Getting Started¶
To use the package, type the following into your Python console:
>>> import dimepy
At the moment, this pipeline only supports mzML files. You can easily convert proprietary formats to mzML using ProteoWizard.
Loading a single file¶
If you are only going to load in a single file for fingerprint matrix
estimation, then just create a new spectrum object. If the sample
belongs to a characteristic, it is recommend that you also pass it
through when instantiating a new Spectrum
object.
>>> filepath = "/file/to/file.mzML"
>>> spec = dimepy.Spectrum(filepath, identifier="example", stratification="class_one")
/file/to/file.mzML
By default the Spectrum object doesn’t set a snr estimator. It is strongly recommended that you set a signal to noise estimation method when instantiating the Spectrum object.
If your experimental protocol makes use of mixed-polarity scanning, then please ensure that you limit the scan ranges to best match what polarity you’re interested in analysing:
>>> spec.limit_polarity("negative")
If you are using FIE-MS it is strongly recommended that you use just the infusion profile to generate your mass spectrum. For example, if your scan profiles look like this:
| _
T | / \
I | / \_
C |_____/ \_________________
0 0.5 1 1.5 2 [min]
Then it is fair to assume that the infusion occured during the scans
ranging from 30 seconds to 1 minute. The limit_infusion()
method
does this by estimating the median absolute deviation (MAD) of total ion
count (TIC) before limiting the profile to the range between the time
range in which whatever multiple of MAD has been estimated:
>>> spec.limit_infusion(2) # 2 times the MAD.
Now, we are free to load in the scans to generate a base mass_spectrum:
>>> spec.load_scans()
You should now be able to access the generated mass spectrum using the
masses
and intensities
attributes:
>>> spec.masses
array([ ... ])
>>> spec.intensities
array([ ... ])
Working with multiple files¶
A more realistic pipeline would be to use multiple mass-spectrum files.
This is where things really start to get interesting. The
SpectrumList
object facilitates this through the use of the
append
method:
>>> speclist = dimepy.SpectrumList()
>>> speclist.append(spec)
You can make use of an iterator to recursively generate Spectrum
objects, or do it manually if you want.
If you’re only using this pipeline to extract mass spectrum for
Metabolanalyst, then you can now simply call the _to_csv
method:
>>> speclist.to_csv("/path/to/output.csv", output_type="metaboanalyst")
That being said, this pipeline contains many of the preprocessing methods found in Metaboanalyst - so it may be easier for you to just use ours.
As a diagnostic measure, the TIC can provide an estimation of factos
that may adversely affect the overal intensity count of a run. As a
rule, it is common to remove spectrum in which the TIC deviates 2/3
times from the median-absolute deviation. We can do this by calling the
detect_outliers
method:
>>> speclist.detect_outliers(thresh = 2, verbose=True)
Detected Outliers: outlier_one;outlier_two
A common first step in the analysis of mass-spectrometry data is to bin
the data to a given mass-to-ion value. To do this for all Spectrum
held within our SpectrumList
object, simply apply the bin
method:
>>> speclist.bin(0.25) # binning our data to a bin width of 0.25 m/z
In FIE-MS null values should concern no more than 3% of the total number
of identified bins. However, imputation is required to streamline the
analysis process (as most multivariate techniques are unable to
accomodate missing data points). To perform value imputation, just use
value_imputate
:
>>> speclist.value_imputate()
Now transforming and normalisating the the spectrum objects in an samples independent fashion can be done using the following:
>>> speclist.transform()
>>> speclist.normalise()
Once completed, you are now free to export the data to a data matrix:
>>> speclist.to_csv("/path/to/proc_metabo.csv", output_type="matrix")
This should give you something akin to:
Sample ID | M0 | M1 | M2 | M3 | … |
---|---|---|---|---|---|
Sample 1 | 213 | 634 | 3213 | 546 | … |
Sample 2 | 132 | 34 | 713 | 6546 | … |
Sample 3 | 1337 | 42 | 69 | 420 | … |