Displays correlation between sampling time points and CDR. It returns the two parameters of the linear fit, Pearson's r, p-value and standard error. If optional argument 'doplot' is False, the plot is not displayed.
(self, doplot=True)
| 1829 | return polter |
| 1830 | |
| 1831 | def plot_CDR_correlation(self, doplot=True): |
| 1832 | """ |
| 1833 | Displays correlation between sampling time points and CDR. It returns the two |
| 1834 | parameters of the linear fit, Pearson's r, p-value and standard error. If optional argument 'doplot' is |
| 1835 | False, the plot is not displayed. |
| 1836 | """ |
| 1837 | pel2, tol = self.get_gene(self.rootlane, ignore_log=True) |
| 1838 | pel = numpy.array([pel2[m] for m in self.pl])*tol |
| 1839 | dr2 = self.get_gene('_CDR')[0] |
| 1840 | dr = numpy.array([dr2[m] for m in self.pl]) |
| 1841 | po = scipy.stats.linregress(pel, dr) |
| 1842 | if doplot: |
| 1843 | pylab.scatter(pel, dr, s=9.0, alpha=0.7, c='r') |
| 1844 | pylab.xlim(min(pel), max(pel)) |
| 1845 | pylab.ylim(0, max(dr)*1.1) |
| 1846 | pylab.xlabel(self.rootlane) |
| 1847 | pylab.ylabel('CDR') |
| 1848 | xk = pylab.linspace(min(pel), max(pel), 50) |
| 1849 | pylab.plot(xk, po[1]+po[0]*xk, 'k--', linewidth=2.0) |
| 1850 | pylab.show() |
| 1851 | return po |
| 1852 | |
| 1853 | def plot_rootlane_correlation(self): |
| 1854 | """ |