Step 1: convert .d files to mzML format

Note: msconvert works for many conversions, but for .d, one must use the Agilent DLLS, which only works on Windows.

Step 2: Create features

Option 1 - use MZmine2 software

create batch queue file using MZmine GUI
save this queue file as .xml
run MZmine from command line with .xml input option
export XML file containing resulting peak list

Option 2 - run python code with pymzml package

process_raw_data.py

Step 3: Run prediction analysis

run_prediction_in_python.py

Name		Name	Last commit message	Last commit date
Latest commit History 122 Commits
.gitignore		.gitignore
Hubbard_simple super SL code_CC.R		Hubbard_simple super SL code_CC.R
Pythontest_ryp.py		Pythontest_ryp.py
README.md		README.md
Rtest_rpy.R		Rtest_rpy.R
Simple learner prediction.R		Simple learner prediction.R
clean_LCMS_data_functions.R		clean_LCMS_data_functions.R
clean_clinical_data_functions_v2.R		clean_clinical_data_functions_v2.R
clean_data_functions.R		clean_data_functions.R
create_additional_figures.py		create_additional_figures.py
create_data_for_analysis.R		create_data_for_analysis.R
cross_val_utils.py		cross_val_utils.py
def_build_2D_binned_array.pyx		def_build_2D_binned_array.pyx
explore_raw_data.py		explore_raw_data.py
extract_LCMS_features.py		extract_LCMS_features.py
extract_clinical_info_from_database.R		extract_clinical_info_from_database.R
find_VIM clinical variables_no adjustments.R		find_VIM clinical variables_no adjustments.R
investigate abundance data from CO.R		investigate abundance data from CO.R
investigate data content (scratch).R		investigate data content (scratch).R
investigate person identifier matching.R		investigate person identifier matching.R
investigate_abundance_filtering.R		investigate_abundance_filtering.R
investigate_clinical_data.R		investigate_clinical_data.R
main_code.R		main_code.R
matplotlib_style_utils.py		matplotlib_style_utils.py
my_cython_functions.c		my_cython_functions.c
my_cython_functions.pyx		my_cython_functions.pyx
prediction with subsetting outside of CV.R		prediction with subsetting outside of CV.R
prediction_functions_v2.R		prediction_functions_v2.R
prediction_in_python.py		prediction_in_python.py
prediction_with_LCMS_from_python.R		prediction_with_LCMS_from_python.R
prediction_with_specified_compounds.R		prediction_with_specified_compounds.R
prepare_MassHunter_data.py		prepare_MassHunter_data.py
prepare_data_from_MZmine.R		prepare_data_from_MZmine.R
prepare_python_output_for_analysis.R		prepare_python_output_for_analysis.R
process_raw_data_and_do_prediction.py		process_raw_data_and_do_prediction.py
python_scratch.py		python_scratch.py
run_extract_LCMS_features.bash		run_extract_LCMS_features.bash
run_prediction_in_python.py		run_prediction_in_python.py
scratch.R		scratch.R
scratch_prediction.R		scratch_prediction.R
scratch_prediction_v2.R		scratch_prediction_v2.R
setup.py		setup.py
summarize_clinical_data.R		summarize_clinical_data.R
summarize_clinical_data_v2.R		summarize_clinical_data_v2.R
summarize_clinical_functions.R		summarize_clinical_functions.R
test2.pyx		test2.pyx

cotterman/DengueCode

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Step 1: convert .d files to mzML format

Step 2: Create features

Option 1 - use MZmine2 software

Option 2 - run python code with pymzml package

Step 3: Run prediction analysis

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