ftsv'):
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input_file = open(os.path.join(path, fname))
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out.append(dataset.read_ftsv(input_file))
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return out
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class DatasetLoadFunctionSmokerFull(workflow.Function):
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"""Loader for all ftsv files of smokers small datasets."""
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def __init__(self):
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workflow.Function.__init__(self, 'load_full', 'Smoker (Full)')
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def run(self):
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path = 'data/smokers-full/'
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files = os.listdir(path)
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out = []
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for fname in files:
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if fname.endswith('.ftsv'):
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input_file = open(os.path.join(path, fname))
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out.append(dataset.read_ftsv(input_file))
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return out
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class DatasetLoadFunctionSmokerGO(workflow.Function):
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"""Loader for all ftsv files of smokers small datasets."""
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def __init__(self):
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workflow.Function.__init__(self, 'load_go', 'Smoker (GO)')
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def run(self):
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path = 'data/smokers-go/'
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files = os.listdir(path)
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out = []
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for fname in files:
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if fname.endswith('.ftsv'):
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input_file = open(os.path.join(path, fname))
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out.append(dataset.read_ftsv(input_file))
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return out
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class DatasetLoadFunctionCYCLE(workflow.Function):
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"""Loader for pickled CYCLE datasets."""
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def __init__(self):
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workflow.Function.__init__(self, 'load_data', 'Cycle')
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def run(self):
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filename='fluents/data/CYCLE'
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if filename:
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return dataset.from_file(filename)
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##### WORKFLOW SPECIFIC FUNCTIONS ######
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class DiffKernelFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'diffkernel', 'Diffusion')
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def run(self, x, a):
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"""x is gene expression data, a is the network.
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"""
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#sanity check:
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g = a.asnetworkx()
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genes = x.get_identifiers(x.get_dim_name(1), sorted=True)
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W = nx.adj_matrix(g, nodelist=genes)
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X = x.asarray()
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Xc, mn_x = cx_utils.mat_center(X, ret_mn=True)
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out = []
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alpha=1.0
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beta = 1.0
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K = nx_utils.K_diffusion(W, alpha=alpha, beta=beta,normalised=True)
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Xp = scipy.dot(Xc, K) + mn_x
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# dataset
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row_ids = (x.get_dim_name(0),
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x.get_identifiers(x.get_dim_name(0),
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sorted=True))
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col_ids = (x.get_dim_name(1),
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x.get_identifiers(x.get_dim_name(1),
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sorted=True))
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xout = dataset.Dataset(Xp,
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(row_ids, col_ids),
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name=x.get_name()+'_diff'+str(alpha))
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out.append(xout)
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return out
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class RandDiffKernelFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'diffkernel', 'Rand. Diff.')
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def run(self, x, a):
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"""x is gene expression data, a is the network.
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"""
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#sanity check:
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g = a.asnetworkx()
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genes = x.get_identifiers(x.get_dim_name(1))
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# randomise nodelist
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genes = [genes[i] for i in cx_utils.randperm(x.shape[1])]
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W = nx.adj_matrix(g, nodelist=genes)
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X = x.asarray()
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Xc, mn_x = cx_utils.mat_center(X, ret_mn=True)
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out = []
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alpha=1.
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beta = 1.0
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K = nx_utils.K_diffusion(W, alpha=alpha, beta=beta,normalised=True)
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Xp = scipy.dot(Xc, K) + mn_x
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# dataset
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row_ids = (x.get_dim_name(0),
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x.get_identifiers(x.get_dim_name(0),
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sorted=True))
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col_ids = (x.get_dim_name(1),
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x.get_identifiers(x.get_dim_name(1),
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sorted=True))
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xout = dataset.Dataset(Xp,
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(row_ids, col_ids),
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name=x.get_name()+'_diff'+str(alpha))
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out.append(xout)
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return out
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class ModKernelFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'mokernel', 'Modularity')
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def run(self,x,a):
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X = x.asarray()
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g = a.asnetworkx()
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genes = x.get_identifiers(x.get_dim_name(1), sorted=True)
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W = nx.adj_matrix(g, nodelist=genes)
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out=[]
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alpha=.2
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Xc,mn_x = cx_utils.mat_center(X, ret_mn=True)
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K = nx_utils.K_modularity(W, alpha=alpha)
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Xp = scipy.dot(Xc, K)
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Xp = Xp + mn_x
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# dataset
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row_ids = (x.get_dim_name(0),
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x.get_identifiers(x.get_dim_name(0),
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sorted=True))
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col_ids = (x.get_dim_name(1),
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x.get_identifiers(x.get_dim_name(1),
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sorted=True))
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xout = dataset.Dataset(Xp,
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(row_ids,col_ids),
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name=x.get_name()+'_mod'+str(alpha))
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out.append(xout)
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return out
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class NCBIQuery(workflow.Function):
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def __init__(self, gene_id_name='gene_id'):
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self._gene_id_name = gene_id_name
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workflow.Function.__init__(self, 'query', 'NCBI')
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def run(self, selection):
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if not selection.has_key(self._gene_id_name):
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logger.log("notice", "Expected gene ids: %s, but got: %s" %(self._gene_id_name, selection.keys()))
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return None
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if len(selection[self._gene_id_name])==0:
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logger.log("notice", "No selected genes to query")
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return None
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logger.log("notice", "No selected genes to query")
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base = 'http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?'
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options = {r'&db=' : 'gene',
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r'&cmd=' : 'retrieve',
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r'&dopt=' : 'full_report'}
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gene_str = ''.join([gene + "+" for gene in selection[self._gene_id_name]])
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options[r'&list_uids='] = gene_str[:-1]
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opt_str = ''.join([key+value for key,value in options.items()])
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web_str = base + opt_str
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webbrowser.open(web_str)
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class KEGGQuery(workflow.Function):
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def __init__(self, org='hsa', gene_id_name='gene_id'):
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self._org=org
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self._gene_id_name = gene_id_name
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workflow.Function.__init__(self, 'query', 'KEGG')
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def run(self, selection):
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if not selection.has_key(self._gene_id_name):
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logger.log("notice", "Expected gene ids: %s, but got. %s" %(self._gene_id_name, selection.keys()))
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return None
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if len(selection[self._gene_id_name])==0:
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logger.log("notice", "No selected genes to query")
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return None
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base = r'http://www.genome.jp/dbget-bin/www_bget?'
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gene_str = ''.join([gene + "+" for gene in selection[self._gene_id_name]])
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gene_str = gene_str[:-1]
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gene_str = self._org + "+" + gene_str
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web_str = base + gene_str
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webbrowser.open(web_str)
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class LogFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'log', 'Log')
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def run(self, data):
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logger.log('notice', 'Taking the log of dataset %s' % data.get_name())
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d = data.copy()
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d._array = scipy.log(d._array)
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d._name = 'log(%s)' % data.get_name()
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return [d]
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class GoEnrichment(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'goenrich', 'Go Enrichment')
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def run(self, data):
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import rpy
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rpy.r.library("GOstats")
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# Get universe
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# Here, we are using a defined dataset to represent the universe
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if not 'gene_ids' in data:
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logger.log('notice', 'No dimension called [gene_ids] in dataset: %s', data.get_name())
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return
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universe = list(data.get_identifiers('gene_ids'))
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# Get current selection and validate
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curr_sel = main.project.get_selection()
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selected_genes = list(curr_sel['gene_ids'])
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if len(selected_genes)==0:
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logger.log('notice', 'This function needs a current selection!')
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return
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# Hypergeometric parameter object
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pval_cutoff = 0.99
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cond = False
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test_direction = 'over'
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params = rpy.r.new("GOHyperGParams",
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geneIds=selected_genes,
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annotation="hgu133a",
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ontology="BP",
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pvalueCutoff=pval_cutoff,
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conditional=cond,
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testDirection=test_direction
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)
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# run test
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# result.keys(): ['Count', 'Term', 'OddsRatio', 'Pvalue', 'ExpCount', 'GOBPID', 'Size']
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result = rpy.r.summary(rpy.r.hyperGTest(params))
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# dataset
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terms = result['GOBPID']
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pvals = scipy.log(scipy.asarray(result['Pvalue']))
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row_ids = ('go-terms', terms)
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col_ids = ('_john', ['_doe'])
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xout = dataset.Dataset(pvals,
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(row_ids, col_ids),
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name='P values (enrichment)')
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return [xout]
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