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
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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
Mining biological knowledge from the big data generated by the Next Generation Sequencing (NGS) Technology
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
Deep sequencing data have
distinctive profiled signatures
along the chromosomes,
especially at the gene promoter
However, there is no way to
utilize such information in the
BN learning algorithms.
Liu et al, Nucleic Acids Res, 2010
Profiles of hESC regulators around TSSs
In this work, we infer causal
and gene expression level
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.
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++
Better clustering quality is
necessary for the final good
BN learning result.
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.
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
Our goal is to infer a genetic regulatory network among the
Deletion mutant genes …
However, traditional Bayesian network learning approaches
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
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.
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.
The copy number segmentation data were mapped to the positions of genes and miRNAs.
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
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.