src/scBONITA/singleCell.py
def __filterData(self, threshold):
        """Filter data based on CV cutoffs"""
        self.cvGenes = []
        if threshold is not None:
            for i in range(0, self.expMat.get_shape()[0]):
                rowData = list(self.expMat.getrow(i).todense())
                if np.std(rowData) / np.mean(rowData) >= threshold:
                    self.cvGenes.append(self.geneList[i])
        else:
            self.cvGenes = copy.deepcopy(self.geneList)
Filter data based on CV cutoffs
Filter data based on CV cutoffs
Filter data based on CV cutoffs
def __filterData(self, threshold):
        self.cvGenes = []
        if threshold is not None:
            for i in range(0, self.expMat.get_shape()[0]):
                rowData = list(self.expMat.getrow(i).todense())
                if np.std(rowData) / np.mean(rowData) >= threshold:
                    self.cvGenes.append(self.geneList[i])
        else:
            self.cvGenes = copy.deepcopy(self.geneList)
def __filterData ( self , threshold ) : self . cvGenes = [ ] if threshold is not None : for i in range ( 0 , self . expMat . get_shape ( ) [ 0 ] ) : rowData = list ( self . expMat . getrow ( i ) . todense ( ) ) if np . std ( rowData ) / np . mean ( rowData ) >= threshold : self . cvGenes . append ( self . geneList [ i ] ) else : self . cvGenes = copy . deepcopy ( self . geneList )
Filter data based on CV cutoffs
Filter data based on CV cutoffs
"""Filter data based on CV cutoffs"""
self
threshold