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naivenorthΤεχνίτη Νοημοσύνη και Ρομποτική

8 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

92 εμφανίσεις

Theodore
Alexandrov
, Michael Becker,
Sören

Deininger
,
Günther

Ernst,
Liane

Wehder
, Markus
Grasmair
, Ferdinand
von
Eggeling
, Herbert Thiele, and Peter
Maass


Background on MS Imaging and goals of
paper



Methods



Results



Conclusions and Criticism


Background on MS Imaging and goals of
paper



Methods



Results



Conclusions and Criticism


In the words of All
-
Mighty

Wikipedia:


Mass spectrometry imaging is a technique
used in mass spectrometry to visualize the
spatial distribution of e.g. compounds,
biomarker, metabolites, peptides or proteins
by their molecular masses.


Or in images:


To propose a new procedure for spatial
segmentation of MALDI
-
imaging datasets.


This procedure clusters all spectra into
different groups based on their similarity.


This partition is represented by a
segmentation map, which helps to
understand the spatial structure of the
sample.

(it is MS Imaging after all)


Current multivariate algorithm (PCA) are not
meant for MS data and cannot be used to
directly interpret the data.


Current clustering algorithm do not take in
account spatial information.



Here, we assume that spectra close to each
other should be similar.


Background on MS Imaging and goals of
paper



Methods



Results



Conclusions and Criticism


Rat brain coronal section


80 µm raster


200 laser shots per position; 20185 spectra


Data acquired: 2.5 kDa
-
25
kDa


Data considered: 2.5 kDa
-
10
kDa
; 3045 points


Section of
neuroendocrine

tumor (NET)
invading the small intestine


50 µm raster


300 laser shots per position; 27360 spectra


Data acquired:1 kDa
-
30
kDa


Data considered: 3.2 kDa
-
18kDa; 5027 points



Baseline correction


TopHat

algorithm, minimal baseline width set to
10%, default in
ClinProTools


No normalization


No binning


ASCII
-
>
Matlab




Part1: conventional peak picking applied to
each 10
th

spectrum. Select 10 peaks.


Orthogonal Matching Pursuit (OMP) because it is
fast and simple


Gaussian kernel
deconvolution



Part 2: keep consensus peaks:


Only keep peaks that appear in at least 1% of the
considered spectra


Omit spurious peaks



Imaging dataset is a reduced
datacube

with 3
coordinates: x, y, m/z (reduced in m/z
dimension by peak picking)


MALDI
-
imaging data is noisy


Must be able to keep fine anatomical or
histological details


Grasmair

modification of Total Variation
minimizing
Chambolle

algorithm


Parameter
θ

between 0.5 and 1: smoothness of
resulting image


Total variation (TV) ~ sum of absolute
differences between neighboring pixels


Chambolle

algorithm searches for an
approximation of the image with small TV


Chambolle

algorithm => smoothness
adjusted globally by manually choosing a
parameter


Grasmair

locally adapts
denoising

parameter
of
Chambolle


Specify number of cluster a
-
priori


High Dimensional
Discriminant

Clustering
(HDDC)


Available in
Matlab

tool box


Each cluster is modeled by a Gaussian distribution
of its own covariance structure.


HDDC developed for high
-
dimensional data (d >
10)


Note: In
Matlab

HDDC = high
-
dimensional data
clustering


Background on MS Imaging and goals of
paper



Methods



Results



Conclusions and Criticism


used 2019 spectra out of 20185 (10%)


potential peaks: 373 peaks (red triangles)


consensus peaks: 110 peaks (green triangles)


Present in at least 20 spectra out of the 2019 (1%)


Discarded peaks
mostly in low

m/z
regions


Hypothesize they
are noise peaks
because MALDI
imaging spectra
have high baseline
in low m/z region.


OMP successfully detects major peaks


Gaussian
function
provides
reasonable
approxima
tion of
peak shape


Strong noise


Noise variance changes within m/z image and
between m/z images


Noise variance is linearly proportional to peak
intensity


Apply
Grasmair

method to selected 110
consensus peaks


Efficiently removes the noise while not
smoothing out edges


Shows
anatomical
features


Restricted
to spatial
resolution
of MALDI
-
imaging
dataset


No
denoising
: borders do not match as well


3x3 median smoothing: bad edge
preservation


5x5 median smoothing: lose many regions


Find mass values expressed in region


3 main parameters in addition to peak width


Portion of spectra considered for peak picking (each
10
th

spectrum)


Number of peaks selected for each spectrum (10
peaks)


Percentage of spectra where peak is found for
consensus peak list (1%)


Robust to changes of second and third parameter

5 10 20 peaks

0.1%




1%




5%


Increase of parameter 1 can be compensated
by higher value for parameter 2


Each 5
th

spectrum

Each 20
th

spectrum


Segmentation maps for


3 levels of
denoising

(0.6, 0.7, 0.8)


3 number of clusters (6, 8, 10)


Decrease in number of clusters merge
features


Too much
denoising

causes loss of structure
details


Background on MS Imaging and goals of
paper



Methods



Results



Conclusions and Criticism


Peak picking: usually done on mean spectrum


1% consensus better for peaks in small spatial area


Edge
-
preserving
denoising


One study with average moving window and one
study
posthoc

to improve classification


Clustering methods


HDDC better results than k
-
means but significantly
slower


Currently, mostly hierarchical clustering = memory
intensive


Importance to cancer studies


Represents a proteomic functional topographic map


Didn’t explain why they got rid of part of the
range for which the data was acquired


Dataset reduction by peak picking


done initially on per spectrum basis, it may get rid
of lower abundance peaks which still show
interesting image


Also, because the peak must be present in 1% of
the 10% selected spectra, can miss smaller regions
of interest if bad selection of 10%


Highly parameterized + slow running time
would
make it hard to run many trials