1) The document presents two principal component analysis (PCA)-based unsupervised feature extraction methods (VBPCAFE and CPCAFE) for analyzing gene/microRNA expression data from stressed mouse hearts to understand posttraumatic stress disorder (PTSD)-mediated heart disease. 2) In synthetic tests, unsupervised methods outperformed supervised methods when there was label noise, demonstrating robustness to mislabeling. 3) When applied to real biological gene expression data, CPCAFE identified features with expected biological relationships and terms relevant to heart disease and neurodegeneration, performing better than supervised methods.

1. Principal component analysis­based
unsupervised feature extraction
applied to in silico drug discovery for
posttraumatic stress disorder (PTSD)­
mediated heart disease
Y­h. Taguchi, Department of Physics
Mitsuo Iwadate/Hideaki Umeyama
Department of Biological Science
Chuo University

2. Purpose:
Fulfill the needs to analyze datasets with ….
・Categorical multiclasses samples
・small samples (〜 10)
・Large feature (〜104
)
Solution proposed:
Principal component analysis (PCA) based
unsupervised feature extraction (FE)
Target data set:
Gene/microRNA expression of stressed mouse
heart

3. What is PCA based unsupervised FE?
N features
Categorical
multiclasses
In contrast usual usage of PCA, not samples but
features are embedded into Q dimensional space.
PCA
PC1
samples
M samples
N × M Matrix X (numerical values)
PC2
PC1
+
+ +
+ +
++
+
+
+
++ +
+
+
No distinction
between classes

4. After specifying biologically meaningful PCs,
features as outliers along the PC are extracted.
PC2
PC1
+
+ +
+ +
++
+
+
+
++ +
+
+
Synthetic example
# of samples:
M=20
= 5 samples × 4 class
# of features: N=100
Distinct feature set N0
=10 = 5 features × 2 sets
2 sets : up/downregulated set
(But class order was no used for analysis)

5. 5 features: downregulated features
hard easymedium
5 features: upregulated features
90 features: undistinct between four classes
Task: Extract 10 features correctly
S=0.5 S=1.0 S=2.0
SD within each class =0.5
Hereafter, we call this CPCAFE.

6. 1) Pair wise t test with adjusted P­values
2) Categorical regression (ANOVA) with
adjusted P­values (CRP)
3) Categorical regression (ANOVA) with
selection of top 10 FE (CRR)
Methods to be compared

7. 4) BAHSIC
Top 10 features associated with larger values are selected

8. 4) Variational Bayes PCA based FE
P( A ,B,C A ,CB ,σ)∼P(σ) P( A ,B) P( A,CA)∏q
P(B,CB
q
)
=exp
[−
(BAT
−X)2
σ2
−
1
2{Tr( AC A
−1
AT
)+∑iq
Biq
2
CB
iq
}]
Real matrix
Given : X : N × M
To be inferred:
A : M × Q , B : N × Q , CA
: Q × Q (diagonal),
Cq
B
: N × Q, Real number : σ

9. Boxplot of Aj1
S=0.5 S=1.0 S=2.0
A1j
represents N0 features distinct between
classes. Then 10 features associated with
larger Ci1
B
are extracted.
Hereafter, we call this VBPCAFE.

10. S=2.0 (easy)
log10 CB
iq
Frequency
Histogram
Features distinct
between 4 classes
Ratio of Features distinct
between 4 classes
log10 CB
iq
1.0
0.0

11. S=1.0 (medium)
log10 CB
iq
Frequency
Histogram
Features distinct
between 4 classes
Ratio of Features distinct
between 4 classes
log10 CB
iq
1.0
0.0

12. S=0.5 (hard)
log10 CB
iq
Frequency
Histogram
Features distinct
between 4 classes
Ratio of Features distinct
between 4 classes
log10 CB
iq
0.35
0.05

13. Averaged over 100
independent ensembles
S
CRR performed best. BAHSIC is the
second. Unsupervised methods
(VBPCA,CPCA) are not superior, but..

14. Unsupervised methods
have robustness towards
miss­labeling

17. Since in the real application, we cannot expect
100% accuracy of labeling. Thus, it is important to
compare the performances in real applications.
Target:
PTSD mediated heart diseases.
From medical point of views, it is unsolved, too.
Labeling is easily miss­leaded, since we do not
know what causes differences (Apparent distinct
treatments may result in the same outcome.
This leads to miss­labeling, since features distinct
between distinct treatments are often favored).

18. Target data set:
Gene/microRNA expression of stressed mouse
heart
Treated(stressed) Control
Caged with violent mouse
Conditions:
X days stressed + Y days rest
→ gene expression/microRNA expression of heart extracted
12 conditions(X vsY) × 4 samples = 46 samples

19. Since microRNAs are expected to suppress
mRNA expression, negative correlations are
favored between mRNA/microRNA expression
PC1s between
mRNA and miRNA
expression seem to
be negatively
correlated with
each other.
R=­0.37 P=0.01
CPCAFE
R=­0.69 P=0.01
PC1 mRNA
PC1 miRNA
Samples Conditions

20. Outliers
along PC1s
are extracted
(red dots).
Features
associated
more
contributions
to PC2 than
PC1 are
excluded.
miRNA
mRNA
PC1
PC2

21. Biological validations of extracted genes/miRNAs
・Almost all expected pairs of miRNA/mRNAs are
negatively correlated.
・Expected pairs are associated with expected
biological terms (heart failures as well as
neurodegenerative diseases)
More details are omitted...

22. VBPCAFE is computationally too
challenging to apply to real applications.
However, we can successfully suggest that
VBPCAFE is coincident with CPCAFE.
(Addition of 100 unextracted features to 100
extracted features by CPCAFE. Application
of VBPCAFE to generated set of features
recovers the results of CPCAFE)

23. miRNA mRNA
Rank
Frequency
100%
Top 100 ranked
features are
selected by
VBPCAFE
100 200 100 200
B
CB Red dots are
also selected
by CPCAFE
Bi1
vs CB
Red dots are
also selected
by CPCAFE

24. Biologically, CPCAFE outperforms CRRCRR as well as BAHSIC

25. We further performed in silico drug discovery
using FAMS and chooseLD developed by
Profs. Iwadate and Umeyama (Kitazato/Chuo
University), but no time to discuss about it.
See publication for more details.
Taguchi YH, Iwadate M, Umeyama H.
BMC Bioinformatics. 2015 16:139.
“Principal component analysis­based
unsupervised feature extraction applied to in
silico drug discovery for posttraumatic stress
disorder­mediated heart disease”

26. Conclusions
・We proposed two unsupervised FE, VBPCAFE
and CPCAFE.
・In synthetic examples, unsupervised FE
outperformed supervised methods only when
data sets include miss­labeling.
・In the applications to real biological data,
unsupervised methods seem to be superior to
supervised methods from the biological point of
views.
・Unsupervised methods may be useful when
applying to real problems where labeling is not
always 100 % accurate.