306 lines
11 KiB
Python
306 lines
11 KiB
Python
import gtk
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from laydi import dataset, logger, plots, workflow
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#import geneontology
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#import gostat
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from scipy import array, randn, log, ones
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import cPickle
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import networkx
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class TestWorkflow (workflow.Workflow):
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name = 'Test Workflow'
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ident = 'test'
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description = 'Test Gene Ontology Workflow. This workflow currently serves as a general testing workflow.'
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def __init__(self, app):
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workflow.Workflow.__init__(self, app)
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load = workflow.Stage('load', 'Load Data')
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load.add_function(CelFileImportFunction())
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load.add_function(DataLoadTestFunction(self))
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load.add_function(TestDataFunction())
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load.add_function(DatasetLoadFunction())
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load.add_function(SelectFunction())
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self.add_stage(load)
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preproc = workflow.Stage('preprocess', 'Preprocessing')
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preproc.add_function(DatasetLog())
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preproc.add_function(workflow.Function('rma', 'RMA'))
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self.add_stage(preproc)
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go = workflow.Stage('go', 'Gene Ontology Data')
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go.add_function(GODistanceFunction())
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go.add_function(ImagePlotFunction())
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self.add_stage(go)
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regression = workflow.Stage('regression', 'Regression')
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regression.add_function(workflow.Function('pls', 'PLS'))
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self.add_stage(regression)
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explore = workflow.Stage('explore', 'Explorative analysis')
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explore.add_function(PCAFunction(self))
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self.add_stage(explore)
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save = workflow.Stage('save', 'Save Data')
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save.add_function(DatasetSaveFunction())
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self.add_stage(save)
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class LoadAnnotationsFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'load-go-ann', 'Load Annotations')
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self.annotations = None
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def load_file(self, filename):
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f = open(filename)
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self.annotations = Annotations('genes', 'go-terms')
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logger.log('notice', 'Loading annotation file: %s' % filename)
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for line in f.readlines():
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val = line.split(' \t')
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if len(val) > 1:
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val = [v.strip() for v in val]
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retval.add_annotations('genes', val[0],
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'go-terms', set(val[1:]))
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def on_response(self, dialog, response):
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if response == gtk.RESPONSE_OK:
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logger.log('notice', 'Reading file: %s' % dialog.get_filename())
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self.load_file(dialog.get_filename())
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def run(self):
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btns = ('Open', gtk.RESPONSE_OK, \
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'Cancel', gtk.RESPONSE_CANCEL)
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dialog = gtk.FileChooserDialog('Open GO Annotation File',
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buttons=btns)
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dialog.connect('response', self.on_response)
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dialog.run()
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dialog.destroy()
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return [self.annotations]
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class GODistanceFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'go_diatance', 'GO Distances')
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self.output = None
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def run(self, data):
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logger.log('debug', 'datatype: %s' % type(data))
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if not type(data) == Annotations:
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return None
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logger.log('debug', 'dimensions: %s' % data.dimensions)
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genes = data.get_ids('genes')
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gene_distances = array((len(genes), len(genes)))
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return gene_distances
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class ImagePlotFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'image', 'Show Image')
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def run(self, data):
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return [plots.ImagePlot(data, name='foo')]
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class TestDataFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'test_data', 'Generate Test Data')
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def run(self):
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logger.log('notice', 'Injecting foo test data')
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x = randn(500,15)
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X = dataset.Dataset(x)
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p = plots.ScatterPlot(X, X, 'rows', 'rows', '0_1', '0_2',name='scatter')
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p2 = plots.ScatterMarkerPlot(X, X, 'rows', 'rows', '0_1', '0_2',name='marker')
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graph = networkx.XGraph()
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for x in 'ABCDEF':
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for y in 'ADE':
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graph.add_edge(x, y, 3)
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ds = dataset.GraphDataset(array(networkx.adj_matrix(graph)))
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ds_plot = plots.NetworkPlot(ds)
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cds = dataset.CategoryDataset(ones([3, 3]))
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ds_scatter = plots.ScatterMarkerPlot(ds, ds, 'rows_0', 'rows_0', '0_1', '0_2')
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lp = plots.LineViewPlot(X,major_axis=0)
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vp = plots.VennPlot()
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return [X, ds, p, ds_plot, ds_scatter, p2, cds, lp, vp]
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class SelectFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'select', 'Select')
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def run(self, data):
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s = dataset.Selection('Arbitrary selection')
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s.select('rows', ['0_1', '0_2'])
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return [s]
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class DatasetLog(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.asarray()
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d = log(d)
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new_data_name = 'log(%s)' % data.get_name()
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ds = dataset.Dataset(d, name=new_data_name)
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return [ds]
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class DatasetLoadFunction(workflow.Function):
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"""Loader for previously pickled Datasets."""
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def __init__(self):
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workflow.Function.__init__(self, 'load_data', 'Load Pickled Dataset')
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def run(self):
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chooser = gtk.FileChooserDialog(title="Select cel files...", parent=None,
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action=gtk.FILE_CHOOSER_ACTION_OPEN,
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buttons=(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL,
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gtk.STOCK_OPEN, gtk.RESPONSE_OK))
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pkl_filter = gtk.FileFilter()
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pkl_filter.set_name("Python pickled data files (*.pkl)")
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pkl_filter.add_pattern("*.[pP][kK][lL]")
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all_filter = gtk.FileFilter()
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all_filter.set_name("All Files (*.*)")
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all_filter.add_pattern("*")
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chooser.add_filter(pkl_filter)
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chooser.add_filter(all_filter)
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try:
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if chooser.run() == gtk.RESPONSE_OK:
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return [cPickle.load(open(chooser.get_filename()))]
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finally:
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chooser.destroy()
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class DatasetSaveFunction(workflow.Function):
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"""QND way to save data to file for later import to this program."""
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def __init__(self):
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workflow.Function.__init__(self, 'save_data', 'Save Pickled Dataset')
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def run(self):
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if not data:
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logger.log("notice", "No data to save.")
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return
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else:
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data = data[0]
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chooser = gtk.FileChooserDialog(title="Save pickled data...", parent=None,
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action=gtk.FILE_CHOOSER_ACTION_SAVE,
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buttons=(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL,
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gtk.STOCK_SAVE, gtk.RESPONSE_OK))
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pkl_filter = gtk.FileFilter()
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pkl_filter.set_name("Python pickled data files (*.pkl)")
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pkl_filter.add_pattern("*.[pP][kK][lL]")
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all_filter = gtk.FileFilter()
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all_filter.set_name("All Files (*.*)")
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all_filter.add_pattern("*")
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chooser.add_filter(pkl_filter)
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chooser.add_filter(all_filter)
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chooser.set_current_name(data.get_name() + ".pkl")
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try:
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if chooser.run() == gtk.RESPONSE_OK:
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cPickle.dump(data, open(chooser.get_filename(), "w"), protocol=2)
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logger.log("notice", "Saved data to %r." % chooser.get_filename())
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finally:
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chooser.destroy()
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class CelFileImportFunction(workflow.Function):
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"""Loads AffyMetrix .CEL-files into matrix."""
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def __init__(self):
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workflow.Function.__init__(self, 'cel_import', 'Import Affy')
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def run(self, data):
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import rpy
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chooser = gtk.FileChooserDialog(title="Select cel files...", parent=None,
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action=gtk.FILE_CHOOSER_ACTION_OPEN,
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buttons=(gtk.STOCK_CANCEL, gtk.RESPONSE_CANCEL,
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gtk.STOCK_OPEN, gtk.RESPONSE_OK))
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chooser.set_select_multiple(True)
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cel_filter = gtk.FileFilter()
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cel_filter.set_name("Cel Files (*.cel)")
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cel_filter.add_pattern("*.[cC][eE][lL]")
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all_filter = gtk.FileFilter()
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all_filter.set_name("All Files (*.*)")
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all_filter.add_pattern("*")
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chooser.add_filter(cel_filter)
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chooser.add_filter(all_filter)
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try:
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if chooser.run() == gtk.RESPONSE_OK:
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rpy.r.library("affy")
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silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
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silent_eval('E <- ReadAffy(filenames=c("%s"))' % '", "'.join(chooser.get_filenames()))
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silent_eval('E <- rma(E)')
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m = rpy.r('m <- E@exprs')
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vector_eval = rpy.with_mode(rpy.VECTOR_CONVERSION, rpy.r)
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rownames = vector_eval('rownames(m)')
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colnames = vector_eval('colnames(m)')
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# We should be nice and clean up after ourselves
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rpy.r.rm(["E", "m"])
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if m:
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data = dataset.Dataset(m, (('ids', rownames), ('filename', colnames)), name="AffyMatrix Data")
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plot = plots.LinePlot(data, "Gene profiles")
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return [data, plot]
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else:
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logger.log("notice", "No data loaded from importer.")
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finally:
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chooser.destroy()
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class DataLoadTestFunction(workflow.Function):
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def __init__(self, wf):
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workflow.Function.__init__(self, 'datadirload', 'Load from datadir')
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self._wf = wf
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def run(self):
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print self._wf.get_data_file_name('smoker-x.ftsv')
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fn = self._wf.get_data_file_name('smoker-x.ftsv')
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if fn:
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fd = open(fn)
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ds = dataset.read_ftsv(fd)
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return [ds]
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else:
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print "Cannot find file %s" % fn
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return []
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class PCAFunction(workflow.Function):
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"""Generic PCA function."""
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def __init__(self, wf):
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workflow.Function.__init__(self, 'pca', 'PCA')
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self._workflow = wf
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def run(self, data):
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import rpy
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dim_2, dim_1 = data.get_dim_names()
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silent_eval = rpy.with_mode(rpy.NO_CONVERSION, rpy.r)
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rpy.with_mode(rpy.NO_CONVERSION, rpy.r.assign)("m", data.asarray())
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silent_eval("t = prcomp(t(m))")
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T_ids = map(str, range(1, rpy.r("dim(t$x)")[1]+1))
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T = dataset.Dataset(rpy.r("t$x"), [(dim_1, data.get_identifiers(dim_1)),
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("component", T_ids)], name="T")
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P = dataset.Dataset(rpy.r("t$rotation"), [(dim_2, data.get_identifiers(dim_2)),
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("component", T_ids)], name="P")
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# cleanup
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rpy.r.rm(["t", "m"])
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loading_plot = plots.ScatterMarkerPlot(P, P, 'ids','component','1','2', "Loadings")
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score_plot = plots.ScatterMarkerPlot(T, T,'filename','component','1','2', "Scores")
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return [T, P, loading_plot, score_plot]
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