Implemented Limma function for Affy workflow.
Extended ScatterPlot to take two datasets and updated code using it.
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033d4d5333
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@ -1,5 +1,5 @@
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import pygtk
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pygtk.require('2.0')
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# pygtk.require('2.0')
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import gtk
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import sys
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import os
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@ -429,18 +429,19 @@ class LinePlot(Plot):
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class ScatterPlot(Plot):
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def __init__(self, dataset, id_dim, sel_dim, id_1, id_2, name="Scatter plot"):
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def __init__(self, dataset_1, dataset_2, id_dim, sel_dim, id_1, id_2, name="Scatter plot"):
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Plot.__init__(self, name)
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fig = Figure(figsize=(5,4), dpi=72)
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self.ax = ax = fig.add_subplot(111)
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self.current_dim = id_dim
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# testing testing
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self.dataset = dataset
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x_index = dataset[sel_dim][id_1]
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y_index = dataset[sel_dim][id_2]
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self.dataset_1 = dataset_1
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self.xaxis_data = dataset._array[:,x_index]
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self.yaxis_data = dataset._array[:,y_index]
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x_index = dataset_1[sel_dim][id_1]
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y_index = dataset_2[sel_dim][id_2]
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self.xaxis_data = dataset_1._array[:,x_index]
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self.yaxis_data = dataset_2._array[:,y_index]
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ax.plot(self.xaxis_data,self.yaxis_data,'og')
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ax.set_title(self.get_title())
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ax.set_xlabel("%s - %s" % (sel_dim, id_1))
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@ -478,13 +479,13 @@ class ScatterPlot(Plot):
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#logger.log('debug','Selection y_start bigger than y_end')
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index =scipy.nonzero((xdata>x1) & (xdata<x2) & (ydata<y1) & (ydata>y2))
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ids = self.dataset.get_identifiers(self.current_dim, index)
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ids = self.dataset_1.get_identifiers(self.current_dim, index)
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self.selection_listener(self.current_dim, ids)
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def selection_changed(self, selection):
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ids = selection[self.current_dim] # current identifiers
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index = self.dataset.get_indices(self.current_dim, ids)
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index = self.dataset_1.get_indices(self.current_dim, ids)
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xdata_new = scipy.take(self.xaxis_data,index) #take data
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ydata_new = scipy.take(self.yaxis_data,index)
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self.ax.clear()
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@ -86,6 +86,29 @@ CEL\tsex\tage\tinfected
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self.assertEquals(set(['F', 'M', 'I', 'N']), set(dataset.get_categories()))
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def testGetFactors(self):
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cel_data = """\
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CEL\tsex\tage\tinfected
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02-05-33\tF\t8\tI
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02-05-34\tF\t9\tN
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02-05-35\tM\t8\tI
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"""
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dataset = PhenotypeDataset(cel_data)
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self.assertEquals(set(["sex", "infected"]), dataset.get_factors(["F", "I"]))
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def testGetCategoryVariable(self):
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"""Can get set/unset list for given category."""
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cel_data = """\
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CEL\tsex\tage\tinfected
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02-05-33\tF\t8\tI
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02-05-34\tF\t9\tN
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02-05-35\tM\t8\tI
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"""
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dataset = PhenotypeDataset(cel_data)
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self.assertEquals([1, 1, 0], dataset.get_category_variable("F"))
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self.assertEquals([0, 0, 1], dataset.get_category_variable("M"))
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self.assertEquals([1, 0, 1], dataset.get_category_variable("I"))
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self.assertEquals([0, 1, 0], dataset.get_category_variable("N"))
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if __name__=='__main__':
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@ -1,4 +1,5 @@
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import gtk
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import os.path
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from system import dataset, logger, plots, workflow, dialogs
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from scipy import randn, array, transpose, zeros
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import cPickle
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@ -17,9 +18,12 @@ class AffyWorkflow (workflow.Workflow):
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load.add_function(PhenotypeImportFunction())
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load.add_function(TestDataFunction())
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load.add_function(DatasetLoadFunction())
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load.add_function(ContrastMatrixGenerateFunction())
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self.add_stage(load)
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significance = workflow.Stage('significance', 'Significance analysis')
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significance.add_function(LimmaFunction())
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self.add_stage(significance)
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explore = workflow.Stage('explore', 'Explorative analysis')
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explore.add_function(PCAFunction(self))
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explore.add_function(PrintFunction())
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@ -106,18 +110,86 @@ class DatasetSaveFunction(workflow.Function):
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chooser.destroy()
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class ContrastMatrixGenerateFunction(workflow.Function):
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class LimmaFunction(workflow.Function):
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def __init__(self):
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workflow.Function.__init__(self, 'contrast_create', 'Create contrast matrix')
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workflow.Function.__init__(self, 'limma', 'Limma')
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def run(self, data):
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def run(self, affy, data):
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response = dialogs.get_text('Enter contrasts...', """\
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Enter comma-separated list of contrasts.
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Available categories: %s
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Example: Y-N, M-F""" % ", ".join(data.get_categories()))
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logger.log("notice", "contrasts selected: %s" % response)
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categories = []
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[categories.extend(s.split("-")) for s in response.split(",")]
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categories = [s.strip() for s in categories]
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factors = data.get_factors(categories)
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if not factors:
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logger.log("warning", "nothing to do, no factors")
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table = data.get_phenotype_table()
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cn = table[0]
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entries = zip(*table[1:])
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rn = entries[0]
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import rpy
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rpy.r.library("limma")
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rpy.r("a <- matrix('kalle', nrow=%d, ncol=%d)" % (len(rn), len(cn)))
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for i, row in enumerate(entries):
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for j, entry in enumerate(row):
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rpy.r("a[%d, %d] <- '%s'" % (j+1, i+1, entry))
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rpy.r.assign("rn", rn)
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rpy.r.assign("cn", cn)
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rpy.r("rownames(a) <- rn")
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rpy.r("colnames(a) <- cn")
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unique_categories = list(set(categories))
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# compose fancy list of factors for design matrix
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rpy.r("design <- matrix(0, nrow=%d, ncol=%d)" % (len(rn), len(unique_categories)))
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for i, category in enumerate(unique_categories):
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for j, value in enumerate(data.get_category_variable(category)):
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rpy.r("design[%d, %d] <- %d" % (j+1, i+1, value))
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rpy.r.assign("colnames.design", unique_categories)
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rpy.r("colnames(design) <- colnames.design")
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rpy.r.assign("expr", affy.asarray())
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rpy.r("fit <- lmFit(expr, design)")
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# FIXME: might be a case for code injection...
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string = "contrast.matrix <- makeContrasts(%s, levels=design)" % response
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rpy.r(string)
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rpy.r("fit2 <- contrasts.fit(fit, contrast.matrix)")
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rpy.r("fit2 <- eBayes(fit2)")
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coeff = rpy.r("fit2$coefficients")
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amean = rpy.r("fit2$Amean")
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padj = rpy.r("p.adjust(fit2$p.value, method='fdr')")
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dim_1, dim_2 = affy.get_dim_names()
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coeff_data = dataset.Dataset(coeff, [(dim_1, affy.get_identifiers(dim_1)),
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("contrast", [response])],
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name="Coefficients")
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amean_data = dataset.Dataset(array(amean), [("average", ["average"]),
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(dim_1, affy.get_identifiers(dim_1))],
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name="Average Intensity")
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padj_data = dataset.Dataset(padj, [(dim_1, affy.get_identifiers(dim_1)),
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("contrast", [response])],
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name="Adjusted P-value")
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vulcano_plot = plots.ScatterPlot(coeff_data, padj_data, dim_1,
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'contrast', response, response,
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name="Vulcano plot")
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return [coeff_data, amean_data, padj_data, vulcano_plot]
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class CelFileImportFunction(workflow.Function):
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"""Loads Affymetrics .CEL-files into matrix."""
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@ -147,7 +219,6 @@ class CelFileImportFunction(workflow.Function):
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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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@ -223,9 +294,9 @@ class PCAFunction(workflow.Function):
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# cleanup
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rpy.r.rm(["t", "m"])
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loading_plot = plots.ScatterPlot(P, dim_2, component_dim, '1', '2',
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loading_plot = plots.ScatterPlot(P, P, dim_2, component_dim, '1', '2',
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"Loadings")
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score_plot = plots.ScatterPlot(T, dim_1,component_dim, '1', '2',
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score_plot = plots.ScatterPlot(T, T, dim_1,component_dim, '1', '2',
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"Scores")
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return [T, P, loading_plot, score_plot]
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@ -239,7 +310,7 @@ class PhenotypeDataset(dataset.Dataset):
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col_names = rows[0][1:]
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phenotypes = []
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categories = {}
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self._categories = set()
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self._categories = {}
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for col_name, column in zip(col_names, columns[1:]):
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try:
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@ -257,7 +328,7 @@ class PhenotypeDataset(dataset.Dataset):
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entries[entry].append(i)
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for key in keys:
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self._categories.add(key)
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self._categories[key] = col_name
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z = zeros(len(column))
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for i in entries[key]:
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z[i] = 1
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@ -288,4 +359,29 @@ class PhenotypeDataset(dataset.Dataset):
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If factor 'sick' had possibilites Y/N, and 'sex' M/F, the
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categories would be Y, N, M and F.
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"""
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return self._categories
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return self._categories.keys()
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def get_factors(self, categories):
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factors = set()
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for c in categories:
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factors.add(self._categories[c])
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return factors
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def get_category_variable(self, category):
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# abit brute-force, but does the job until optimization is
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# necessary
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factor = self._categories[category]
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variable = []
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for column in zip(*self.get_phenotype_table()):
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if column[0] == factor:
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for entry in column[1:]:
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if entry == category:
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variable.append(1)
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else:
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variable.append(0)
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return variable
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@ -248,7 +248,7 @@ class PCAFunction(workflow.Function):
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# cleanup
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rpy.r.rm(["t", "m"])
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loading_plot = plots.ScatterPlot(P,'ids','component','1','2', "Loadings")
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score_plot = plots.ScatterPlot(T,'filename','component','1','2', "Scores")
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loading_plot = plots.ScatterPlot(P, P, ,'ids','component','1','2', "Loadings")
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score_plot = plots.ScatterPlot(T, T,'filename','component','1','2', "Scores")
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return [T, P, loading_plot, score_plot]
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@ -102,9 +102,9 @@ class PCAFunction(Function):
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#tsq = dataset.Dataset(tsq,[singel_def,data_ids[1])
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## plots
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loading_plot1 = plots.ScatterPlot(P,'genes','comp','1','2')
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loading_plot2 = plots.ScatterPlot(P,'genes','comp','3','4')
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score_plot = plots.ScatterPlot(T,'samples','comp','1','2')
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loading_plot1 = plots.ScatterPlot(P,P,'genes','comp','1','2')
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loading_plot2 = plots.ScatterPlot(P,P,'genes','comp','3','4')
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score_plot = plots.ScatterPlot(T,T,'samples','comp','1','2')
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return [T,P,E,loading_plot1,loading_plot2,score_plot]
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