Page QuantProtP4_Elution

09 Aug 2023 - 17:03 | Version 16 | UnknownUser

Project Planning for QuantProtP4 - Elution Profile comparison

Status - Stefan

implement a C++ code (Main.C, see attachment) to determine the elution profile for each feature; output: intensity values + a feature number
implement a C++ code to get the consensus features
refine Main.C (version2), output: four files 1) intensity values 2) retention time values 3) unique feature ID 4) consensus features
convert output files for better handling in R (.Rdata)
3083 features in 2595 consensus groups (plot 1 & 2)
402 consensus groups consist of at least 2 features, which results in 890 features total
we want analyse the 890 features within 402 consensus groups
to compare the features we use the pearson correlation coefficient
compare scores of of features within one group with scores of features from distinct groups
Functions
- to compare two features have to be equal length/width
  - centerRT (consider index) - the shorter of two features get added with null values at both sides (plot3)
  - equalLength (consider retention time) - some time series are not consecutive, missing time point was added / both features get the same retention time (plot4)
- calculate score matrix one for each function to get features with equal length

todo include a fitting function in from OpenMS in Main.C - did not manage
smoothing elution profiles with R
save new data points (same nr of points for every feature → features get the same width)
Pearson correlation

plot_1: Number of intensity values for each feature (see attachment)
plot_2: Number of features which belong to a consensus feature (see attachment)
plot_3: left side: collected data points for features from OpenMS; right side: the feature with smaller length get centered to the greater feature, here only the time steps (index) are considered
plot_4: left side: collected data points for features from OpenMS; right side: additional data points for the feature to get them in equal length, here the retention time are considered
plot_5: (centeredEP)frequency of correlation coefficients of features which belong to a consesus group (red) and of features between consensus groups
plot_6: (equalLength) frequency of correlation coefficients of features which belong to a consesus group (red) and of features between consensus groups (green)
plot_7: for equalLength data, clustering (ward method) of features within a group and a correlation smaller the 0.2
plot_8: for equalLength data, clustering (ward method) of features within a group and a correlation greater than 0.8

plot_9: (after smoothing function) frequency of correlation coefficients of features which belong to a consesus group (red) and of features between consensus groups (green)

get raw data here: http://page.mi.fu-berlin.de/bielow/P4/elution_profile.zip
extract chromatograms and a list of features which belong together
- useful OpenMS classes
  - AreaIterator, Feature, FeatureMap, FeatureXMLFile, ConsensusMap, ConsensusXMLFile, MSExperiment, MzMLFile
- the raw data file (.mzML) contains the actual peaks, the feature file (.featureXML) describes which datapoints in the mzML file belong to one feature (see Feature→ConvexHull→BoundingBox). Corresponding features are grouped in the consensus feature file (.consensusXML).
choose metric
- Pearson correlation?
- Mann Whitney U test?
- …
using the metrices
- for any two features A and B they are either * linked (i.e. belong into the same group in the consensusXML) OR * unrelated
- compute the metric for all pairs A/B based on their elution profile
evaluate (todo)
- distribution of scores of a) elution profiles belonging together v.s. b) unrelated features' elution profiles given different metrics
- can you find two clusters of scores? i.e. one for linked feature pairs and one for unrelated feature pairs, Does one scoring function give more clear clusters compared to other scoring functions?

Programming with OpenMS (ie use OpenMS classes in your own C++ project):
http://www-bs2.informatik.uni-tuebingen.de/services/OpenMS/search/OpenMS/doc/html2/external_code.html You can probably avoid using 'my_custom_lib', as one main program should suffice.