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Added SAM and conditioned enrichment analysis

This commit is contained in:
Arnar Flatberg 2007-08-14 16:16:31 +00:00
parent 8d4848d5fa
commit d510e092e3
1 changed files with 109 additions and 19 deletions
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126
workflows/smokers.py
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@ -25,11 +25,6 @@ class SmallTestWorkflow(workflow.Workflow):
#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())
@ -42,6 +37,7 @@ class SmallTestWorkflow(workflow.Workflow):
model.add_function(blmfuncs.PCA())
model.add_function(blmfuncs.PLS())
model.add_function(blmfuncs.LPLS())
model.add_function(SAM())
#model.add_function(bioconFuncs.SAM(app))
self.add_stage(model)
@ -60,6 +56,7 @@ class SmallTestWorkflow(workflow.Workflow):
# go.add_function(gobrowser.TTestFunction())
go.add_function(gobrowser.PlotDagFunction())
go.add_function(GoEnrichment())
go.add_function(GoEnrichmentCond())
self.add_stage(go)
# EXTRA PLOTS
@ -144,6 +141,58 @@ class DatasetLoadFunctionCYCLE(workflow.Function):
##### WORKFLOW SPECIFIC FUNCTIONS ######
class SAM(workflow.Function):
def __init__(self, id='sam', name='SAM'):
workflow.Function.__init__(self, id, name)
def run(self, x, y):
n_iter = 50 #B
alpha = 0.01 #cut off on qvals
###############
# Main function call
# setup prelimenaries
import rpy
rpy.r.library("siggenes")
rpy.r.library("multtest")
cl = scipy.dot(y.asarray(), scipy.diag([1,2,3]) ).sum(1)
data = x.asarray().T
sam = rpy.r.sam(data, cl=cl, B=n_iter, var_equal=False,med=False,s0=scipy.nan,rand=scipy.nan)
qvals = scipy.asarray(rpy.r.slot(sam, "p.value"))
pvals = scipy.asarray(rpy.r.slot(sam, "q.value"))
sam_index = (qvals<alpha).nonzero()[0]
# Update selection object
dim_name = x.get_dim_name(1)
sam_selection = x.get_identifiers(dim_name, indices=sam_index)
main.project.set_selection(dim_name, sam_selection)
sel = dataset.Selection('SAM selection')
sel.select(dim_name, sam_selection)
logger.log('notice','Number of significant varibles (SAM): %s' %len(sam_selection))
# ## OUTPUT ###
xcolname = x.get_dim_name(1) # genes
x_col_ids = [xcolname, x.get_identifiers(xcolname, sorted=True)]
sing_id = ['_john', ['0']] #singleton
D_qvals = dataset.Dataset(qvals, (x_col_ids, sing_id), name='q_vals')
D_pvals = dataset.Dataset(pvals, (x_col_ids, sing_id), name='p_vals')
# plots
s_indx = qvals.flatten().argsort()
s_ids = [x_col_ids[0],[x_col_ids[1][i] for i in s_indx]]
xindex = scipy.arange(len(qvals))
qvals_s = qvals.take(s_indx)
D_qs = dataset.Dataset(qvals_s, (s_ids, sing_id), name="sorted qvals")
Dind = dataset.Dataset(xindex, (s_ids, sing_id), name="dum")
st = plots.ScatterPlot(D_qs, Dind, 'gene_ids', '_john', '0', '0', s=10, name='SAM qvals')
return [D_qvals, D_pvals, D_qs, st, sel]
class DiffKernelFunction(workflow.Function):
@ -294,18 +343,6 @@ class KEGGQuery(workflow.Function):
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]
class GoEnrichment(workflow.Function):
def __init__(self):
workflow.Function.__init__(self, 'goenrich', 'Go Enrichment')
@ -320,6 +357,7 @@ class GoEnrichment(workflow.Function):
logger.log('notice', 'No dimension called [gene_ids] in dataset: %s', data.get_name())
return
universe = list(data.get_identifiers('gene_ids'))
logger.log('notice', 'Universe consists of %s gene ids from %s' %(len(universe), data.get_name()))
# Get current selection and validate
curr_sel = main.project.get_selection()
selected_genes = list(curr_sel['gene_ids'])
@ -328,7 +366,7 @@ class GoEnrichment(workflow.Function):
return
# Hypergeometric parameter object
pval_cutoff = 0.99
pval_cutoff = 0.9999
cond = False
test_direction = 'over'
params = rpy.r.new("GOHyperGParams",
@ -353,3 +391,55 @@ class GoEnrichment(workflow.Function):
(row_ids, col_ids),
name='P values (enrichment)')
return [xout]
class GoEnrichmentCond(workflow.Function):
""" Enrichment conditioned on dag structure."""
def __init__(self):
workflow.Function.__init__(self, 'goenrich', 'Go Cond. Enrich.')
def run(self, data):
import rpy
rpy.r.library("GOstats")
# Get universe
# Here, we are using a defined dataset to represent the universe
if not 'gene_ids' in data:
logger.log('notice', 'No dimension called [gene_ids] in dataset: %s', data.get_name())
return
universe = list(data.get_identifiers('gene_ids'))
logger.log('notice', 'Universe consists of %s gene ids from %s' %(len(universe), data.get_name()))
# Get current selection and validate
curr_sel = main.project.get_selection()
selected_genes = list(curr_sel['gene_ids'])
if len(selected_genes)==0:
logger.log('notice', 'This function needs a current selection!')
return
# Hypergeometric parameter object
pval_cutoff = 0.9999
cond = True
test_direction = 'over'
params = rpy.r.new("GOHyperGParams",
geneIds=selected_genes,
annotation="hgu133a",
ontology="BP",
pvalueCutoff=pval_cutoff,
conditional=cond,
testDirection=test_direction
)
# run test
# result.keys(): ['Count', 'Term', 'OddsRatio', 'Pvalue', 'ExpCount', 'GOBPID', 'Size']
result = rpy.r.summary(rpy.r.hyperGTest(params))
# dataset
terms = result['GOBPID']
pvals = scipy.log(scipy.asarray(result['Pvalue']))
row_ids = ('go-terms', terms)
col_ids = ('_john', ['_doe'])
xout = dataset.Dataset(pvals,
(row_ids, col_ids),
name='P values (enrichment)')
return [xout]