This document describes a study that uses principal component analysis (PCA) based unsupervised feature extraction to identify microRNA-mRNA interactions in Wilms tumor. PCA is applied to gene expression data to select outlier mRNAs and miRNAs without using predetermined significance thresholds. Selected mRNA-miRNA pairs are validated using existing miRNA-mRNA interaction databases and survival analysis. The approach identifies more biologically feasible mRNAs than only considering differential gene expression between normal and tumor samples.

1. microRNA­mRNA interaction identification in
Wilms tumor using principal component
analysis based unsupervised feature
extraction
Y­h. Taguchi
Department of Physics
Chuo University
Tokyo
Japan

2. 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

3. 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

4. Application:Application:
microRNA­mRNA interaction microRNA­mRNA interaction
identification in Wilms tumoridentification in Wilms tumor

5. What is microRNA (miRNA)？
DNA
mRNA
protein
miRNA

6. Difficulty of inference of miRNA­mRNA interaction
＊too many pairs
mRNA 〜 104, miRNA 〜 103 → pairs 〜 107
＊Computational prediction is sequence based

7. 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

8. 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?

9. Example previous researches
significant DEsignificant DE
cancers
Previous studies
None
No mention

10. 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

11. 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 → ∞ .

12. 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)

13. mRNA miRNA
mRNAsmiRNAs
outliers
miRTarBase
Feature
embedding
MiRNA
­mRNA
pairs
Reciprocal
pairs
vs
Expression
matrix
Controls
Patients
Sequence based
miRNA­mRNA
interaction prediction

14. 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

15. R=-0.126 (P=0.008)
R=-0.267 (P<10­16)

16. 3,4
2

17. Survival Analysis with genes targeted by multipleSurvival Analysis with genes targeted by multiple
miRNAsmiRNAs （（OncoLnc.org, BoldOncoLnc.org, Bold：：Kidney cancersKidney cancers））
3,4

18. 2

19. 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.