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\title[Do I look like a microbe?] % (optional, use only with long paper titles)
{A systems approach to dissecting the tissue-specific architecture of cellular networks}


\author[]
%Mohammadi et al.] % (optional, use only with lots of authors)
{S. Mohammadi, B. Saberidokht, S. Subramaniam, \underline{A. Grama}}

\institute[Joint] % (optional, but mostly needed)
{
  Department of Computer Science, Purdue University\\
  Department of Bioengineering, University of California, San Diego
}

\date[BDW 2013] % (optional, should be abbreviation of conference name)
%{National Academy of Science, 2014}


\subject{Computational Biology}


\pgfdeclareimage[height=0.5cm]{university-logo}{figures/purdue}
\logo{\pgfuseimage{university-logo}}


\AtBeginSubsection[]
{
  \begin{frame}<beamer>{Outline}
    \tableofcontents[currentsection,currentsubsection]
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}

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\begin{document}

\begin{frame}
  \titlepage
\end{frame}

\begin{frame}{Outline}
  \tableofcontents
\end{frame}





\section{Motivation}
\begin{frame}{Yeast as a pioneering model organism}{Simple yet powerful}
  \begin{columns}[c]
    \column{.35\textwidth}
    \begin{center}
		\includegraphics[width=\columnwidth]{figures/yeast_yellow.jpg}\\
    \end{center}
	  
    \column{.65\textwidth}
"... yeast has graduated from a position as the premier model for eukaryotic cell biology to become the pioneer organism that facilitated the establishment of entirely new fields of study called \textit{functional genomics} and \textit{systems biology}." -- D. Botstein and G. Fink (2011). 
  \end{columns}  
\end{frame}



\begin{frame}{Yeast as a pioneering model organism}{Why yeast?}
    \begin{itemize}
	    \item Rapid growth and ease of manipulation
	    \item Mature genetic and molecular toolbox, including deletion mutants, over-expression libraries, and green fluorescent protein (GFP)-tagged yeast strains	    
		\item Multitude of high-throughput datasets, ranging from genetic arrays, transcriptome, proteome, and metabolome profiles
    	\item Saccharomyces Genome Database (SGD)
		\flushright\includegraphics[width=.3\columnwidth]{figures/SGD3.jpg}\\
    \end{itemize}
\end{frame}



\begin{frame}{Conserved pathways between yeast and higher-order organisms}
\small
    Many of the underlying functionalities and associated machineries are shared
with higher eukaryotes:    
    \begin{itemize}
    	\item Cell cycle
    	\item Programmed cell death
    	\item Protein folding, quality control, and degradation
		\item Signaling pathways, such as MAPK, TOR, and insulin/IGF-I
		\item Aging and CR-mediated pathways
		\begin{itemize}
		\item \textbf{Chronological:} amount of time cells
survive in post-mitotic state
		\item \textbf{Replicative:} number of times a cell can divide before
senescence occurs.				
		\end{itemize}
    \end{itemize}
\end{frame}


\begin{frame}{Conserved pathways}{Continued}
    \begin{center}
		\includegraphics[height=0.75\textheight]{figures/CR.jpeg}\\	      
		\tiny{Fontana \textit{et al}, Science (2010)}
    \end{center}
\end{frame}






\begin{frame}{Yeast as a model organism for human disease}{recent success stories}
  \begin{columns}[c]
    \column{.3\textwidth}
    \begin{center}
		\includegraphics[width=\columnwidth]{figures/NAB.jpg}\\	      
		\tiny{Adopted from D. Tardiff \textit{et al.} (2013)}
    \end{center}
		
    \column{.7\textwidth}
	    \begin{itemize}
	    	\item Heterologous expression of disease gene(s)
	    	\item Yeast as an unbiased phenotypic screen
	    	\item N-aryl benzimidazole (NAB) strongly protects cells from $\alpha$-synuclein toxicity in the humanized yeast model
	    	\item Validated this discovery using iPS cell from Parkinson's patients with $\alpha$-Syn mutation
	    \end{itemize}
    \end{columns}
\end{frame}



\begin{frame}{Problem statement}

  \begin{columns}[c]
    \column{.3\textwidth}
		\alert{For which tissues is yeast a good model organism?}

    \column{.4\textwidth}
    \begin{center}
		\includegraphics[width=\columnwidth]{figures/tissue_types.jpg}\\	      
    \end{center}
		\tiny{Different human tissues, while inheriting a similar genetic code, exhibit unique anatomical and physiological properties.}
	

    \column{.3\textwidth}
		\alert{What are the shared/ missing functional components in yeast, compared to human tissues?}

	\end{columns}
\end{frame}




%-----------------------------------------------------------------------------------
%-----------------------------------------------------------------------------------
%						Materials and Methods
%-----------------------------------------------------------------------------------
%-----------------------------------------------------------------------------------
\section{Materials and methods}
\subsection{Datasets}
\begin{frame}{Tissue-specific gene expression}
The GNF Gene Atlas dataset:
  \begin{columns}[c]
    \column{.4\textwidth}
    \begin{center}
		\includegraphics[width=\columnwidth]{figures/BioGPS}\\
    \end{center}
	  
    \column{.6\textwidth}
		\begin{itemize}
			\item 79 different tissues
			\item 44,775 human transcripts
			\item Platforms:
			\begin{enumerate}
				\item Affymetrix HG-U133A.
				\item Custom GNF1H array.
			\end{enumerate}
		\end{itemize}
	\end{columns}		
\end{frame}


\begin{frame}{Tissue-specific interactomes}
	\begin{itemize}
		\item Vertex-induced subgraphs of the global human interactome
		\item Based on the GNF Gene Atlas dataset
		\begin{itemize}
			\item[$\Rightarrow$] A gene is considered as present in a tissue, if its normalized expression level is $> 200$ (average difference between match-mismatch pairs).
		\end{itemize}
	\end{itemize}
	
    \begin{center}
		\includegraphics[width=.8\textwidth]{figures/bossi}\\
		\tiny{Adopted from Bossi et al., 2009}
    \end{center}
\end{frame}


\begin{frame}{Sequence similarity of protein pairs}
\begin{itemize}
\item Protein sequences are downloaded from Ensembl database, release 69.
\item Reference genomes:
\begin{itemize}[label=$\triangleright$]
\item \textbf{Human}: GRCh37
\item \textbf{Yeast}: EF4
\end{itemize}
\item Number of protein sequences:
\begin{itemize}[label=$\triangleright$]
\item \textbf{Human}: 101,075
\item \textbf{Yeast}: 6,692
\end{itemize}
\item Low-complexity regions are masked using \textbf{pseg}
\item Smith-Waterman algorithm is used to compute local sequence alignments.
\end{itemize}
\end{frame}




\subsection{Algorithmic contributions}
\begin{frame}{Sparse network alignment}
\begin{center}
\small
\alert{Integer Quadratic Program}-- Approximated using Belief Propagation:

\begin{eqnarray*}
\label{eq:IQP}
\max_{\x} & & ( \alpha \w^T\x + \frac{\beta}{2}\mathbf{\x^T \Square \x} )\\
\hbox{Subject to:} & & \left\{
                       \begin{array}{ll}
                        \mathcal{C}\x \leq \mathbf{1}_{n_G*n_H}& \hbox{Matching constraints;} \\
                         x_{ii'} \in \{0, 1\}, & \hbox{Integer constraint.}
                       \end{array}
                     \right. \nonumber
\end{eqnarray*}
\end{center}
\tiny
\begin{itemize}
\item $\x$: Matching vector
\item $L$: Bipartite graph of similarities between pair of proteins in input networks
\item $\w$: Edge-weights in the graph $L$ (based on sequence similarities)
\item $\Square$: Matrix encoding conserved edges in the product graph ($G \otimes H$)
\item $\mathcal{C}$: Incidence matrix of graph $L$
\end{itemize}
\end{frame}





\begin{frame}{Random model for tissue-specific networks}
\begin{definition}
\footnotesize
\begin{itemize}
\item \alert{Global human interactome:} All potential interactions between human proteins, represented by graph $G = (V_G, E_G)$
\item \alert{Tissue-specific network(s)}: Vertex-induced subgraph(s) of the Global human interactome,  represented by $G_T = (V_T, E_T)$ with $n_T= |V_T|$, $V_T \subset V_G$, and $E_T \subset E_G$
\item \alert{Universal genes}: Ubiquitously expressed subset of human genes corresponding to houskeeping functions, represented by $V_U \subset V_G$, and $n_U = |V_U|$
\item \alert{Random tissue-specific network(s)}: Vertex-induced subgraphs of $G$, constructed from $V_\mathcal{R} = V_U \cup V_\mathcal{S}$, with $V_\mathcal{S}$ being random set of vertices of size $n_T - n_U$ selected from $V_G \setminus V_U$
\end{itemize}
\end{definition}
\end{frame}




\begin{frame}{Significance of network alignment(s)}
\tiny
\begin{definition}
\begin{itemize}
\item \alert{Original alignment}: $\Weight = \w^T\x$, $\Overlap = \frac{1}{2}\x^T \Square \x$
\item \alert{Monte-Carlo simulation}: Let $\Weight_{\R}$ and $\Overlap_{\R}$ be the random vectors representing the weight and overlap of aligning $k_{\R}$ random tissue-specific networks with yeast
\item \alert{Positive/Negative cases}: $k_P$ is the number of random cases with both $\Weight_{\R} \leq \Weight$ and . $\Overlap_{\R} \leq \Overlap$. $k_N$ is defined as the size of complement set.
\item \alert{p-value} bounds:
\begin{equation*}
\delta_{\R} = \frac{k_P}{k_{\R}} \leq \mbox{alignment p-value} \leq 1-\frac{k_N}{k_{\R}} = \Delta_{\R}
\end{equation*}
\item \alert{Alignment p-value}:
\begin{equation*}
p-value = Prob(\alpha*\Overlap + \beta*\Weight \leq \Overlap\Weight_{\R})
\end{equation*}
\end{itemize}
\end{definition}
\end{frame}






\begin{frame}{Partitioning human genes based on their expression selectivity}

\begin{definition}
\footnotesize
\alert{Selectivity \emph{p}-value}-- Given a cluster of homogenous tissues:
\begin{eqnarray*}
\text{\emph{p}-value}(X = c_n) &=& Prob(c_n \leq X)\nonumber \\
&=& HGT(c_n| N, n, c_N) \nonumber  \\
&=& \sum_{x=c_n}^{min(c_N, n)} \frac{{C(c_N, x)} {C(N-c_N,n-x)}} {{C(N,n)}}
\label{eq:ts_pval}
\end{eqnarray*}
\alert{$N$}: total number of tissues, \alert{$n$}: number of tissues in the cluster, \alert{$c_N$}: number of tissues in which a given gene is expressed, \alert{$c_n$}: number of tissue in the cluster that the given gene is expressed.
\end{definition}
\end{frame}






\begin{frame}{Human-specific or conserved?}
\tiny
\begin{definition}
Classification of human tissue-selective genes:
\begin{itemize}
\item \alert{Conserved}: Subset of tissue-selective genes that are consistently
aligned in the "majority" of aligned tissues in the given group
\item \alert{Human-specific}: Subset of tissue-selective genes that are
consistently unaligned in the "majority" of tissues in the given group
\item \alert{Unclassified}: None of the above
\end{itemize}
\end{definition}

\begin{definition}
Majority voting:

\begin{itemize}
\item \alert{Alignment consistency table}: Yeast partner of each tissue-selective gene in the given cluster of tissues
\item \alert{Consensus rate}: Minimum percentage of tissues (columns) in each row of the alignment consistency table that have to agree to make a decision about conserved/human-specificity
\end{itemize}
\end{definition}
\end{frame}



\begin{frame}{Summary}
\begin{center}
\includegraphics[width=.8\textwidth]{figures/flow}
\end{center}
\end{frame}




%-----------------------------------------------------------------------------------
%-----------------------------------------------------------------------------------
%						Results and discussion
%-----------------------------------------------------------------------------------
%-----------------------------------------------------------------------------------
\section{Results and discussion}
\subsection{Core alignment graph of housekeeping genes}
\begin{frame}{Core genes-- The most conserved subset of housekeeping genes}
	\begin{center}
		\includegraphics[width=\textwidth]{figures/Core_AlignGraph}
	\end{center}
\end{frame}



\begin{frame}{Functional enrichment of HK genes}{Core subset}
\begin{itemize}
\item Ribosome biogenesis
\item Translation
\item Protein targeting
\item RNA splicing
\item mRNA surveillance
\end{itemize}
\end{frame}


\begin{frame}{Functional enrichment of HK genes}{Human-specific subset}
\begin{itemize}
\item Anatomical structure development
\item Paracrine signaling
\item NADH dehydrogenase (mitochondrial Complex I)
\end{itemize}
\end{frame}


\subsection{Computing similarity of human tissues with yeast}
\begin{frame}{The most similar tissues to yeast}
\tiny
\begin{table}[!h]
\centering
\begin{tabular}{l|cccc}
Name	&pval lower bound	&overall pval	&pval upper bound	&confidence	\\\hline
Myeloid Cells	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
Monocytes	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
Dentritic Cells	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
NK Cells	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
T-Helper Cells	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
Cytotoxic T-Cells	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
B-Cells	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
Endothelial	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
Hematopoietic Stem Cells	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
MOLT-4	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
B Lymphoblasts	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
HL-60	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
K-562	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
Early Erythroid	&$<$ 1.00e-04	&$<$ 1.00e-04	&$<$ 1.00e-04	&1	\\
Bronchial Epithelial Cells	&$<$ 1.00e-04	&$<$ 1.00e-04	&0.0002	&0.9998	\\
Colorectal Adenocarcinoma	&$<$ 1.00e-04	&$<$ 1.00e-04	&0.0004	&0.9996	\\
Daudi	&$<$ 1.00e-04	&$<$ 1.00e-04	&0.0009	&0.9991	\\
Testis Seminiferous Tubule	&$<$ 1.00e-04	&$<$ 1.00e-04	&0.0012	&0.9988	\\
Smooth Muscle	&$<$ 1.00e-04	&$<$ 1.00e-04	&0.0016	&0.9984	\\
Blood (Whole)	&$<$ 1.00e-04	&$<$ 1.00e-04	&0.0053	&0.9947	\\
Thymus	&$<$ 1.00e-04	&0.0001	&0.0062	&0.9938	\\
Testis Interstitial	&$<$ 1.00e-04	&0.0004	&0.0086	&0.9914
\end{tabular}
\end{table}
\end{frame}


\begin{frame}{The least similar tissues to yeast}
\tiny
\begin{table}[!h]
\centering
\begin{tabular}{l|cccc}
Name	&pval lower bound	&overall pval	&pval upper bound	&confidence	\\\hline
Trigeminal Ganglion	&0.9947	&0.9994	&1	&0.9947	\\
Superior Cervical Ganglion	&0.9847	&0.9991	&1	&0.9847	\\
Ciliary Ganglion	&0.9407	&0.9813	&0.9964	&0.9443	\\
Atrioventricular Node	&0.8746	&0.9792	&0.9921	&0.8825	\\
Skin	&0.8355	&0.9297	&0.9809	&0.8546	\\
Heart	&0.7934	&0.9585	&0.9815	&0.8119	\\
Appendix	&0.7596	&0.9371	&0.973	&0.7866	\\
Dorsal Root Ganglion	&0.7065	&0.933	&0.9717	&0.7348	\\
Skeletal Muscle	&0.3994	&0.5902	&0.7866	&0.6128	\\
Uterus Corpus	&0.233	&0.7736	&0.8769	&0.3561	\\
Lung	&0.0771	&0.3853	&0.5544	&0.5227	\\
Pons	&0.0674	&0.5201	&0.6983	&0.3691	\\
Salivary Gland	&0.0639	&0.3449	&0.5173	&0.5466	\\
Liver	&0.0600	&0.6857	&0.8519	&0.2081	\\
Ovary	&0.0388	&0.2735	&0.4481	&0.5907	\\
Trachea	&0.0259	&0.2376	&0.4146	&0.6113	\\
Globus Pallidus	&0.0206	&0.2471	&0.4336	&0.587	\\
Cerebellum	&0.0127	&0.1950	&0.3783	&0.6344
\end{tabular}
\end{table}

\end{frame}



\subsection{Consistency of alignment p-values}
\begin{frame}{Tissue-tissue similarity network}
	\begin{center}
		\includegraphics[width=.7\textwidth]{figures/TTSN_groups}
	\end{center}
\end{frame}



\subsection{Partitioning tissue-selective genes}
\begin{frame}{Tissue-selectivity density function}
	\begin{center}
		\includegraphics[width=.7\textwidth]{figures/TS_pdfs}\\
		\tiny
		(\textbf{A}) Brain tissues, (\textbf{B}) Blood cells, (\textbf{C}) Ganglion tissues, (\textbf{D}) Testis tissues
	\end{center}
\end{frame}



\subsection{Conservation and functional role of tissue selective genes}
\begin{frame}{Blood cells}
\begin{figure}[!ht]
\centering
\subfigure[Conserved] {%
	\includegraphics[width=.45\textwidth]{figures/UniqueEM/BloodCG_unique_annotated.pdf}
	\label{figure:blood_unique_CG}
}%
\subfigure[Human-specific] {%
	\includegraphics[width=.45\textwidth]{figures/UniqueEM/BloodHS_unique_annotated.pdf}
	\label{figure:blood_unique_HS}
}%
\caption{{\bf Enrichment map of unique blood-selective functions.} }
\label{figure:blood_unique}
\end{figure}
\end{frame}


\begin{frame}{Brain tissues}
\begin{figure}[!ht]
\centering
\subfigure[Conserved] {%
	\includegraphics[width=.45\textwidth]{figures/UniqueEM/BrainCG_unique_annotated.pdf}
	\label{figure:brain_unique_CG}
}%
\subfigure[Human-specific] {%
	\includegraphics[width=.45\textwidth]{figures/UniqueEM/BrainHS_unique_annotated.pdf}
	\label{figure:brain_unique_HS}
}%
\caption{{\bf Enrichment map of unique brain-selective functions.} }
\label{figure:brain_unique}
\end{figure}
\end{frame}



\subsection{Tissue-selective genes predict tissue-specific pathologies}

\begin{frame}{Enriched disease classes}
\tiny
\begin{table}[!h]
\centering
\begin{tabular}{l|lc|lc} 
&Conserved genes & &Human-specific genes&\\
&Disease class& \emph{p}-value&Disease class& \emph{p}-value\\\hline
Blood cells& Cancer& $2.85*10^{-3}$& Immune& $1.88*10^{-9}$\\
&&&Infection& $1.00*10^{-2}$\\\hline
Brain tissues&Psych& $3.59*10^{-4}$&Psych& $5.70*10^{-8}$\\
&Chemdependency& $2.60*10^{-3}$&Neurological& $2.97*10^{-2}$\\
&Pharmacogenomic& $9.74*10^{-2}$&&
\end{tabular}
\label{table:disease_summary}
\end{table}
\end{frame}


\begin{frame}{Comparative analysis of brain-specific pathologies}
\tiny
\begin{table}[!h]
\centering
\begin{tabular}{p{0.6\linewidth}|cc}
Disorder& Conserved  genes&Human-specific genes\\\hline
schizophrenia	&0.008573	&8.4905E-06\\
autism	&0.048288	&0.00077448\\
dementia	&0.0014356	&-\\
schizophrenia; schizoaffective disorder; bipolar disorder	&-	&0.0021433\\
myocardial infarct; cholesterol, HDL; triglycerides; atherosclerosis, coronary; macular degeneration; colorectal cancer	&0.0051617	&-\\
epilepsy	&0.071562	&0.0064716\\
seizures	&-	&0.020381\\
bipolar disorder	&0.048288	&0.022016\\
attention deficit disorder conduct disorder oppositional defiant disorder	&0.032444	&0.023865
\end{tabular}
\label{table:disease_brain}
\end{table}
\end{frame}
\end{document}


