Alignment executables ====================== :mod:`FeatureAlignment` executable ------------------- The Feature Alignment executable can be run as :: python feature_alignment.py and the for help please use :: python feature_alignment.py --help Some of the most used options are the following fdr_cutoff """""""""" This is the seeding score cutoff, if a precursor has an identification in one run with at least this score, it will be included for alignment. max_fdr_quality """""""""""""""" This is the extension score cutoff. During each step of the algorithm, a peakgroup from a new run is added to the initial seed (see above). Only if the additional peakgroup in the new run has a score better than max_fdr_quality will it be included in the final result. target_fdr """""""""" Experimental option for dynamic parameter estimation of the fdr_cutoff parameter. If you want to use this, please turn off fdr_cutoff (but max_fdr_quality still needs to be set). method """""" Defines the method to use for the clustering. Available options are * best_overall * best_cluster_score * global_best_cluster_score * global_best_overall * LocalMST * LocalMSTAllCluster Note that the MST options will perform a local, MST guided alignment while the other options will use a reference-guided alignment. The global option will also move peaks which are below the selected FDR threshold (while the best_overall and best_cluster_score will not touch any peak that is below fdr_cutoff). realign_method """""""""""""" Method to use to re-align retention times between pairs of runs. The following options are available: * None: use the raw RT from the file (not recommended) * diRT: use only deltaiRT from the input file * linear: perform a linear regression using best peakgroups * splineR: perform a spline fit using R (this feature relies on the rpy2 package) * splineR_external: perform a spline fit using R (start an R process using the command line, not tested under Windows) * splinePy: use Python native spline from scikits.datasmooth (not recommended, very slow) * nonCVSpline, CVSpline: splines with and without cross-validation from scipy.interpolate * lowess: use Robust locally weighted regression (lowess smoother) * earth : use Multivariate Adaptive Regression Splines using py-earth * WeightedNearestNeighbour: the weighted RT of the nearest neighbours is used * SmoothLLDMedian: a local kernel of linear differences is computed Recommended options are CVSpline and splineR and splineR (if you have R). Both WeightedNearestNeighbour and SmoothLLDMedian gave acceptable results. :mod:`FeatureAlignment` Module ------------------- .. autoclass:: feature_alignment.AlignmentStatistics :members: :undoc-members: :show-inheritance: .. autoclass:: feature_alignment.Experiment :members: :undoc-members: :show-inheritance: .. autofunction:: feature_alignment.estimate_aligned_fdr_cutoff .. autofunction:: feature_alignment.doMSTAlignment .. autofunction:: feature_alignment.doParameterEstimation .. autofunction:: feature_alignment.doReferenceAlignment .. autofunction:: feature_alignment.main :mod:`Noise imputation` Module ------------------- Analysis functions ^^^^^^^^^^^^^^^ .. autofunction:: requantAlignedValues.runSingleFileImputation .. autofunction:: requantAlignedValues.runImputeValues .. autofunction:: requantAlignedValues.analyze_multipeptides .. autofunction:: requantAlignedValues.analyze_multipeptide_cluster .. autofunction:: requantAlignedValues.integrate_chromatogram .. autofunction:: requantAlignedValues.write_out .. autofunction:: requantAlignedValues.main Data Structures ^^^^^^^^^^^^^^^ .. autoclass:: requantAlignedValues.ImputeValuesHelper :members: :undoc-members: :show-inheritance: .. autoclass:: requantAlignedValues.SwathChromatogramRun :members: :undoc-members: :show-inheritance: .. autoclass:: requantAlignedValues.SwathChromatogramCollection :members: :undoc-members: :show-inheritance: