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microRNA­mRNA interaction identification in 
Wilms tumor using principal component 
analysis based unsupervised feature 
extraction
Y­h. Taguchi
Department of Physics
Chuo University
Tokyo
Japan
What is PCA based unsupervised FE?
 N features
Categorical 
multiclasses
In contrast to usual usage of PCA, not samples but 
features are embedded into Q dimensional space.
PCA
PC1
samplesPC Loadings
M samples
N × M Matrix X (numerical values)
PC2
PC1
PC Score
+
+ +
+ +
++
+
+
+
++ +
+
+
No distinction 
between classes
Synthetic example
10 samples
10 samples
90 features 10 features
N(0)
N()
[N()+N(0)]/2
+:Top 10 outliers

Thus, extracting outliers 
selects features distinct 
between two classes in an 
unsupervised way.
Accuracy:(100 trials)Accuracy:(100 trials)
 89.5% (
 52.6% (
PC1
PC2
Normal μ:mean 
Distribution ½ :SD
Application:Application:
microRNA­mRNA interaction microRNA­mRNA interaction 
identification in Wilms tumoridentification in Wilms tumor
What is microRNA (miRNA)?
DNA
mRNA
protein
miRNA
Difficulty of inference of miRNA­mRNA interaction
*too many pairs
 mRNA 〜 104, miRNA 〜 103 → pairs 〜 107
*Computational prediction is sequence based
How to solve this problem?
Pre­screening mRNA/miRNA based on
 differential expression (DE)
 Ex.:functional miRNA­mRNA pairs in disease
 → mRNA/miRNA with significant DE:
 Normal vs Patients
mRNA miRNA
normal
patients
matching
Negative
correlation
normal
patients
Problem:
 ””significant DE” significant DE” is arbitrary 
Screening criteria: P­value+Fold Change:FC
P­value:
Fixed number of mRNA/miRNA, N
Variable sample numbers:M
 M:large → P:small 
FC:
Typical thershold: 2  or ½, but any basis?
Example previous researches
significant DEsignificant DE
cancers
Previous studies
None
No mention
In real studies....
Control P­value and FC → good results
feasibility → no discussions
If biologically feasible, no problem?If biologically feasible, no problem?
(No discussion about P­value and FC)
→”Which ones are DE mRNA/miRNA?”
→True answer exist (but unknown)
 → Data driven strategy can help us
IdeaIdea::PCA based unsupervised FEPCA based unsupervised FE
Fixed number of mRNA/miRNA, N,
M:variable, what is convergent as M → ∞?
⇓
Distributions of PC score(genes) should 
converge as M → ∞ .
M(≪N)
sample
Gene expression 
matrix
PC loading
(Converge M    )→ ∞
normal
patients
PC1M
N
PC1
PC2
Gaussian
(assumed)
cf.Prob. PCA
PC scores
outliers*
    ||
selected
significance:
T test:P<0.05
*:multiple normal+χ2 dist
BH corrected P value<0.01
N(mRNA/miRNA)
mRNA miRNA 
mRNAsmiRNAs
outliers
miRTarBase
Feature
 embedding
MiRNA
­mRNA 
pairs
Reciprocal 
pairs
 vs 
Expression 
matrix
Controls
Patients
Sequence based 
miRNA­mRNA 
interaction prediction
Results:Results:
Samples: (Ludwig et al, IJMS, 2016)Samples: (Ludwig et al, IJMS, 2016)
mRNA miRNA
(P)atients (N)ormal P N
28 4 62 4
SelectedSelected
mRNA 1114 miRNA 55
                                Discrimination (PCA+LDA+LOOCV)Discrimination (PCA+LDA+LOOCV)
mRNA miRNA
P N P N
P 27 0 6161 0
N 1 4 1 44
R=-0.126 (P=0.008)
R=-0.267 (P<10­16)
3,4
2
Survival Analysis with genes targeted by multipleSurvival Analysis with genes targeted by multiple
miRNAsmiRNAs ((OncoLnc.org, BoldOncoLnc.org, Bold::Kidney cancersKidney cancers))
3,4
2
Conclusion:Conclusion:
Integrated  analysis  of  mRNA  expression, 
miRNA  expression  and  mRNA­miRNA 
interaction  enables  us  to  identify  more more 
biologically  feasiblebiologically  feasible  mRNAs  than 
considering only differential expressiononly differential expression 
of mRNAs.

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