The document describes a new method called MixEnrich for identifying dysregulated pathways within individual patients using single-subject transcriptome analysis. MixEnrich uses a mixture model to discover differentially expressed genes between samples, followed by gene set enrichment analysis to identify pathways enriched with dysregulated genes. Simulation studies showed MixEnrich outperformed existing methods, accurately identifying dysregulated pathways across a range of parameters. The method was also validated in a case study and can help advance precision medicine by discovering dynamic changes in individual transcriptomes.

1. Single-subject Analysis for Advancing Precision Medicine

2. Qike Li, A. Grant Schissler, Vincent Gardeux, Ikbel Achour, Colleen Kenost,
Joanne Berghout, Haiquan Li, Hao Helen Zhang, Yves A. Lussier
N-of-1-pathways MixEnrich: advancing precision
medicine via single-subject analysis in discovering
dynamic changes of transcriptomes

3. Outline
• Background
• Methods: N-of-1-pathways MixEnrich
• Results:
• Simula@on Study
• Valida@on Case Study
• LimitaAons
• Take home message
We developed a new and effecAve method to idenAfy
dysregulated pathways within a single paAent.

4. Problem statement
Population
Case Control
Transcriptome
Analysis

5. Problem statement
Population
Case Control
Average / Common
Gene signature / pathway signature
Transcriptome
Analysis
DEG+Enrichment1
GSEA2
1Beißbarth, T. and Speed, T. Bioinforma)cs 2004; 2Subramanian A. et. al PNAS 2005

6. Problem statement
Population
Case Control
Average / Common
Gene signature / pathway signature
Transcriptome
Analysis
DEG+Enrichment1
GSEA2
1Beißbarth, T. and Speed, T. Bioinforma)cs 2004; 2Subramanian A. et. al PNAS 2005
cohort-based methods

7. Problem statement
Population
Case Control
Average / Common
Gene signature / pathway signature
Control / Case
Paired Samples
Individual
Common
signature
Individual
signature
Transcriptome
Analysis Transcriptome
Analysis
DEG+Enrichment1
GSEA2
1Beißbarth, T. and Speed, T. Bioinforma)cs 2004; 2Subramanian A. et. al PNAS 2005

8. Problem statement
Population
Case Control
Average / Common
Gene signature / pathway signature
Control / Case
Paired Samples
Individual
Common
signature
Individual
signature
Transcriptome
Analysis Transcriptome
Analysis
DEG+Enrichment1
GSEA2
1Beißbarth, T. and Speed, T. Bioinforma)cs 2004; 2Subramanian A. et. al PNAS 2005
MixEnrich

9. ApplicaAons of Single-subject Analysis
• Personalized drug responses
• Personalized disease mechanisms
• Rare diseases

10. Bridging the GAP
Methods developed so far
• Focus on staAc transcriptome profile
Gene Expression
Pathway signature
(GO, KEGG, etc.)
Case
FAIME1
(Func@onal Analysis of Individual
Microarray Expression)
1Yang, X., …, Lussier, Y., PLoS Comp Bio 2012
Individual

11. Bridging the GAP
Dynamic mRNA Changes
Control Case
Methods developed so far
• Focus on staAc transcriptome profile
• Dynamic changes of transcriptomes
Individual
Pathway dysregulaAon

12. Bridging the GAP
N-of-1-pathways Wilcoxon1
N-of-1-pathways MD2
DeviaAon from Equality
Methods developed so far
• Focus on staAc transcriptome profile
• Dynamic changes of transcriptomes
1Gardeux, V., …, Lussier, Y., JAMIA 2013; 2Schissler, A., …, Lussier, Y., Bioinforma)cs 2015
Control (Log2 Expression)
Sample 2
Sample 1
Case (Log2 Expression)

13. Bridging the GAP
Methods developed so far
• Focus on staAc transcriptome profile
• Dynamic changes of transcriptomes
• Ignore the background noise
DeviaAon from Equality
1Gardeux, V., …, Lussier, Y., JAMIA 2013; 2Schissler, A., …, Lussier, Y., Bioinforma)cs 2015
Control (Log2 Expression)
Sample 2
Sample 1
Case (Log2 Expression)
N-of-1-pathways Wilcoxon1
N-of-1-pathways MD2

14. Bridging the GAP
Methods developed so far
• Focus on staAc transcriptome profile
• Dynamic changes of transcriptomes
• Ignore the background noise
• UnidirecAonal dynamic change
Control (Log2 Expression)
Sample 2
Sample 1
Case (Log2 Expression)
DeviaAon from Equality
N-of-1-pathways Wilcoxon1
N-of-1-pathways MD2

15. Bridging the GAP
Our Approach
A compeAAve model to discover
uni- and bi-direcAonal dysregulated
pathways
Baseline Case
Dynamic mRNA Changes
Individual

16. Bridging the GAP
Our Approach
A compeAAve model to discover
uni- and bi-direcAonal dysregulated
pathways
Our Method
Mixture model clustering
followed by gene-set
enrichment test
(MixEnrich)
Baseline Case
Dynamic mRNA Changes
Individual

17. Outline
• Background
• Methods: N-of-1-pathways MixEnrich
• Results:
• Simula@on Study
• Valida@on Case Study
• LimitaAons
• Take home message
We developed a new and effecAve method to idenAfy
dysregulated pathways within a single paAent.

18. Two Samples of Transcriptome
N = 1
Healthy
tissue
Tumor
tissue
Two samples of Transcriptome
(single subject)

19. DEG Discovery: Mixture Model
|log2
FC|
DEG
N = 1
Healthy
tissue
Tumor
tissue
Two samples of Transcriptome
(single subject)
DEG Discovery: Mixture Model
(single subject)
Density
Unaltered genes
Dysregulated genes
Absolute log Fold Change

20. DEG Discovery: Mixture Model
• The cluster membership of each genei is a latent variable that follows Bernoulli
distribu@on.
!! = ! !! = ! , !!
!
!!!
= 1 ! = 1, ⋯ , !; ! = 1,2
where Zi is the cluster membership of gene i
and G is the total number of genes.

21. DEG Discovery: Mixture Model
• The cluster membership of each genei is a latent variable that follows Bernoulli
distribu@on.
• Given the cluster membership, the |log2FC| of genei follows the following distribu@on,
! !! !! = ! = ! !! !!, !!
!
! = 1, ⋯ , !; ! = 1,2
where !! is |log2FC| of gene i, ! indicates density function
of a normal distribution with mean !! and variance !!
!
.
!! = ! !! = ! , !!
!
!!!
= 1 ! = 1, ⋯ , !; ! = 1,2

22. DEG Discovery: Mixture Model
• The cluster membership of each genei is a latent variable that follows Bernoulli
distribu@on.
• Given the cluster membership, the |log2FC| of genei follows the following distribu@on,
• The likelihood that a genei belongs one cluster or the other is assessed by the
posterior probability using Bayes rules.
! !! !! = ! = ! !! !!, !!
!
! = 1, ⋯ , !; ! = 1,2
! !! = !|!!, !!, !!, !!, !! =
!!!(!!, |!!, !!
!
)
!!! !! !!, !!
!)!
!!!
!! = ! !! = ! , !!
!
!!!
= 1 ! = 1, ⋯ , !; ! = 1,2

23. Gene Set Enrichment
Gene set
knowledge
(GO, KEGG)
|log2
FC|
DEG
N = 1
Healthy
tissue
Tumor
tissue
Two samples of Transcriptome
(single subject)
DEG Discovery: Mixture Model
(single subject)
Gene Set Enrichement
(single subject)
Density
Unaltered genes
Dysregulated genes
Absolute log Fold Change
Subject GO-BPID Dysregulated p-value
M.Jones GO:0000018 Yes 0.0010
M.Jones GO:0000060 Yes 0.0015
M.Jones GO:2001244 No 0.0547
Identify pathways enriched with
dysregulated genes via FET

24. Gene Set Enrichment
Contingency table for Fisher’s Exact Test
dysregulated genes unaltered genes
genes in target pathway d M – d
genes not in target pathway D - d N - M - D + d

25. SimulaAon Parameters
Parameter Description of the parameter Values tested
p.S Number of mRNAs randomly chosen in the target pathway {5, 10, [15, 490] by step 25, 500}
p.dPct Percentage of dysregulated mRNAs in the target pathway {(0, 1] by step 0.05}
p.FC Fold change of mRNAs in the target pathway {1.3, 1.5, 2}
p.upPct Percentage of up-regulated mRNAs among dysregulated
mRNAs in the target pathways
{0, 0.1, 0.2, 0.3, 0.4, 0.5}
bg.FC Fold change of dysregulated background mRNAs {0, 1.3, 1.5, 2}
bg.dPct Percentage of dysregulated mRNAs as noise in the
background
{0, 0.01, 0.05, 0.1, 0.2}
• 107,640 scenarios of pathway dysregulaAon were invesAgated

26. Outline
• Background
• Methods: N-of-1-pathways MixEnrich
• Results:
• Simula@on Study
• Valida@on Case Study
• LimitaAons
• Take home message
We developed a new and effecAve method to idenAfy
dysregulated pathways within a single paAent.

27. Exemplar ROC curves
AUC
0.986
0.976
0.938
Method
MixEnrich
MD
Wilcoxon
0.00
0.25
0.50
0.75
1.00
TruePositiveRate
0.00 0.25 0.50 0.75 1.00
False Positive Rate
bg.dPct=0 bg.FC=0
p.upPct=0 p.FC=2
Parameter:

28. Exemplar ROC curves
AUC
bg.dPct=0.2 bg.FC=2
p.upPct=0.5 p.FC=1.3
0.892
0.528
0.532
0.00
0.25
0.50
0.75
1.00
0.00 0.25 0.50 0.75 1.00
False Positive Rate
TruePositiveRate
Method
MixEnrich
MD
Wilcoxon
Parameter:
AUC
0.986
0.976
0.938
Method
MixEnrich
MD
Wilcoxon
0.00
0.25
0.50
0.75
1.00
TruePositiveRate
0.00 0.25 0.50 0.75 1.00
False Positive Rate
bg.dPct=0 bg.FC=0
p.upPct=0 p.FC=2
Parameter:

29. Overall Performance Comparison
0.4
0.6
0.8
1.0
MixEnrich MD Wilcoxon
AreaUndertheROCCurve
(AUC)

30. Background Fold-Change
MixEnrich MD
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0.00
0.25
0.50
0.75
1.00
0 0.010.05 0.1 0.2 0 0.010.05 0.1 0.2
Percentage of background noise (bg.dPct)
AUC

31. Up-regulaAon Percentage
MixEnrich MD
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0.4
0.6
0.8
1.0
0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5
Up−regulation Percentage (p.upPct)
AUC

32. Pathway DysregulaAon Percentage
MixEnrich MD
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0.00
0.25
0.50
0.75
1.00
0.2 0.4 0.6 0.8 1 0.2 0.4 0.6 0.8 1
Percentage of dysregulated genes
in the target pathway (p.dPct)
AUC