Emerging causal inference problems in molecular systems biology

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Emerging causal inference problems in molecular systems biology

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Emerging causal inference problems in molecular systems biology

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Emerging causal inference problems in molecular systems biology

Yi Liu, Ph.D.

Beijing Jiaotong University

The presented work was mainly collaborated with:

Prof. Jing-Dong Jackie Han, Dr. Nan Qiao, Dr. Wei Zhang

@ CAS -Max Planck partner Institute for Computational Biology

Prof. Min Liu, Dr. Jin’e Li

@ Institute of Genetics & Developmental Biology, CAS

- Background
Mining biological knowledge from the big data generated by the Next Generation Sequencing (NGS) Technology

- Examples of causal inference problems in biology
1) Inferring causal relationships between transcription factors, epigenetic modifications and gene expression level from heterogeneous deep sequencing data sets

2) Reverse-engineering the Yeast genetic regulatory network from deletion-mutant gene expression data

3) Discovering subtypes of ovarian cancer and uncovering key molecular signatures that distinguish these subtypes.

Yi Liu* and Jing-Dong J. Han*. Application of Bayesian networks on large-scale biological data. Frontiers in Biology, 2010, 5(2):98-104.

SeqSpider: A new Bayesian network inference algorithm enabling integrative analysis of deep sequencing data

Y Liu, N Qiao et al., Cell Research (2013)

Thanks for Prof. Jing-Dong Han’s contribution to the slides on this topic.

Limitation of traditional BN learning approaches

In traditional BN structure learning approaches, each node must take

a discrete value.

The only exception is the Linear-Gaussian case. However, this

Parameterization is still very restrictive.

Profiled signature of deep sequencing data

H3K4me3 profile

Deep sequencing data have

distinctive profiled signatures

along the chromosomes,

especially at the gene promoter

regions.

However, there is no way to

utilize such information in the

BN learning algorithms.

mRNA profile

Liu et al, Nucleic Acids Res, 2010

Profiles of hESC regulators around TSSs

In this work, we infer causal

relationships between

transcription factors,

epigenetic modifications

and gene expression level

In human/mouse

embryonic stem cells.

More severely, there could be heterogeneous data types in one

systems biological investigation.

Handling multiple data-types simultaneously in BN structure

learning is not a trivial task.

In this work, we use the Kernel Generalized Variance

(F. Bach, JMLR 2002) to quantify the joint dependence

between heterogeneous variables, which replace the

mutual information-like measures in BN learning.

Using Kernel Generalized Variance (F. Bach, JMLR 2002),

to quantify the joint dependence between heterogeneous

variables, we only need to define a kernel for each type of data.

Discrete Data:

Real-valued Data:

For vectored (profiled) Data, we define:

Bin-to-bin distances (such as Euclidean) are not ideal ones to

measure the discrepancy between two sequence tag profiles.

The Earth Mover’s distance (EMD) computes the minimum mass

transportation efforts to ‘deform’ one profile to another.

The L1-RPS distance is equivalent to EMD when the two profiles

have equal mass. In other cases, it also quantifies the total mass

difference between the two profiles while EMD not.

We use cluster centers of input data, instead of each gene, as the

training data to the BN learning algorithm for noise reduction.

We propose the Super k-means

algorithm to perform clustering,

which yields tighter clusters

than the k-means algorithm (in

Cluster 3.0) and the k-means++

algorithm.

Better clustering quality is

necessary for the final good

BN learning result.

Human Embryonic

Stem Cells

We relax the acyclic constraint and perform additional structure

search after BN learning to find potential feedback edges (as

learning a dependency network), since feedbacks are important and

ubiquitous in biology.

Cluster 3.0

Affinity

Propagation

Functional Dissection of Regulatory Models Using Gene Expression Data of Deletion Mutants

J Li, Y Liu et al., PLoS Genetics (2013)

In this table, each column represents a deletion mutant strain, and

each row measures the expression changes of a specific gene,

‘1’ means up-regulation, ‘-1’ means down-regulation and ‘0’ means no

specific change.

Our goal is to infer a genetic regulatory network among the

Deletion mutant genes …

However, traditional Bayesian network learning approaches

failed…

Why?

It is because the dominant value in the deletion mutant gene

expression data set is ‘0’, which quantity is magnitudes larger

than the ‘1’ and ‘-1’ values.

Using traditional BN-learning metrics, such as K2, BDeu,

BIC/MDL, the huge intra-similarities between ‘0’s will overwhelm

true regulatory signals….

To overcome this problem, we resort to kernel-based BN

learning.

To this end, we propose the DM_BN kernel.

The key insight is to block the intra-similarities between ‘0’s:

We also use a template matrix to incorporate the a priori

knowledge from deletion-mutant experiments into BN learning.

If Gene B is in the (influence) target list of Gene A, but not the

reverse case , we set (i, j) = 1, (j, i) = 0 in the template matrix to

prohibit the appearance of B->A in the BN.

In this way, the template matrix constraints the set of plausible

edges in a DAG.

Finally, to convert a DAG to a PDAG after BN learning, we must

Resort to Meek’s rules [Meek, 1995] to judge the reversibility of

Each edge, but not Chickering’s algorithm [Chickering, 1995].

Without using the template matrix, DM_BN kernel leads to

~80% accuracy in the de novo inference of edge directionalities,

which is statistically significant compared to random guessing.

Online acyclicity

checking is

implemented to

enable learning

large networks.

Integrating Genomic, Epigenomic, and Transcriptomic Features Reveals Modular Signatures Underlying Poor Prognosis in Ovarian Cancer

W Zhang, Y Liu et al., Cell Reports (2013)

Thanks for Dr. Wei Zhang’s contribution to the slides on this topic.

http://cancergenome.nih.gov/

The copy number segmentation data were mapped to the positions of genes and miRNAs.

Normalization:

Valuenorm = (Valueraw – Mediancontrols) / STDpatients

By combining the clinical and heterogeneous high-

throughput data, can we discover Ovarian cancer

subtypes whose outcomes are different?

Whether we can find active regulatory pathways

of the subtypes which could explain their different

prognosis?

To investigate which features are related to the

prognosis of ovarian cancer, we first used Cox

proportional hazard model to perform the

regression analysis between each feature and

the patients’ survival time.

In total we selected 4,526 features as hazard factors

(P < 0.05), including 1,651 genes’ expression

changes, 455 genes’ promoter DNA methylation

changes, 140 miRNAs’ expression changes, and the

CNAs of 2,191 genes and 89 miRNAs.

De novo discovery of ovarian cancer

subtypes by adaptive clustering

These signatures were identified using Wilcoxon rank-sum test.

These terms, such as cell adhesion, TGF-beta binding,

angiogenesis and positive regulation of cell proliferation,

are related to tumorigenesis and metastasis.

The 5-year survival rate of subtype 2 was even worse

than that of tumor stage IV.

The hallmarks of cancer

Hanahan & Weinberg 2011

Used to filter out signature genes that are not drivers of cancer.

- Q & A?