mapping the sub-cellular proteome · 2015-12-04 · spatial proteomics - why? mis-localisation...
TRANSCRIPT
Mapping the sub-cellular proteome
Computational analyses of high-throughput massspectrometry-based spatial proteomics data
Laurent [email protected] – @lgatt0
Computational Proteomics Unithttp://cpu.sysbiol.cam.ac.uk/
http://lgatto.github.io/
(Slides @ http://goo.gl/SZRMjg)
14 Oct 2015, CCBI
Plan
Introduction
Spatial proteomics
Data analysis
Transfer learning
Dynamics
Regulations
Cell organisation
Spatial proteomics is the systematic study of protein localisations.
Image from Wikipedia http://en.wikipedia.org/wiki/Cell_(biology).
Spatial proteomics - Why?
Mis-localisationDisruption of the targeting/trafficking process alters propersub-cellular localisation, which in turn perturb the cellularfunctions of the proteins.
I Abnormal protein localisation leading to the loss of functionaleffects in diseases (Laurila and Vihinen, 2009).
I Disruption of the nuclear/cytoplasmic transport (nuclearpores) have been detected in many types of carcinoma cells(Kau et al., 2004).
Re-localisation in
I Differentiation: Tfe3 in mouse ESC (Betschinger et al., 2013).
I Metabolism: changes in carbon sources, elemental limitations.
Plan
Introduction
Spatial proteomics
Data analysis
Transfer learning
Dynamics
Spatial proteomics - How, experimentally
Single celldirect
observation
Population level
Subcellular fractionation (number of fractions)
Tagging Quantitative mass spectrometryCataloguing Relative abundance
1 fraction2 fractions(enriched
and crude)n discrete fractions
n continuous fractions(gradient approaches)
Subtractiveproteomics
(enrichment)
Invariantrich
fraction(clustering)
(χ )2PCP LOPIT
(PCA, PLS-DA)
Pure fraction
catalogue
GFPEpitope
Prot.-spec.antibody
Figure : Organelle proteomics approaches (Gatto et al., 2010)
Fusion proteins and immunofluorescence
Fusion proteins and immunofluorescence
Figure : Example of discrepancies between IF and FPs as well as betweenFP tagging at the N and C termini (Stadler et al., 2013).
Spatial proteomics - How, experimentally
Single celldirect
observation
Population level
Subcellular fractionation (number of fractions)
Tagging Quantitative mass spectrometryCataloguing Relative abundance
1 fraction2 fractions(enriched
and crude)n discrete fractions
n continuous fractions(gradient approaches)
Subtractiveproteomics
(enrichment)
Invariantrich
fraction(clustering)
(χ )2PCP LOPIT
(PCA, PLS-DA)
Pure fraction
catalogue
GFPEpitope
Prot.-spec.antibody
Figure : Organelle proteomics approaches (Gatto et al., 2010). Gradientapproaches: Dunkley et al. (2006), Foster et al. (2006).
⇒ Explorative/discovery approches, global localisation maps.
Fractionation/centrifugation
Quantitation/identificationby mass spectrometry
e.g. Mitochondrion
Cell lysis
e.g. Mitochondrion
Plan
Introduction
Spatial proteomics
Data analysis
Transfer learning
Dynamics
Quantitation data and organelle markers
Fraction1 Fraction2 . . . Fractionm markers
p1 q1,1 q1,2 . . . q1, m unknownp2 q2,1 q2,2 . . . q2, m loc1
p3 q3,1 q3,2 . . . q3, m unknownp4 q4,1 q4,2 . . . q4, m loci...
......
......
...pj qj,1 qj,2 . . . qj, m unknown
Annotated data sets
I Several mouse E14TG2a Embryonic Stem cells.
I Human Embryonic Kidney fibroblast cells.
I The Arabidopsis AT CHLORO data base (Ferroet al., 2010).
I Mouse organs (Foster et al., 2006).
I Arabidopsis from callus (Dunkley et al., 2006;Nikolovksi et al. 2014) and roots (Groen et al.,2014).
I Drosophila embryos (Tan et al., 2009).
I Chicken DT40 Lymphocyte cell (Hall et al.,2009).
I . . .
Available in the pRolocdata experiment package.
0
500
1000
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Num
ber
of P
MID
s
Spatial/organelle(s) proteomics papers
Visualisation and classification
0.2
0.3
0.4
0.5
Correlation profile − ER
Fractions
1 2 4 5 7 81112
0.1
0.2
0.3
0.4
Correlation profile − Golgi
Fractions
1 2 4 5 7 81112
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Correlation profile − mit/plastid
Fractions
1 2 4 5 7 81112
0.15
0.20
0.25
0.30
0.35
Correlation profile − PM
Fractions
1 2 4 5 7 81112
0.1
0.2
0.3
0.4
0.5
0.6
Correlation profile − Vacuole
Fractions
1 2 4 5 7 81112
●●
●●
●
●
●
●
●
●●
●●
●
●
●
●●●● ●
●●
●●
●●
−10 −5 0 5
−5
05
Principal component analysis
PC1
PC
2
●
ERGolgimit/plastidPM
vacuolemarkerPLS−DAunknown
Figure : From Gatto et al. (2010), Arabidopsis thaliana data fromDunkley et al. (2006)
Data analysis
Fraction1 Fraction2 . . . Fractionm
prot1 q1,1 q1,2 . . . q1, mprot2 q2,1 q2,2 . . . q2, mprot3 q3,1 q3,2 . . . q3, mprot4 q4,1 q4,2 . . . q4, m...
......
......
proti qi,1 qi,2 . . . qi, m...
......
......
protn qn,1 qn,2 . . . qn, m
markers. . . unknown . . .
organelle1unknownorganelle2
......
...organellek
......
.... . . unknown
Fraction1 Fraction2 . . . Fractionm
prot1 . . . . . . . . . . . .
proti...
......
...protn . . . . . . . . . . . .
−6 −4 −2 0 2 4 6
−4
−2
02
4
Principal Component Analysis Plot
PC1 (64.36%)
PC
2 (2
2.34
%)
●
●
●
●
●
●
●
●
●
●● ●●
●
●
●
●
●●
●●
●
●●
●
●
●
●●
●●●
●
●
●
●
●
●●
●
●
●
●
●
● ●●●●●
●
●● ● ●
●●
●●
●
●
●
●
●
●●●●
●
●●
●●
●
●
● ●
● ●
●●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●
●●
●
●
●●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●●
●●
●
●
●
●
●
●
●
●●
●●
●● ●
●●
●
●●●
●●
●
●
●
●●
●●
●
●●
●
●●
●●
●●
●●
●●
●
●●
●●
●●
●
● ●
●
●
●
●●●
●
●●
●●
●
●
●
●●
●
●
●
●
●
●
●●
●
●●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●●
●●●●
●●●
●
●
●
●●
●
●
●
●●
●
●
●●
●
●
●
●
●
●●●
●
●●●●
●● ●●
●●
●
●●
●● ●
●
●●●
●●
●
● ●●●●
●
●
●●
●●
●
●●
●
●●
●
●
●
●●
●●
●●
●
●●
●
●●●
●
●● ●●●
●
●●
●
●
●
●
●● ●●●
●●●
●●
●●
●
●
●
●
●●●
●●●●●
●●
●
●●
●●
●
●
●
●
● ●● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
● ●●●
●
● ●
●
●
●
●●
●
●
●
●
●
●●
●
Supervised machine learning
Using labelled marker proteins to match unlabelled proteins (ofunknown localisation) with similar profiles and classify them asresidents to the markers organelle class.
Current approaches - supervised ML
svm
sigma
cost
0.0625
0.125
0.25
0.5
1
2
4
8
16
0.01 0.1 1 10 100 1000
0.5
0.6
0.7
0.8
0.9
1.0
−6 −4 −2 0 2 4 6−
4−
20
24
Optimised parameters
PC1 (64.36%)
PC
2 (2
2.34
%)
−6 −4 −2 0 2 4 6
−4
−2
02
4
Wrong parameters
PC1 (64.36%)
PC
2 (2
2.34
%)
Figure : Support vector machines classifier with a radial basis functionkernel function, using the pRoloc Bioconductor package1 (Gatto et al.,2014).
1www.bioconductor.org/packages/release/bioc/html/pRoloc.html
F1
0.5
0.6
0.7
0.8
0.9
1.0
knn nb nnet plsda rf svm
● ● ●●
● ●
●
●
● ●
Tan.PD
knn nb nnet plsda rf svm
● ● ●
●
●
●●●●●
●
●●
●
●●●●●●●●●●●●●
●
●
●
●●
●
●
●●●
●
Tan
knn nb nnet plsda rf svm
● ● ●●
● ●●●●
●●●●
●
●
● ●
●
●●●●
●
●
●
●
●
●●
●
●
●
●●●●●●●●●●●
●
●
●●
●
●
●●●●●
●
●●●●
●
●●●
Dunkley.PD
● ● ● ● ● ●●●●
●●●●●●●
●●●●●●●●●●●●●●
●
●●
●●●●
●●●●●●●●
Dunkley
●● ● ● ●
●
●
●
●
●
●
●
●
Andy.PD
0.5
0.6
0.7
0.8
0.9
1.0
●
● ●
● ●●
●●
●● ●
●
●
Andy
0.5
0.6
0.7
0.8
0.9
1.0
●
●
●
●
●●
●
●
●●
AT_CHLORO
● ● ● ●
● ●
●●●●●
●●●
●
●
Nikolovski
● ●●
●
● ●
●
●
●
●
●●●●●● ●●
Nikolovski.Imp
Figure : Comparing classifiers
Limitations
−3 −2 −1 0 1 2 3
−3
−2
−1
01
23
PC1 (58.53%)
PC
2 (2
9.96
%)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●● ●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●● ●
●
●● ●
●
●
●●●
●
●
●
●
ER/GolgimitochondrionPMunknown
Incomplete annotation, and therefore lack of training data, formany/most organelles. Drosophila data from Tan et al. (2009).
Novelty detection
−3 −2 −1 0 1 2 3
−3
−2
−1
01
23
PC1 (58.53%)
PC
2 (2
9.96
%)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●● ●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●● ●
●
●● ●
●
●
●●●
●
●
●
●
ER/GolgimitochondrionPMunknown
−3 −2 −1 0 1 2 3
−3
−2
−1
01
23
PC1 (58.53%)
PC
2 (2
9.96
%)
CytoskeletonERGolgiLysosomemitochondrionNucleus
PeroxisomePMProteasomeRibosome 40SRibosome 60S
Figure : Left: Drosophila data from Tan et al. (2009). Right:Semi-supervised learning, Breckels et al. (2013).
−3 −2 −1 0 1 2 3
−3
−2
−1
01
23
PC1 (58.53%)
PC
2 (2
9.96
%)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●● ●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●● ●
●
●● ●
●
●
●●●
●
●
●
●
ER/GolgimitochondrionPMunknown
−3 −2 −1 0 1 2 3
−3
−2
−1
01
23
PC1 (58.53%)
PC
2 (2
9.96
%)
CytoskeletonERGolgiLysosomemitochondrionNucleus
PeroxisomePMProteasomeRibosome 40SRibosome 60S
Input data:D = (DL, DU )
Phenotype modeling:Select Di
L and modelF = Di
L ∪ DU using aGMM (cluster numberestimate using BIC).
Get candidates: Mem-bers of DU clustered
with DiL are considered
candidats of class i.
Each candidate is testedagainst an outlier
detection algorithm.
Candidates classifiedas members of i are
merged with DiL. Those
which are rejectedare returned to DU
Update classes: ex-amples in DU that areconsistently accepted
into a single class i arelabelled as members of Di
L.
New phenotype: Anyexample of DU not merged
with any DiL and which
are consistenlty clusteredtogether throughoutthe N iterations areconsidered membersof a new phenotype.
Output: Returnunassigned examples,
new DiL members
and new phenotypes.
next class i
all classes considered
Repeat N times
Plan
Introduction
Spatial proteomics
Data analysis
Transfer learning
Dynamics
What about annotation data from repositories such as GO,sequence features, signal peptide, transmembrane domains,images, . . .
I From a user perspective: ”free/cheap” vs. expensive
I Abundant (all proteins, 100s of features) vs. (experimentally)limited/targeted (1000s of proteins, 6 – 20 of features)
I For localisation in system at hand: low vs. high quality
I Static vs. dynamic
number GO features � experimental fractions⇒ dilution of experimental data
What about annotation data from repositories such as GO,sequence features, signal peptide, transmembrane domains,images, . . .
I From a user perspective: ”free/cheap” vs. expensive
I Abundant (all proteins, 100s of features) vs. (experimentally)limited/targeted (1000s of proteins, 6 – 20 of features)
I For localisation in system at hand: low vs. high quality
I Static vs. dynamic
number GO features � experimental fractions⇒ dilution of experimental data
GoalSupport/complement the primary target domain (experimentaldata) with auxiliary data (annotation) features withoutcompromising the integrity of our primary data.
Updated experimental design for
I primary/experimental data
and
I auxiliary/annotation data
Learning from heterogeneous data sources: an application in spatial
proteomics. Breckels LM, Holden S, Wonjar D, Mulvey CM, Christoforou
A, Groen AJ, Kohlbacher O, Lilley KS and Gatto L.
bioRχiv pre-print http://dx.doi.org/10.1101/022152.
Fractionation/centrifugation
Quantitation/identificationby mass spectrometry
Database query
Extract GO CC terms
Convert terms to binary
PR
IMA
RY EX
PER
IMEN
TAL
DATA
AU
XIL
IARY D
RY D
ATA
O00767P51648Q2TAA5Q9UKV5......
GO:0016021 GO:0005789 GO:0005783 ... ... ...
1 1 1 ... ... ...1 1 0 ... ... ...1 1 0 ... ... ...0 0 0 ... ... .... . .. . .. . .. . .. . .. . .
x1
.
.
.
.
.
.
.
.xn
GO1 ... ... ... ... GOA
O00767P51648Q2TAA5Q9UKV5......
0.1361 0.150 0.1062 0.147 0.277 0.1429 0.0380 0.003380.1914 0.205 0.0566 0.165 0.237 0.0996 0.0180 0.027270.1297 0.201 0.0546 0.146 0.292 0.1463 0.0206 0.009020.0939 0.207 0.0419 0.204 0.344 0.1098 0.0000 0.00000. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .
x1
.
.
.
.
.
.
.
.xn
X113 X114 X115 X116 X117 X118 X119 X121
Visualisation Visualisation
e.g. Mitochondrion
Cell lysis
e.g. Mitochondrion
−2 0 2 4
−2
−1
01
23
4
PC1 (40.28%)
PC
2 (2
5.7%
)
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●●
●
●●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
40S Ribosome60S RibosomeCytosolEndoplasmic reticulumLysosomeMitochondrionNucleus − ChromatinNucleus − NucleolusPlasma membraneProteasomeunknown
Data from mouse stem cells (E14TG2a)
We use a class-weighted kNNtransfer learning algorithm tocombine primary and auxiliarydata, based on Wu andDietterich (2004):
V (ci )j = θ∗nPij + (1− θ∗)nA
ij
Classes and weightsC = {ci=1, . . . , ci=l}; Θ = {0, 0.5, 1}
Primary data
LP =
q1,1 q1,2 . . . q1,mq2,1 q2,2 . . . q2,m
.
.
.
.
.
.qj,1 qj,2 . . . qj,m
;
y1y2
.
.
.yj
; kP
Auxiliary data
LA =
b1,1 b1,2 . . . . . . b1,nb2,1 b2,2 . . . . . . b2,n
.
.
.
.
.
.bj,1 bj,2 . . . . . . bj,n
;
y1y2
.
.
.yj
; kA
Neighbour matrices
NP =
ci=1 . . . ci=l
nP1,1 . . . nP
1,l
nP2,1 . . . nP
2,l
.
.
.
.
.
.
; NA =
ci=1 . . . ci=l
nA1,1 . . . nA
1,l
nA2,1 . . . nA
2,l
.
.
.
.
.
.
Classes and weightsC = {ci=1, . . . , ci=l}; Θ = {0, 0.5, 1}
Primary data
LP =
q1,1 q1,2 . . . q1,mq2,1 q2,2 . . . q2,m
.
.
.
.
.
.qj,1 qj,2 . . . qj,m
;
y1y2
.
.
.yj
; kP
Auxiliary data
LA =
b1,1 b1,2 . . . . . . b1,nb2,1 b2,2 . . . . . . b2,n
.
.
.
.
.
.bj,1 bj,2 . . . . . . bj,n
;
y1y2
.
.
.yj
; kA
Neighbour matrices
NP =
ci=1 . . . ci=l
nP1,1 . . . nP
1,l
nP2,1 . . . nP
2,l
.
.
.
.
.
.
; NA =
ci=1 . . . ci=l
nA1,1 . . . nA
1,l
nA2,1 . . . nA
2,l
.
.
.
.
.
.
1
2
●
●
●
c1c2c3
NP =
c1 c2 c3
p133 0 0
p213
23 0
......
...
Classes and weightsC = {ci=1, . . . , ci=l}; Θ = {0, 0.5, 1}
Primary data
LP =
q1,1 q1,2 . . . q1,mq2,1 q2,2 . . . q2,m
.
.
.
.
.
.qj,1 qj,2 . . . qj,m
;
y1y2
.
.
.yj
; kP
Auxiliary data
LA =
b1,1 b1,2 . . . . . . b1,nb2,1 b2,2 . . . . . . b2,n
.
.
.
.
.
.bj,1 bj,2 . . . . . . bj,n
;
y1y2
.
.
.yj
; kA
Neighbour matrices
NP =
ci=1 . . . ci=l
nP1,1 . . . nP
1,l
nP2,1 . . . nP
2,l
.
.
.
.
.
.
; NA =
ci=1 . . . ci=l
nA1,1 . . . nA
1,l
nA2,1 . . . nA
2,l
.
.
.
.
.
.
Weights matrix (labelled)
c1 c2 c3
θ1 0 0 0θ2 0 0 1
θi...
...... 1 1 0θΘl 1 1 1
F11
F12
F1i...
F1Θl
θ∗ = {1, 0, 1}
Classes and weightsC = {ci=1, . . . , ci=l}; Θ = {0, 0.5, 1}
Primary data
LP =
q1,1 q1,2 . . . q1,mq2,1 q2,2 . . . q2,m
.
.
.
.
.
.qj,1 qj,2 . . . qj,m
;
y1y2
.
.
.yj
; kP
Auxiliary data
LA =
b1,1 b1,2 . . . . . . b1,nb2,1 b2,2 . . . . . . b2,n
.
.
.
.
.
.bj,1 bj,2 . . . . . . bj,n
;
y1y2
.
.
.yj
; kA
Neighbour matrices
NP =
ci=1 . . . ci=l
nP1,1 . . . nP
1,l
nP2,1 . . . nP
2,l
.
.
.
.
.
.
; NA =
ci=1 . . . ci=l
nA1,1 . . . nA
1,l
nA2,1 . . . nA
2,l
.
.
.
.
.
.
Class-weighted classifier(unlabelled)
V (ci )j = θ∗nPij + (1− θ∗)nA
ij
ci=1 . . . ci=l
123 V (ci )j...j
yj = argmax(V (ci )j )
θ∗ = {1, 0, 1}
NP =
c1 c2 c3
p133 0 0
p213
23 0
......
...
V (c1)1 =1 ×3
3+ (1 − 1) × nA
1,1
V (c2)1 =0 × 0 + (1 − 0) × nA1,2
V (c3)1 =1 × 0 + (1 − 1) × nA1,3
V (c1)2 =1 ×1
3+ (1 − 1) × nA
1,1
V (c2)2 =0 ×2
3+ (1 − 0) × nA
1,2
V (c3)2 =1 × 0 + (1 − 1) × nA1,3
Class-weighted classifier(unlabelled)
V (ci )j = θ∗nPij + (1− θ∗)nA
ijc1 c2 c3
1 V (c1)1 V (c2)1 V (c3)1
2 V (c1)2 V (c2)2 V (c3)2...
...j
yj = argmax(V (ci )j )
D E
A B C
●
● ●●
●
● ●● ●●●●●
●
● ●●●●●●●
●
●●
●
●●
●
●●
●●●
●
●●
40S Ribosome 60S Ribosome Cytosol Endoplasmic reticulum
Lysosome Mitochondrion Nucleus − Chromatin Nucleus − Nucleolus
Plasma membrane Proteasome
0.4
0.6
0.8
1.0
0.6
0.7
0.8
0.9
1.0
0.00
0.25
0.50
0.75
1.00
0.7
0.8
0.9
1.0
0.00
0.25
0.50
0.75
1.00
0.75
0.80
0.85
0.90
0.95
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
Combined Primary Auxiliary Combined Primary Auxiliary Combined Primary Auxiliary Combined Primary Auxiliary
Combined Primary Auxiliary Combined Primary Auxiliary Combined Primary Auxiliary Combined Primary Auxiliary
Combined Primary Auxiliary Combined Primary Auxiliary
F1 s
core
−6 −4 −2 0
−6−4
−20
2
PC1 (3.43%)
PC2
(2.0
8%)
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●●●●●
●
●
●
●
●
●
●
●
●
●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●●
●
●
●
●●●●●
●
●
●
●●
●
●
●●
●
●●●
●
●
●
●●●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●●●●●
●
●
●●●
●
●
●
●
●●
●●
●●
●
●
●
●●
●
●●●
●
●
●●
●
●●
●●●
●
●
●
●●●
●
●
●
●●●●
●
●
●●●
●
●
● ●
●
●
●
●
●●
●●●
●
●●
●●●●●●●●●●●●●●
●
●
●
●
●
●
●●●
●
●
●●
●
●●●●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
● ●● ●
●
●
●
●●●
●●
●
●
●
●
●●
●
●
●
●●
●●
●
●
●
●
●
●●
●●
●
●●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●
●●
●●
●
●
●
●
●
●●●
●●
●
●●●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●●
●●
●
●
●
●
●
●●●●
●
●●●●●
●
●
●
●●
●
●
●●●●
●
●
●
●
●●●●●
●
●●●●
●●
●●●
●
●
●●
●
●●●●
●
●●
●
●
●
●
● ●
●●●
●
●●
●
●●●●
●●
●●
●
●
●
●
●●
●
●●●
●●●●●
●
●●●●
● ●
●●●●●
●
●●●
●●
●
●●
●●●
●
●
●●●●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●●●
●●
●
●
●
●
●
●●
●●●●●●
●
●
●
●
●●●
●●●●●
●
●
●●●●
●
●●●
●●●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●●
●●
●
●
●●
●
●
●●●
●
●●●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●●● ●●●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●●
●
●
●●
●
●
●
●●●
●
●
●
●●●
●
●
●
●
●● ●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
●
●
●
40S Ribosome60S RibosomeCytosolEndoplasmic reticulumLysosomeMitochondrionNucleus − ChromatinNucleus − NucleolusPlasma membraneProteasomeunknown
−2 0 2 4
−2−1
01
23
4
PC1 (40.28%)
PC2
(25.
7%)
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●●
●
●
●
●
●
●
●
●●
●
●●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
● ●
●
●●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●●
●●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
40S Ribosome60S RibosomeCytosolEndoplasmic reticulumLysosomeMitochondrionNucleus − ChromatinNucleus − NucleolusPlasma membraneProteasomeunknown ●
●
0.5
0.6
0.7
0.8
0.9
Combined Primary Auxiliary
F1 s
core
Proteasome
Plasma membrane
Nucleus − Nucleolus
Nucleus − Chromatin
Mitochondrion
Lysosome
Endoplasmic reticulum
Cytosol
60S Ribosome
40S Ribosome
0 1/3 2/3 1Classifier weight
Cla
ss
Data from mouse stem cells (E14TG2a).
From SML to transfer learning: learn from heterogeneous datasources (experimental spatial proteomics and GO annotation,sequence features, imaging data) to infer localisation more reliably(Breckels et al. 2015).
0.25
0.50
0.75
1.00
knn knn−TL svm svm−TL
Sco
res outcome
correct
incorrect
Plan
Introduction
Spatial proteomics
Data analysis
Transfer learning
Dynamics
Dual-localisation Proteins may be present simultaneously inseveral organelles (e.g. trafficking).
−6 −4 −2 0 2 4 6
−4
−2
02
4
PC1 (64.36%)
PC
2 (2
2.34
%) ●
●● ●
●
●●
●
●
●
●
● ●
●
●
●
●●●
●
●
●
●
●
●● ●
●
●
●
●
●
● ●●●●●
●
●
●● ●
●
●
●●
●●
●
●
●
●
●
●●●●
●
●●
●●
●
●
● ●●
● ●
●●
●●
●
●
●
●
●●
●
●
●●
●
●
●
●
●●
●●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●●
●●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●●
●●
●
●
●
●
●
●
●
●●
●●
●
●●
●
●●
●●
●●
●●
●●
●
●●
●●
●●
●
● ●
●
●
●
●●●
●
●●
●●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●●
●
●
●●
● ●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●●●
●●●●
●●
●
●
●●
●
●●●●
●● ●●
●
●● ●
●
●●●
● ●●●●
●
●
●●
●●
●
●●
●
●●
●
●
●
●●
●●
●●
●
●●
●
●●●
●
●● ●●●
●
●●
●
●
●
●● ●●●
●●●
●●
●●
●
●
●
●
●●●
●●●●●
●●
●
●●
●●
●
●
●
●
● ●● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
● ●●●
●
● ●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
● ●● ●●
●
●
●
●●
●
●
●●
●
●
●● ●
●
●
●
●
●●●
●●
● ●
●
●
●
●
●
●●
●
●
●
●
●●●
●
●
●
●●
●
●
●
●
●
●●●
●
●
●
●
●●
●
●●
● ●
●● ●●
●
●
●
●●●
●●●
●●●●
●
●
●
●
●
●
●●
●
●●
●● ●●●
●
●
●●●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●●
●
●
●●●
●
●
●
●
●●●
●
●
●●
●
●
●
●
●
●
●●●
●
●
● ●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
ER lumenER membraneGolgiMitochondrionPlastidPMRibosomeTGNvacuoleunknown
●● ● ●●
●● ● ● ●
●●
●
●
●●
●● ● ● ●
From Betschinger et al. (2013)
−6 −4 −2 0 2 4
−4
−2
02
4
Mouse ESC (E14TG2a) in serum LIF
PC1 (50.05%)
PC
2 (2
4.61
%)
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●● ●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
● ●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●●●
●
●
●●
●●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●●●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Actin cytoskeletonCytosolEndosomeER/GAExtracellular matrixLysosomeMitochondriaNucleus − ChromatinNucleus − NucleolusPeroxisomePlasma MembraneProteasomeRibosome 40SRibosome 60Sunknown
●Tfe3
Dual-localisation Proteins may be present simultaneously inseveral organelles (e.g. trafficking).
−6 −4 −2 0 2 4 6
−4
−2
02
4
PC1 (64.36%)
PC
2 (2
2.34
%) ●
●● ●
●
●●
●
●
●
●
● ●
●
●
●
●●●
●
●
●
●
●
●● ●
●
●
●
●
●
● ●●●●●
●
●
●● ●
●
●
●●
●●
●
●
●
●
●
●●●●
●
●●
●●
●
●
● ●●
● ●
●●
●●
●
●
●
●
●●
●
●
●●
●
●
●
●
●●
●●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●●
●●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●●
●●
●
●
●
●
●
●
●
●●
●●
●
●●
●
●●
●●
●●
●●
●●
●
●●
●●
●●
●
● ●
●
●
●
●●●
●
●●
●●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●●
●
●
●●
● ●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●●●
●●●●
●●
●
●
●●
●
●●●●
●● ●●
●
●● ●
●
●●●
● ●●●●
●
●
●●
●●
●
●●
●
●●
●
●
●
●●
●●
●●
●
●●
●
●●●
●
●● ●●●
●
●●
●
●
●
●● ●●●
●●●
●●
●●
●
●
●
●
●●●
●●●●●
●●
●
●●
●●
●
●
●
●
● ●● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
● ●●●
●
● ●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
● ●● ●●
●
●
●
●●
●
●
●●
●
●
●● ●
●
●
●
●
●●●
●●
● ●
●
●
●
●
●
●●
●
●
●
●
●●●
●
●
●
●●
●
●
●
●
●
●●●
●
●
●
●
●●
●
●●
● ●
●● ●●
●
●
●
●●●
●●●
●●●●
●
●
●
●
●
●
●●
●
●●
●● ●●●
●
●
●●●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●●
●
●
●●●
●
●
●
●
●●●
●
●
●●
●
●
●
●
●
●
●●●
●
●
● ●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
ER lumenER membraneGolgiMitochondrionPlastidPMRibosomeTGNvacuoleunknown
●● ● ●●
●● ● ● ●
●●
●
●
●●
●● ● ● ●
From Betschinger et al. (2013)
−6 −4 −2 0 2 4
−4
−2
02
4
Mouse ESC (E14TG2a) in serum LIF
PC1 (50.05%)
PC
2 (2
4.61
%)
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●● ●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
● ●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●●●
●
●
●●
●●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●●●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
Actin cytoskeletonCytosolEndosomeER/GAExtracellular matrixLysosomeMitochondriaNucleus − ChromatinNucleus − NucleolusPeroxisomePlasma MembraneProteasomeRibosome 40SRibosome 60Sunknown
●Tfe3
Spatial dynamics
Trans-localisation Changes in localisation upon perturbations.
−4 −2 0 2
−4
−3
−2
−1
01
23
PC1 (43.43%)
PC
2 (3
9.04
%)
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●●●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
● ●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
● ●
● ●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
cytoplasmERGolgiMitochondrialNucleiPlasma membraneProteasome & RibosomeVacuoleunknown
Condition 1
−4 −2 0 2 4
−3
−2
−1
01
23
PC1 (39.04%)
PC
2 (3
0.9%
)
●
●●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●●
●
●
●●
●●
●
●
●●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●●
●●
●
●●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
● ●
● ●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●●
●
●
●
● ●●●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●●●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
Condition 2
Spatial dynamics
d1 = dist(profilerep1condition1
, profilerep1condition2
)
d2 = dist(profilerep2condition1
, profilerep2condition2
)
●
●
●
●● ●
●●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
● ●● ●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●
●
●
●
●●●●
●
● ●
●
●●
●●●
●●
●
●●●
●
●●
● ●
●
●
●
●
●
●
●
●●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
● ●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●●
●
●●
● ●●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●
●
●
●
●
●
● ●
●●
●
● ●
●
● ●
●
●
●
●
●●
●
●●
●●
●●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
● ●●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
● ●
●
●
●
●
●●
●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●●
●
●●
●
●
●
●
●● ●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
● ●
●●
●
●
●
●
●
●
●●
●
●●●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●
●●●
●●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●●
●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●● ●● ●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●●
●
●
●
●●
● ● ●
●
●
●
●
●
●
●
●●
●
●●
●● ●
●
●
●●
●
●
●●
●
●
●
● ●
●
●
●●
●
●●● ●
●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●●
●●●
● ●
●
●
●●
●
●
●
●
●
●●
●
● ●●
●
●
●
●
●●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●●
●
● ●● ●
●
●
●
●
●
●
●●
●
●
●
●
● ●
●
●●
●
●
●
●
●●
●●
●
●
●●
●●
●
●
●
●
● ●
●
●
●
●●
●
●●
●
●
● ●●
●
●●
●
●
●
●●
●●
●
●
●
●
●
●●
●● ●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●●
●●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
● ●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
● ●
●
●
●●●
●
●●
●
●
●●
●●
●
●
●●
●
●
● ●●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ● ● ●●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
● ●●
●
●
●
●
●● ●
●
●●
●
●
●
●
●
●●
●●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
● ●
●
●
●
●
●● ●
● ●● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●●
●
●
0.0 0.5 1.0 1.5
−3
−2
−1
01
23
(d1 + d2)/2
log2
(d1/
d2)
−4 −2 0 2
−4
−3
−2
−1
01
23
PC1 (43.43%)
PC
2 (3
9.04
%)
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●●●
●
●
●
● ●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
● ●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
Condition 1
●
cytoplasmERGolgiMitochondrialNucleiPlasma membraneProteasome & RibosomeVacuoleunknown
●●●
●●
12
3
4
5
−4 −2 0 2 4
−3
−2
−1
01
23
PC1 (39.04%)
PC
2 (3
0.9%
)●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●●
●
●
●●
●●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●●
●
●●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
● ●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
● ●
● ●●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●●
●
●
●
● ●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●
●
●
●
●
● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●●
Condition 2
●●●●●12345
Beyond organelles: application to PPI/Protein complexes
−10 −5 0 5 10
−5
05
10
markers
PC1 (47.02%)
PC
2 (2
2.25
%) ●
●
●
● ●
●
●
●
●
●
●●
● ●
●●
●●
●
●●
●
●
●
●
●●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
●
● ●●●●
●
●●●
●●
●
●
●
●● ●
● ●● ●
●
●●●●
●
●
●
●
●
●
●●●
●
●
●●●
●●●●
●●●
●
●
●
●
●
●
●
●●●
●
●
●●●
●
●●
●
● ●
●
●
●
●●●●
●
●
●
●
●
●
●
●●●
●
●
●
●●
●
●●
● ●●
●●
●
●●
●
●
●
●
●● ●●●
●●●
●
●
●
●●●
●
●
●
●
●
●
● ●●
●
●●
●●
●
●
●
●●
●
●●
●●
●
●
● ●
●
●●●
●●
●●●●●
●●
●●●●●
●●
●
●●
●
● ●
●●
●
● ●●
●
●
●
●
●
●
●
●
●
●
●
● ●
● ●
●
●
●
●
●●
● ●
●
●●●
●●●●
●
●
●
●
●
●
●
●
●
● ●●●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
● ●●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●●
●
●
●●
●
●
●
●●●●●
●●
●●
●●
●
●
●
●●
●● ●
●●
●
●
●● ●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●● ●
● ● ●●●
●● ●
●
●●
● ●
●
●
●
●
●
●
●
●
●●
●●
●
●
●
●
●
● ●
●●
●
●
●
● ●
●●
●●
●●
●
●● ●
●●
●●
●
●●
●
●●
●●
●
●●
●
●●
●●
●
●
●●
●●
●●●
●● ●
●●●
●
●
●●
●
●
●●
●●
●
●●
●
● ●●●●
●
●
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
●●
●
●
●
●
●●●
●
●
●
●●
●
●
●
●
●
●
●●
●
● ●●●
●●
● ●●
●
●●
●●●●●●
●
●
●
●
●●
●
●
●●
●
●●
●●
●●
●
●
●●
●
●
●
●●
●
●
●●
●●
●
●
●
●
●
●●
●●
●
●
●●●
● ●● ●
●●
●
●
●
●●
●● ●●
●
●●●
●
●
●
●●
●
●
●●
●
●
●
●
●●
●
●
●●
●●
●
●
●
●●
●
●
●
●●●
●
●
●
● ●
●
●
●
●
●
●
● ●
●
●
●
●
●
●
●●●
●
●●
●●
●
●
●
●
●
●
●●
●
●
●●
●●
●●
●
●
●● ● ●
●
●●
●●
●●●●
●●●●
●
●●
●
●
●●
●
●●
●●
●
●
●
●● ●●
●●
●
●
●●●
●
●●
●●●●●
●
●
●
●
●
●
●●
●
●
●
●
●
●
●●
●●
●●●
●
●●●●
●
●
●
●
●●● ●
●
●
●
●●
●
●
●
●●●
●
●
●
●
●●●
●
●
●
●
●
●
●
●
●●●
●
●
●
●
●● ●
●
●●
●
●
●
●
●●
●●●
●●●
●●●
● ●
●
●●●●
● ●●
●●●●●●●●
●
●
●●
●
●●
●
●
●
●
●
●●
●
●
●●
●●●●
●
●
●
●
●●●●
●
●●● ●●
●●●●
●
●
●
●
●
●
●●
●●●
●
●●●
●
●● ●
●●●
●
●
●
●
●
●
●●
●
●
●●
●
●●
●●
●●
●●●●
●●
●
●
●●●
●
●
●
●●
●●●●●
●
●●●
●
●
●●
●●●
●
●●
●●●●●●
●●●
●
●●
●
●
●
●
●
●●
●●
●●●
●● ●
●●
●●●●
●
●● ●●●●●
● ●●●●●
●●
●
●●
●●●
●
●
●
●
●
●
●●●●●●●●●●●●●●●
●●●●●
●●●●
●
●●
●
●
●●
●
●
●
●
●
●
●
●
● ●●
● ●
●●
●
●●
●●●
●●
●
●
●
●
●
●●
●●
● ●
●
●
●●
●●●●
●
●●
●●
●●
●
●
●●
●●
●
●
●
●
●
●
●●
●
●
●
●●
●
●
●●●
●
●
●
●
●
●●●●
●
●
●● ●●
●
●●
●
● ●
●
●
●
●●●●●
●●
●
●
●●
●
●
●
●
●●●●●●●●●●●●●●●●●●●●●●●●● ●●● ●●●●● ●●● ●● ●● ●●● ●●●●●●●●●●●●●●●●●●●
●
●●●●●● ●●
●●●●●●●●●●
●
●●●
●
●●●
●●
●
●●●●
●
●●
●●●●●●●●
●●●
●●●
●●
●●
●
●●
●●●
●●●●●●
●●
●●●
●
●●●●●●●
●●●
●
●●●
●
●
●
●●
●
●●
●
●
●●
●●●
●●
●●●●●●
●●●●
●
●●
●●●
●●●
●●●●●●●
●
●
●
●●
●
●
●
●
●
●
●
●
●
●
●
●
●
●●
●
●●
●●
●
●
●●●●
●
●●●
●
●●●
●●
●●
●
●
●
●
●●
●●●
●
● ●
●
●
●
●
●
●
●●
●
●●
●
●●
●●
●
●
●●
●
●
●
●●
●●
●
●
●●●
●
●
●
●
● ●
●
●● ●●●
●
●
●●● ●
●
●
●
●●
●●
●
●
●
●
●●● ●
●●●●●
●●●●
●
●
●
●●
●●●
●
●●
●
●
●
●
●●●
● ●
●
●●
●
●
●
●
● ●●●●
●
●
●●
●
●●●
●●
●
●
●
● ●
●
●●
●
●
●
●●●
●●●
●●
●
●
●
●
●
●
●
●
●
●
●
● ●●●
●●
● ●
●●
●
●●
●
●
●●
●
●
●
●
●
●
●●
●
●●
●
●●
●
●●
●
●
●
●
●●●●●●●●●
●●
●●
●●●
●●●
●
●●
●●
●●●
●●
●●●
● ●
●●
●
●
●
●
●
●
●
●
●●●
●●
●
●●
●
●●
●
●
● ●●
●
●
●
●
●●
●●
●
●
●
●●●●
●
● ●●● ●
●●
●
●●
●
●●●
●
●●●
●●
●●
●
●
● ●
●
●
●
●
●
●
●
●●
●
●
●●
● ●●
●
●
●
●
●
●● ●
●●●
●
●●
●
●
●●●
●●●
●●●
●
●
●● ●●
●
●
●●
●●
●●
●●●
●●
●
●●●●●●●●●●●●●●●●●●
●
●●●
●
●●
●
●
● ●
●●
●
●
●●
●●
●●
●●
●
●●
●
●
●
●
●
●●●
●●
● ●
●
●
●
●
●
●
●
●
●●
●●●
●
●●
●●●
●
●●
●
●
●
●
●●●●
●
●● ●
●
●
●●●
●
●●●●
●● ●●●
●
●●●
●
●
●●●●
●
●●●●
●●
●
●
●
●
●●
●●
●
●
●
●
●●
●
●
●
●●
●●●●●
●
●
●●
●
●
●
●●
●
●
●
●
●
●●
●
●
●
●
● ●●● ●
●●
● ●●
●●
●●
●
●●
●
●●
●
●●●
●
●
●
●●●●
●
●
●●●
●
●
●
●
●●
●
● ●
●●
●●
●
●
●
●
●
●
●
●
●
●
●
● ●
●
●
●
●
●
● ●● ●
●●
●
●
●●●
●
●●
●
●●●
●●●●
●●●●●●
●●●
●
●● ●
●
● ●●●●
●
●●●
●●
●
●
● ●
●●
●●
●
●●
●
●
●●
●●●
●
●
●
●
●
●●
●●
●●
●
●
●
●●
●
●
●
●●
●
●
● ●
●
●
●
●● ●
●
●
●
14−319S20S40S60SCCTeIF3Ku70/Ku80PA28Rabunknown
Figure : Data on proteasome complexes from Fabre et al. Mol Syst Biol(2015), DOI: 10.15252/msb.20145497
Software for mass spectrometry and (spatial) proteomics
Bioconductor Open source, enable reproducible research,enables understanding of the data (not a black box) and drivescientific innovation.
I MSnbase – infrastructure to handle quantitative data and meta-data(Gatto and Lilley, 2012) (̃ 350 unique IP download/month).
I pRoloc and pRolocGUI – dedicated visualisation and MLinfrastructure for spatial proteomics (Gatto et al., 2014) (̃ 160unique IP download/month in 2014).
I pRolocdata – structured and annotated spatial proteomics data(Gatto et al., 2014).
I And more generally RforProteomics (Gatto and Christoforou,2014) (̃ 100 unique IP download/month in 2014).
J Betschinger, J Nichols, S Dietmann, P D Corrin, P J Paddison, and A Smith. Exit from pluripotency is gated byintracellular redistribution of the bhlh transcription factor tfe3. Cell, 153(2):335–47, Apr 2013. doi:10.1016/j.cell.2013.03.012.
LM Breckels, L Gatto, A Christoforou, AJ Groen, KS Lilley, and MW Trotter. The effect of organelle discoveryupon sub-cellular protein localisation. J Proteomics, 88:129–40, Aug 2013.
TPJ Dunkley, S Hester, IP Shadforth, J Runions, T Weimar, SL Hanton, JL Griffin, C Bessant, F Brandizzi,C Hawes, RB Watson, P Dupree, and KS Lilley. Mapping the Arabidopsis organelle proteome. PNAS, 103(17):6518–6523, Apr 2006.
LJ Foster, CL de Hoog, Y Zhang, Y Zhang, X Xie, VK Mootha, and M Mann. A mammalian organelle map byprotein correlation profiling. Cell, 125(1):187–199, Apr 2006.
L Gatto and A Christoforou. Using R and Bioconductor for proteomics data analysis. Biochim Biophys Acta, 1844(1 Pt A):42–51, Jan 2014.
L Gatto and KS Lilley. MSnbase - an R/Bioconductor package for isobaric tagged mass spectrometry datavisualization, processing and quantitation. Bioinformatics, 28(2):288–9, Jan 2012.
L Gatto, JA Vizcaino, H Hermjakob, W Huber, and KS Lilley. Organelle proteomics experimental designs andanalysis. Proteomics, 2010.
L Gatto, L M Breckels, S Wieczorek, T Burger, and K S Lilley. Mass-spectrometry based spatial proteomics dataanalysis using pRoloc and pRolocdata. Bioinformatics, Jan 2014.
TR Kau, JC Way, and PA Silver. Nuclear transport and cancer: from mechanism to intervention. Nat Rev Cancer,4(2):106–17, Feb 2004.
K Laurila and M Vihinen. Prediction of disease-related mutations affecting protein localization. BMC Genomics,10:122, 2009.
DJL Tan, H Dvinge, A Christoforou, P Bertone, A Arias Martinez, and KS Lilley. Mapping organelle proteins andprotein complexes in Drosophila melanogaster. J Proteome Res, 8(6):2667–2678, Jun 2009.
P Wu and TG Dietterich. Improving svm accuracy by training on auxiliary data sources. In Proceedings of theTwenty-first International Conference on Machine Learning, ICML ’04, New York, NY, USA, 2004. ACM.
Acknowledgements
I Lisa Breckels, Computational Proteomics Unit, Cambridge(ML, algo)
I Sean Holden, Computer Laboratory, Cambridge (ML)
I Kathryn Lilley, Cambridge Centre of Proteomics(Proteomics)
Funding: BBSRC, PRIME-XS EU FP7, Software SustainabilityInstitute (SSI)
Slides available at http://goo.gl/SZRMjg, under a CC-BY license .
Thank you for your attention