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moxgreen

  • University of Turin
  • Torino Italy
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>Illumina Single End Apapter 1
ACACTCTTTCCCTACACGACGCTGTTCCATCT
>Illumina Single End Apapter 2
CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT
>Illumina Single End PCR Primer 1
AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCT
>Illumina Single End PCR Primer 2
CAAGCAGAAGACGGCATACGAGCTCTTCCGATCT
>Illumina Single End Sequencing Primer
ACACTCTTTCCCTACACGACGCTCTTCCGATCT
--- src/cxx/lib/io/Xml.cpp 2018-01-05 14:12:25.000000000 -0500
+++ src/cxx/lib/io/Xml.cpp 2018-01-05 14:12:35.000000000 -0500
@@ -168,19 +168,11 @@
if (!tree.empty())
{
unindex(*tree.begin(), treeWithIndexAttributes);
-#ifndef WIN32
- boost::property_tree::write_xml(os, treeWithIndexAttributes, boost::property_tree::xml_writer_make_settings(' ', 2));
-#else
boost::property_tree::write_xml(os, treeWithIndexAttributes, boost::property_tree::xml_writer_make_settings<std::string>(' ', 2));
"""
Partial Correlation in Python (clone of Matlab's partialcorr)
This uses the linear regression approach to compute the partial
correlation (might be slow for a huge number of variables). The
algorithm is detailed here:
http://en.wikipedia.org/wiki/Partial_correlation#Using_linear_regression
Taking X and Y two variables of interest and Z the matrix with all the variable minus {X, Y},