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laydi/workflows/smokers.py

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import sys,os
import webbrowser
from fluents import logger, plots,workflow,dataset
from fluents.lib import blmfuncs,nx_utils,validation,engines,cx_stats,cx_utils
import gobrowser, geneontology
import scipy
import networkx as nx
class SmallTestWorkflow(workflow.Workflow):
name = 'Smokers'
ident = 'smokers'
description = 'A small test workflow for gene expression analysis.'
def __init__(self, app):
workflow.Workflow.__init__(self, app)
# DATA IMPORT
load = workflow.Stage('load', 'Data')
load.add_function(DatasetLoadFunctionSmokerSmall())
load.add_function(DatasetLoadFunctionSmokerMedium())
load.add_function(DatasetLoadFunctionSmokerFull())
#load.add_function(DatasetLoadFunctionCYCLE())
self.add_stage(load)
# PREPROCESSING
prep = workflow.Stage('prep', 'Preprocessing')
prep.add_function(LogFunction())
self.add_stage(prep)
# NETWORK PREPROCESSING
net = workflow.Stage('net', 'Network integration')
net.add_function(DiffKernelFunction())
net.add_function(ModKernelFunction())
#net.add_function(RandDiffKernelFunction())
self.add_stage(net)
# BLM's
model = workflow.Stage('models', 'Models')
model.add_function(blmfuncs.PCA())
model.add_function(blmfuncs.PLS())
#model.add_function(bioconFuncs.SAM(app))
self.add_stage(model)
query = workflow.Stage('query', 'Gene Query')
query.add_function(NCBIQuery())
query.add_function(KEGGQuery())
self.add_stage(query)
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# Gene Ontology
go = workflow.Stage('go', 'Gene Ontology')
go.add_function(gobrowser.LoadGOFunction())
go.add_function(gobrowser.GOWeightFunction())
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go.add_function(gobrowser.DistanceToSelectionFunction())
go.add_function(gobrowser.TTestFunction())
go.add_function(gobrowser.PlotDagFunction())
self.add_stage(go)
# EXTRA PLOTS
#plt = workflow.Stage('net', 'Network')
#plt.add_function(nx_analyser.KeggNetworkAnalyser())
#self.add_stage(plt)
logger.log('debug', 'Small test workflow is now active')
class DatasetLoadFunctionSmokerSmall(workflow.Function):
"""Loader for all ftsv files of smokers small datasets."""
def __init__(self):
workflow.Function.__init__(self, 'load_small', 'Smoker (Small)')
def run(self):
path = 'data/smokers-small/'
files = os.listdir(path)
out = []
for fname in files:
if fname.endswith('.ftsv'):
input_file = open(os.path.join(path, fname))
out.append(dataset.read_ftsv(input_file))
return out
class DatasetLoadFunctionSmokerMedium(workflow.Function):
"""Loader for all ftsv files of smokers small datasets."""
def __init__(self):
workflow.Function.__init__(self, 'load_medium', 'Smoker (Medium)')
def run(self):
path = 'data/smokers-medium/'
files = os.listdir(path)
out = []
for fname in files:
if fname.endswith('.ftsv'):
input_file = open(os.path.join(path, fname))
out.append(dataset.read_ftsv(input_file))
return out
class DatasetLoadFunctionSmokerFull(workflow.Function):
"""Loader for all ftsv files of smokers small datasets."""
def __init__(self):
workflow.Function.__init__(self, 'load_full', 'Smoker (Full)')
def run(self):
path = 'data/smokers-full/'
files = os.listdir(path)
out = []
for fname in files:
if fname.endswith('.ftsv'):
input_file = open(os.path.join(path, fname))
out.append(dataset.read_ftsv(input_file))
return out
class DatasetLoadFunctionCYCLE(workflow.Function):
"""Loader for pickled CYCLE datasets."""
def __init__(self):
workflow.Function.__init__(self, 'load_data', 'Cycle')
def run(self):
filename='fluents/data/CYCLE'
if filename:
return dataset.from_file(filename)
##### WORKFLOW SPECIFIC FUNCTIONS ######
class DiffKernelFunction(workflow.Function):
def __init__(self):
workflow.Function.__init__(self, 'diffkernel', 'Diffusion')
def run(self, x, a):
"""x is gene expression data, a is the network.
"""
#sanity check:
g = a.asnetworkx()
genes = x.get_identifiers(x.get_dim_name(1), sorted=True)
W = nx.adj_matrix(g, nodelist=genes)
X = x.asarray()
Xc, mn_x = cx_utils.mat_center(X, ret_mn=True)
out = []
alpha=1.0
beta = 1.0
K = nx_utils.K_diffusion(W, alpha=alpha, beta=beta,normalised=True)
Xp = scipy.dot(Xc, K) + mn_x
# dataset
row_ids = (x.get_dim_name(0),
x.get_identifiers(x.get_dim_name(0),
sorted=True))
col_ids = (x.get_dim_name(1),
x.get_identifiers(x.get_dim_name(1),
sorted=True))
xout = dataset.Dataset(Xp,
(row_ids, col_ids),
name=x.get_name()+'_diff'+str(alpha))
out.append(xout)
return out
class RandDiffKernelFunction(workflow.Function):
def __init__(self):
workflow.Function.__init__(self, 'diffkernel', 'Rand. Diff.')
def run(self, x, a):
"""x is gene expression data, a is the network.
"""
#sanity check:
g = a.asnetworkx()
genes = x.get_identifiers(x.get_dim_name(1))
# randomise nodelist
genes = [genes[i] for i in cx_utils.randperm(x.shape[1])]
W = nx.adj_matrix(g, nodelist=genes)
X = x.asarray()
Xc, mn_x = cx_utils.mat_center(X, ret_mn=True)
out = []
alpha=1.
beta = 1.0
K = nx_utils.K_diffusion(W, alpha=alpha, beta=beta,normalised=True)
Xp = scipy.dot(Xc, K) + mn_x
# dataset
row_ids = (x.get_dim_name(0),
x.get_identifiers(x.get_dim_name(0),
sorted=True))
col_ids = (x.get_dim_name(1),
x.get_identifiers(x.get_dim_name(1),
sorted=True))
xout = dataset.Dataset(Xp,
(row_ids, col_ids),
name=x.get_name()+'_diff'+str(alpha))
out.append(xout)
return out
class ModKernelFunction(workflow.Function):
def __init__(self):
workflow.Function.__init__(self, 'mokernel', 'Modularity')
def run(self,x,a):
X = x.asarray()
g = a.asnetworkx()
genes = x.get_identifiers(x.get_dim_name(1), sorted=True)
W = nx.adj_matrix(g, nodelist=genes)
out=[]
alpha=.2
Xc,mn_x = cx_utils.mat_center(X, ret_mn=True)
K = nx_utils.K_modularity(W, alpha=alpha)
Xp = scipy.dot(Xc, K)
Xp = Xp + mn_x
# dataset
row_ids = (x.get_dim_name(0),
x.get_identifiers(x.get_dim_name(0),
sorted=True))
col_ids = (x.get_dim_name(1),
x.get_identifiers(x.get_dim_name(1),
sorted=True))
xout = dataset.Dataset(Xp,
(row_ids,col_ids),
name=x.get_name()+'_mod'+str(alpha))
out.append(xout)
return out
class NCBIQuery(workflow.Function):
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def __init__(self, gene_id_name='gene_id'):
self._gene_id_name = gene_id_name
workflow.Function.__init__(self, 'query', 'NCBI')
def run(self, selection):
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if not selection.has_key(self._gene_id_name):
logger.log("notice", "Expected gene ids: %s, but got: %s" %(self._gene_id_name, selection.keys()))
return None
if len(selection[self._gene_id_name])==0:
logger.log("notice", "No selected genes to query")
return None
logger.log("notice", "No selected genes to query")
base = 'http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?'
options = {r'&db=' : 'gene',
r'&cmd=' : 'retrieve',
r'&dopt=' : 'full_report'}
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gene_str = ''.join([gene + "+" for gene in selection[self._gene_id_name]])
options[r'&list_uids='] = gene_str[:-1]
opt_str = ''.join([key+value for key,value in options.items()])
web_str = base + opt_str
webbrowser.open(web_str)
class KEGGQuery(workflow.Function):
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def __init__(self, org='hsa', gene_id_name='gene_id'):
self._org=org
self._gene_id_name = gene_id_name
workflow.Function.__init__(self, 'query', 'KEGG')
def run(self, selection):
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if not selection.has_key(self._gene_id_name):
logger.log("notice", "Expected gene ids: %s, but got. %s" %(self._gene_id_name, selection.keys()))
return None
if len(selection[self._gene_id_name])==0:
logger.log("notice", "No selected genes to query")
return None
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]])
gene_str = gene_str[:-1]
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gene_str = self._org + "+" + gene_str
web_str = base + gene_str
webbrowser.open(web_str)
class LogFunction(workflow.Function):
def __init__(self):
workflow.Function.__init__(self, 'log', 'Log')
def run(self, data):
logger.log('notice', 'Taking the log of dataset %s' % data.get_name())
d = data.copy()
d._array = scipy.log(d._array)
d._name = 'log(%s)' % data.get_name()
return [d]