Condspe

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Condspe

  1. 1. Learning probabilistic networks of condition-specific response: Digging deep in yeast stationary phase Sushmita Roy∗ , Terran Lane∗ , and Margaret Werner-Washburne+ ∗ Department of Computer Science, University of New Mexico + Department of Biology, University of New Mexico AbstractCondition-specific networks are functional networks of genes describing molecular behavior un-der different conditions such as environmental stresses, cell types, or tissues. These networksfrequently comprise parts that are unique to each condition, and parts that are shared amongrelated conditions. Existing approaches for learning condition-specific networks typically iden-tify either only differences or similarities across conditions. Most of these approaches first learnnetworks per condition independently, and then identify similarities and differences in a post-learning step. Such approaches have not exploited the shared information across conditionsduring network learning. We describe an approach for learning condition-specific networks that simultaneously identi-fies the shared and unique subgraphs during network learning, rather than as a post-processingstep. Our approach learns networks across condition sets, shares data from conditions, and leadsto high quality networks capturing biologically meaningful information. On simulated data from two conditions, our approach outperformed an existing approachof learning networks per condition independently, especially on small training datasets. Wefurther applied our approach to microarray data from two yeast stationary-phase cell popu-lations, quiescent and non-quiescent. Our approach identified several functional interactionsthat suggest respiration-related processes are shared across the two conditions. We also iden-tified interactions specific to each population including regulation of epigenetic expression inthe quiescent population, consistent with known characteristics of these cells. Finally, we foundseveral high confidence cases of combinatorial interaction among single gene deletions that canbe experimentally tested using double gene knock-outs, and contribute to our understanding ofdifferentiated cell populations in yeast stationary phase. 1
  2. 2. 1 IntroductionAlthough the DNA for an organism is relatively constant, every organism on earth has the po-tential to respond to different environmental stimuli or to differentiate into distinct cell-types ortissues. Different environmental conditions, cell-types or tissues can be considered as different in-stantiations of a global variable, the condition variable, which induces condition-specific responses.These condition-specific responses typically require global changes at the transcript, protein andmetabolic levels and are of interest as they provide insight into how organisms function at a systemslevel. Condition-specific networks describe functional interactions among genes and other macro-molecules under different conditions, providing a systemic view of condition-specific behavior inorganisms. Analysis of condition-specific responses has been one of the principal goals of molecular biology,and several approaches have been developed to capture condition-specific responses at differentlevels of granularity. The most common approach is the identification of differentially expressedgenes in a condition of interest using genome-wide measurements of gene, and often protein expres-sion [20]. More recent approaches are based on bi-clustering, which cluster genes and conditionssimultaneously [5,7,9,29], and identify sets of genes that are co-regulated in sets of conditions. How-ever, these approaches do not provide fine-grained interaction structure that explains the condition-specific response of genes. More advanced approaches additionally identify transcription modules(set of transcription factors regulating a set of target genes) that are co-expressed in a condition-specific manner [11,13,26,31], but these too do not provide detailed interaction information amonggenes for each condition. In this paper, we describe a novel approach, Network Inference with Pooling Data (NIPD), forcondition-specific response analysis that emphasizes the fine-grained interaction patterns amonggenes under different conditions. The main conceptual contribution of our approach is to learnnetworks for any subset of conditions. This subsumes existing approaches that find either onlypatterns that are specific to each condition, or only patterns that are shared across conditions.To make this clear, let us consider a simple example of two environmental starvation conditions:Carbon and Nitrogen starvation. Using our approach we can simultaneously find patterns that are 2
  3. 3. specific only to Carbon starvation, only to Nitrogen starvation, and those that are shared acrossthese two conditions. From the methodological stand-point our work is similar to Bayesian multi-nets [10], which we extend by allowing data to be pooled across conditions and learning networksfor any subset of conditions. NIPD is based on the framework of probabilistic graphical models (PGMs), where edges rep-resent pairwise and higher-order statistical dependencies among genes. Similar to existing PGMlearning algorithms, NIPD infers networks by iteratively scoring candidate networks and selectingthe network with the highest score [12]. However, NIPD uses a novel score that evaluates candidatenetworks with respect to data from any subset of conditions, pooling data for subsets with morethan one conditions. This subset score and search strategy of NIPD incorporates and exploits theshared information across the conditions during structure learning, rather than as a post-processingstep. As a result, we are able to identify sub-networks not only specific to one condition, but to mul-tiple conditions simultaneously, which allows us to build a more holistic picture of condition-specificresponse. The data pooling aspect of NIPD makes more data available for estimating parameters forhigher-order interactions, i.e., interactions among more than two genes. This enables NIPD torobustly estimate higher-order interactions, which are more difficult to estimate due to the highnumber of parameters relative to pairwise dependencies. By formulating NIPD in the framework of PGMs we have additional benefits: (a) PGMs aregenerative models of the data, providing a system-wide description of the condition-specific behavioras a probabilistic network, (b) the probabilistic component naturally handles noise in the data,(c) the graph structure captures condition-specific behavior at the level of gene-gene interactions,rather than coarse clusters of genes, (d) the PGM framework can be easily extended to morecomplex situations where the condition variable itself may be a random variable that must beinferred during network learning. We implement NIPD with undirected, probabilistic graphicalmodels [14]. However, the NIPD framework is applicable to directed graphs as well. We are not the first to propose networks for capturing condition-specific behavior [24, 34].Several network-based approaches have been developed for capturing condition-specific behavior 3
  4. 4. such as disease-specific subgraphs in cancer [8], stress response networks in yeast [21], or networksacross different species [4,28]. However, these approaches are not probabilistic in nature, often relyon the network being known, and are restricted to pairwise co-expression relationships rather thangeneral statistical dependencies. Other approaches such as differential dependency networks [34],and mixture of subgraphs [24], construct probabilistic models, but focus on differences ratherthan both differences and similarities. The majority of these approaches infer a network for eachcondition separately, and then compare the networks from different conditions to identify the edgescapturing condition-specific behavior. We compared NIPD against an existing approach for learning networks from the conditionsindependently. We refer to this approach as INDEP, which represents a general class of existingalgorithms that learn networks per condition independently. On simulated data from networkswith known ground truth, NIPD inferred networks with higher quality than did INDEP, especiallyon small training datasets. We also applied our approach to microarray data from two yeast(Saccharomyces cerevisiae) cell types, quiescent and non-quiescent, isolated from glucose-starved,stationary phase cultures [2]. Networks learned by NIPD were associated with many more Geneontology biological processes [3], or were enriched in targets of known transcription factors (TFs)[17], than networks learned by INDEP. Many of the TFs were involved in stress response, whichis consistent with the fact that the populations are under starvation stress. NIPD also identifiedmany more shared edges, which represent biologically meaningful dependencies than the INDEPapproach. This suggests that by pooling data from multiple conditions, we are able to not onlycapture shared structures better, but also to infer networks with higher overall quality.2 ResultsThe goal of our experiments was three fold: (a) to examine the quality of condition-specific net-works inferred by our approach that combines data from different conditions (NIPD) versus anindependent learner (INDEP), (b) to evaluate the algorithmic performance (measured by networkstructure quality) as a function of training data size, (c) analyze how two different cell populationsbehave, at the network level, in response to the same starvation stress. We address (a) and (b) 4
  5. 5. on simulated data from networks with known topology, giving us ground truth to directly validatethe inferred networks. We address (c) on microarray data from two yeast cell populations isolatedfrom glucose-starved stationary phase cultures [2].2.1 NIPD had superior performance on networks with known ground truthWe simulated data from two sets of networks, each set with two networks, one network per condition.In the first, HIGHSIM, the networks for the two conditions, shared a larger portion (60%) of theedges, and in the second, LOWSIM, the networks shared a smaller (20%) portion of the edges.We compared the networks inferred by NIPD to those inferred by INDEP by assessing the matchbetween true and inferred node neighborhoods (See Supplementary Methods). Briefly, the data weresplit into q partitions, where q ∈ {2, 4, 6, 8, 10}, and networks learned for each partition. The size ofthe training data decreased with increasing q. We first evaluated overall network structure qualityby obtaining the number of nodes on which one approach was significantly better (t-test p-value,< 0.05) in capturing its neighborhood as a function of q. On LOWSIM, NIPD was significantlybetter for smaller amounts of training data. On HIGHSIM, NIPD performed significantly betterthan INDEP for all training data sizes (Fig 1). Next, we evaluated how well the shared edges were captured as a function of decreasing amountsof training data (Supplementary Fig 1). NIPD captured shared edges better than INDEP onLOWSIM as the amounts of training data decreased. NIPD was better than INDEP on HIGHSIMregardless of the size of the training data. Our results show that when the underlying networks corresponding to the different conditionsshare a lot of structure, NIPD has a significantly greater advantage than INDEP, which does not doany pooling. Furthermore, as training data size decreases, NIPD is better than INDEP for learningboth overall and shared structures, independent of the extent of sharing in the true networks.2.2 Application to yeast quiescenceWe applied NIPD to microarray data from two yeast cell populations, quiescent (QUIESCENT)and non-quiescent (NON-QUIESCENT), isolated from glucose starvation-induced stationary phase 5
  6. 6. cultures [2]. The two cell populations are in the same media but have differentiated physiologicallyand morphologically, suggesting that each population is responding differently. We learned networksusing NIPD and INDEP treating each cell population as a condition. Because each array in thedataset was obtained from a single gene deletion mutant, the networks were constrained such thatgenes with deletion mutants connected to the remaining genes1 . The inferred networks from both methods were evaluated using information from Gene Ontology(GO) process, GO Slim [3] and transcriptional regulatory networks [17]. Gene Ontology is ahierarchically structured ontology of terms used to annotate genes. GO slim is a collapsed singlelevel view of the complete GO terms, providing high level information of the processes, functionsand cellular locations involving a set of genes. Finally, we analyzed combinations of genes withdeletions that were in the neighborhood of other non deletion genes.2.2.1 NIPD identified more biologically meaningful dependenciesTo determine if one network was more biologically meaningful than the other, we examined the net-works based on Gene Ontology (GO) slim category (process, function and location), transcriptionfactor binding data and GO process, referred as GOSLIM, TFNET and GOPROC, respectively(Fig 2). Network quality was determined by the number of GOSLIM categories (or TFNET orGOPROC) with better coverage than random networks (See Methods). Both approaches wereequivalent for GOSLIM, with INDEP outperforming NIPD in QUIESCENT and NIPD outper-forming INDEP on NON-QUIESCENT. NIPD outperformed INDEP with a larger margin thanwas outperformed on TFNET categories from NON-QUIESCENT. NIPD was consistently betterthan INDEP on GOPROC categories. The networks learned by NIPD had many more edges than the networks learned by INDEP(Supplementary Table 1). To estimate the proportion of the edges capturing biologically meaningfulrelationships, we computed semantic similarity of genes connected by the edges [16]. Although bothINDEP and NIPD had significantly better semantic similarity than random networks, INDEPdegraded in p-value for QUIESCENT at the highest value of semantic similarity (Fig 3). NIPD- 1 This is not a bi-partite graph because the genes with deletion mutants are allowed to connect to each other. 6
  7. 7. inferred networks had many more edges with high semantic similarity than INDEP, while keepingthe proportion of edges satisfying a particular semantic similarity threshold close to INDEP. Thissuggests that NIPD identifies more dependencies that are biologically relevant than INDEP withoutsuffering in precision.2.2.2 NIPD identified more shared edges representing common starvation responseWe performed a more fine-grained analysis of the inferred networks by considering each gene andits immediate neighborhood and tested whether these gene neighborhoods were enriched in GObiological processes, or in the target set of transcription factors (TFs) (See Methods). Using a falsediscovery rate (FDR) cutoff of 0.05, we identified many more subgraphs in the networks inferredby NIPD than by INDEP to be enriched in a GO process or in targets of TFs (Figs 4, 5). NIPDidentified more processes and larger subgraphs in both populations (oxidative phosphorylation,protein folding, fatty acid metabolism, ammonium transport) than did INDEP. NIPD identified subgraphs involved in aerobic respiration and oxidative phosphorylation wereenriched in targets of HAP4, a global activator for respiration genes. The presence of HAP4 targetsin both cell populations makes sense because both populations are experiencing glucose starvationand must switch to respiration for deriving energy. We also found the TFs, MSN2, MSN4, andHSF1, regulating subgraphs involved in protein folding. These TFs activate stress responses andare known to activate genes involved in heat, oxidative and starvation stress. We also foundtargets of SIP4 in both populations. SIP4 is a transcriptional activator of gluconeogenesis [32],expressed highly in glucose repressed cells [15], and therefore would be expected to be present inboth quiescent and non-quiescent cells. In contrast, the only shared regulatory connection foundby INDEP was HAP4. We conclude that the NIPD approach identified more networks that werebiologically relevant and informative about glucose starvation response than did INDEP. 7
  8. 8. 2.2.3 Wiring differences in NIPD-inferred networks exhibit population-specific star- vation responseNIPD identified several processes associated exclusively with quiescent cells. This included regu-latory processes (regulation of epigenetic gene expression, and regulation of nucleobase, nucleosideand nucleic acid metabolism) and metabolic processes (pentose phosphate shunt). These werenovel predictions that highlight differences between these cells based on network wiring. INDEPidentified only one population-specific GO process (response to reactive oxygen species in NON-QUIESCENT). An INDEP identified subgraph specific to quiescent (protein de-ubiquitination), wasactually a subset of the NIPD-identified subgraph involved in epigenetic gene expression regulation,indicating that NIPD subsumed most of the information captured by INDEP. NIPD QUIESCENT networks contained subgraphs enriched exclusively in targets of SKO1, andAZF1. Both of these are zinc finger TFs, with AZF1 protein expressed highly under non-fermentablecarbon sources [27], and SKO1 which regulates low affinity glucose transporters [30], and are bothconsistent with the condition experienced by these cells. Unlike NIPD, which identified SIP4 tobe associated with both populations, INDEP identified SIP4 only in QUIESCENT. However, aswe describe in the previous section, it is more likely that SIP4 is involved in both QUIESCENTand NON-QUIESCENT populations. INDEP also found the TFs YAP7 and AFT2 exclusively inQUIESCENT and NON-QUIESCENT, respectively. YAP7 is involved in general stress responseand would be expected to have targets in both QUIESCENT and NON-QUIESCENT. AFT2 isrequired under oxidative stress and is consistent with the over-abundance of reactive oxygen speciesin NON-QUIESCENT population [1]. NIPD also identified wiring differences in the subgraphs involved in shared processes. For ex-ample in addition to HAP4, NIPD identified HAP2 as an important TF in QUIESCENT. Thepresence of both HAP2 and HAP4 makes biological sense because they are both part of theHAP2/HAP3/HAP4/HAP5 complex required for activation of respiratory genes. The presenceof both HAP2 and HAP4 in QUIESCENT, but not NON-QUIESCENT, suggests that the QUI-ESCENT population maybe better equipped for respiration and long term survival in stationaryphase. 8
  9. 9. Overall, the NIPD inferred networks captured key differences and similarities in metabolic andregulatory processes, which are consistent with existing information about these cell populations[1,2], and also include novel findings that can provide new insight into starvation response in yeast.2.2.4 NIPD identified several knock-out combinationsThe microarrays used in this study measured expression profile of single gene deletions that werepreviously identified to be highly expressed at the mRNA level in stationary phase. We constrainedthe inferred networks to identify neighborhoods of genes comprising only the genes with deletionmutants, allowing us to identify combinations of such deletion mutants and their targets. Such com-binations can be validated in the laboratory to verify cross-talk between pathways. We found thatNIPD-inferred networks contained significantly more deletion combinations compared to randomnetworks for both the quiescent and non-quiescent populations (p-value < 3E-10, SupplementaryTables 3, 4, 5), which was not the case for the INDEP-identified networks (Supplementary Tables 6,7). A more stringent analysis of the knock-out combinations using GO process semantic similar-ity identified several double knock-out and target gene candidates (Supplementary Table 2). Wealso found more deletion combinations in NON-QUIESCENT compared to QUIESCENT. This isconsistent with the identification of many more mutants affecting non-quiescent than quiescentcells [2]. In QUIESCENT, we found three genes that were all likely down-stream targets of aCOX7-QCR8 double knock-outs, all involved in the cytochrome-c oxidase complex of the mito-chondrial inner membrane. Other deletion mutant combinations were involved in mitochondrialATP synthesis and ion transport. Many of these genes have been shown to be required for qui-escent non-quiescent cell function, viability and survival [2, 18]. In NON-QUIESCENT, we foundseveral knock-out combinations involved in oxidative phosphorylation, aerobic respiration etc, in-cluding a novel combination, YMR31 and QCR8, connected to TPS2. All three genes are found inthe mitochondria, which play a critical and complex role in starved cells, but the exact mechanismsare not well-understood. Experimental analysis of this triplet can provide new insights into the roleof mitochondria in glucose-starved cells. In summary, these results demonstrated another benefit 9
  10. 10. of data pooling in NIPD: learning more complex, combinatorial relationships among genes.3 DiscussionInference and analysis of cellular networks has been one of the cornerstones of systems biology.We have developed a network learning approach, Network Inference with Pooling Data (NIPD) tocapture a systemic view of condition-specific response. NIPD is based on probabilistic graphicalmodels and infers the functional wiring among genes involved in condition-specific response. Thecrux of our approach is to learn networks for any subset of conditions capturing fine-grained geneinteraction patterns not only in individual conditions but in any combination of conditions. Thisallows NIPD to robustly identify both shared and unique components of condition-specific cellularnetworks. In comparison to an approach that learns networks independently (INDEP), NIPD(a) pools data across different conditions, enabling better exploitation of the shared informationbetween conditions, (b) learns better overall network structures in the face of decreasing amountsof training data, and (c) learns structures with many more biologically meaningful dependencies. Small training data sets, which are especially common for biological data, present significantchallenges for any network learning approach. In particular, approaches such as INDEP may learndrastically different networks due to small data perturbations leading to differences that are notbiologically meaningful. NIPD is more resilient to small perturbations because by pooling datafrom different conditions during network learning, NIPD effectively has more data for estimatingparameters for the shared parts of the network. Another challenge in the analysis of condition-specific networks is to extract patterns thatare shared across conditions. Approaches such as INDEP that learn networks for each conditionindependently, and then compare the networks, are more likely to learn different networks makingit difficult to identify the similarities across conditions. Application of both NIPD and INDEPapproaches to microarray data from two yeast populations showed that many of subgraphs thatwould be considered specific to each population by INDEP, were actually shared biological processesthat must be activated in both populations irrespective of their morphological and physiologicaldifferences. 10
  11. 11. One of the strengths of NIPD in comparison with INDEP was its ability to identify pairs of genedeletions and downstream targets using data from individual gene deletions. Amazingly, severalof these gene deletions are already known to have a phenotypic effect on stationary phase culturesand often on quiescent or non-quiescent cells (Supplementary Table 2) [2,18]. These predictions aretherefore good candidates for future experiments using double deletion mutants, and are a drasticreduction of the space of possible combinations of sixty-nine single gene deletions. Identification ofpopulation-specific malfunctions in signaling pathways via experimental analysis of these multipledeletions can provide new insight into aging and cancer studies using yeast stationary phase as amodel system. The NIPD approach establishes ground-work for important future enhancements, including theability to efficiently learn networks from many conditions. The probabilistic framework of NIPD canbe easily extended to automatically infer the condition variable to make NIPD widely applicable todatasets with uncertainty about the conditions. The NIPD approach can also integrate novel typesof high-throughput data including RNASeq [33] and ChipSeq [25]. These extensions will allowus to systematically identify the parts, and the wiring among them that determine stage-specific,tissue-specific and disease specific behavior in whole organisms.4 Methods4.1 Independent learning of condition-specific networks: INDEPExisting approaches of learning condition-specific networks [4, 21, 28] can be considered as spe-cial cases of a general independent learning approach, INDEP, where networks for each conditionare learned independently and then compared to identify network parts unique or shared acrossconditions. Let {D1 , · · · , Dk } denote k datasets from k conditions. In the INDEP approach, each networkGc , 1 ≤ c ≤ k, is learned independently using data from Dc only. Our implementation of theINDEP framework considered each Gc as an undirected probabilistic graphical model, or a Markovrandom field (MRF) [14], which like Bayesian networks, can capture higher-order dependencies, 11
  12. 12. but additionally captures cyclic dependencies. We use a pseudo-likelihood framework with anMDL penalty to learn the structure of the MRF [6]. The pseudo-likelihood score for a network NGc describing data Dc is PLL(Gc ) = i=1 PLLV(Xi , Mci , c) where X1 , · · · , XN are the randomvariables (one for each gene), encoding the expression value of a gene. PLLV is Xi ’s contribution tothe overall pseudo-likelihood and is defined, including a minimum description length (MDL) penalty, |Dc | |θci |log(|Dc |)as PLLV(Xi , Mci , c) = d logP (Xi = xdi |Mci = mcdi ) + 2 . Here Mci is the Markovblanket (MB) of Xi in condition c and xdi and mcdi are assignments to Xi and Mci , respectivelyfrom the dth data point. θci are the parameters of the conditional distribution P (Xi |Mci ). Weassume the conditional distributions to be conditional Gaussians. The structure learning algorithmfor each graph is described in [22].4.2 Network Inference with Pooling Data: NIPDThe NIPD approach that we present extends the INDEP approach by incorporating shared infor-mation across conditions during structure learning. In this framework, we do not learn networksfor each condition c separately. Instead, we devise a score for each edge addition that considersnetworks for any subset of the conditions. Let C denote the set of k conditions. For a non-singletonset, E ⊆ C, we pool the data from all conditions e ∈ E and evaluate the overall score improve-ment on adding an edge to networks for all e ∈ E. To learn {G1 , · · · , Gk } for the k conditionssimultaneously, we maximize the following MDL-based score: S(G1 , · · · , Gk ) = P (D1 , · · · , Dk |θ1 , · · · , θk )P (θ1 , · · · , θk |G1 , · · · , Gk ) + MDL Penalty (1)Here θ1 , · · · , θk are the maximum likelihood parameters for the k graphs. We assume P (Dc |θ1 , · · · , θk ) =P (Dc |θc ). That is, if we know the parameters θc , the likelihood of the data from condition, Dc , given kθc can be estimated independently. Thus, P (D1 , · · · , Dk |θ1 , · · · , θk ) = c=1 P (Dc |θc ). Because ournetworks are MRFs, we use pseudo-likelihood PLL(Dc ). We expand the complete condition-specificparameter set θc , to {θc1 , · · · , θcN }, which is the set of parameters of each variable Xi , 1 ≤ i ≤ N , 12
  13. 13. in condition c. Using the parameter modularity assumption for each variable, we have: N P (θ1 , · · · , θk |G1 , · · · , Gk ) = P (θ1i , · · · , θki |M1i , · · · , Mki ) (2) i=1Note the parameters of conditional probabilities of individual random variables are independent, butthe parameters per variable are not independent across conditions. To enforce dependency amongthe θci , we make Mci depend on all the neighbors of Xi in condition c and all sets of conditionsthat include c. To convey the intuition behind this idea, let us consider the two condition caseC = {A, B}. A variable Xj can be in Xi ’s MB in condition A, either if it is connected to Xi onlyin condition A, or if it is connected to Xi in both conditions A and B. Let M∗ be the set of Aivariables that are connected to Xi only in condition A but not in both A and B. Similarly, letM∗ {A,B}i denote the set of variables that are connected to Xi in both A and B conditions. Hence,MAi = M∗ ∪ M∗ Ai {A,B}i . More generally, for any c ∈ C, Mci = ∗ E∈powerset(C) : c∈E MEi , where M∗ Eidenotes the neighbors of Xi only in condition set E. To incorporate this dependency in the structurescore, we need to define P (Xi |Mci ) such that it takes into account all subsets E, c ∈ E. We assumethat the MBs, M∗ , independently influence Xi . This allows us to write P (Xi |Mci ) as a product: EiP (Xi |Mci ) ∝ ∗ E∈powerset(C) : c∈E P (Xi |MEi ). To learn the k graphs, we exhaustively enumerateover condition sets, E, and estimate parameters θEi by pooling the data for all non-singleton E. Our structure learning algorithm maintains a conditional distribution for every variable, Xi forevery set E ∈ powerset(C). We consider the addition of an edge {Xi , Xk } in every set E. This addi-tion will affect the conditionals of Xi and Xj in all conditions e ∈ E. Because the MB per conditionset independently influence the conditional, the pseudo-likelihood PLLV(Xi , Mei , e) decomposes as ∗ E s.t: e∈E PLLV(Xi , MEi , e) (Supplementary information). The net score improvement of addingan edge {Xi , Xj } to a condition set E is given by: |De | ∆Score{Xi ,Xj },E = PLLV(Xi , Mei ∪ {Xj }, e) − PLLV(Xi , Mei , e) + e∈E d=1 PLLV(Xj , Mej ∪ {Xi }, e) − PLLV(Xj , Mej , e) (3) 13
  14. 14. Because of the decomposability of PLLV(Xi |Mei ), all terms other than those involving the Markovblanket variables in condition set E remain unchanged producing the score improvement: ∆Score{Xi ,Xj },E = PLLV(Xi |M∗ ∪ Xj ) − PLLV(Xi |M∗ ) Ei EiThis score decomposability allows us to efficiently learn networks over condition sets. Our structurelearning algorithm is described in more detail in Supplementary material.4.3 Simulated data description and analysisWe generated simulated datasets using two sets of networks of known structure, HIGHSIM andLOWSIM. All networks had the same number of nodes n = 68 and were obtained from the E. coliregulatory network [23]. We used the INDEP model for generating the eight simulated datasets.The parameters of the INDEP model were initialized using random partitions of an initial datasetgenerated from a differential-equation based regulatory network simulator [19].4.4 Microarray data descriptionEach microarray measures the expression of all yeast genes in response to genetic deletions fromquiescent (85) and non-quiescent (93) populations [2], with 69 common to both populations. Thearrays had biological replicates producing 170 and 186 measurements per gene in the quiescentand non-quiescent populations, respectively. We filtered the microarray data to exclude genes with> 80% missing values, resulting in 3,012 genes. We constrained the network structures such that agene connected to only the 69 genes with deletion mutants and no gene had more than 8 neighbors.4.5 Validation of network edges using coverage of annotation categoriesThe coverage of an annotation category A is defined as the harmonic mean of a precision andrecall. Let L denote the complete list of genes used for network learning, LA ⊆ L denote the genesannotated with A. Let lA denote the number edges in our learned network among two genes giand gj , such that gi ∈ LA and gj ∈ LA . Let tA be the total number of edges that are connected togenes in LA (note tA > lA ). Let sA denote the total number of edges that could exist among the 14
  15. 15. |LA |genes in LA , which is 2 if |LA | < 8 and |LA | ∗ 8 if |LA | > 8. Precision for category A is defined lA lAas pA = tA and recall is defined as rA = sA . These are used to define the coverage of category A,2pA rApA +rA . We compute this coverage score for all categories using each inferred network, and comparethe score against an expected coverage from random networks with the same degree distribution. To compare of NIPD against INDEP, assume we were comparing the inferred quiescent networks.Let AINDEP and ANIPD denote the categories better than random in the INDEP and NIPD quiescentnetworks, respectively. To determine how much better INDEP is than NIPD, we obtain the numberof categories in AINDEP ∪ ANIPD on which INDEP has a better coverage than NIPD. We similarlyassess how much better NIPD is than INDEP. We repeat this procedure for the non-quiescentnetworks. We also compared the semantic similarity of edges in inferred and random networks [16](Supplementary material).4.6 Evaluation of gene deletion combinationsWe identified combinations of genes with deletion mutants from Markov blankets comprising > 1 ofthese deletion genes. We evaluated each algorithm’s ability to capture gene deletion combinationsby comparing the number of such combinations in random networks with the same number ofedges. This random network model provided a rough significance assessment on the number ofinferred knock-out combinations (Supplementary Table 3). We then performed a more stringentanalysis based on semantic similarity, using the sub-network spanning only the genes with deletioncombinations. We generated random networks with the same degree distributions as this sub-network and computed the semantic similarity of each gene with the set of deletion genes connectedto it, in the inferred and random networks. We then selected genes with significantly higher semanticsimilarity than in random networks (ztest, p-value <0.05).5 AcknowledgementsThis work is supported by grants from NIMH (1R01MH076282-03) and NSF (IIS-0705681) toT.L., from NIH (GM-67593) and NSF (MCB0734918) to M.W.W. and from HHMI-NIH/NIBIB(56005678). 15
  16. 16. HIGHSIM NET1 LOWSIM NET1 %" + , 9:;< :;<= %# :9<=; ;:=>< 4+,-+50/(067*8 ! 5,-.,610)178+9 " $ # # !" + !$ , !"# $$" %"# %%$ &# %"# %%$ &# !"# $$" %"# %%$ &# ()*+,-+./0(1(12+30.0 ()*+,-.,/01)2)23,41/1 HIGHSIM NET2 LOWSIM NET2 %# + ? , :;<= 9:;< ;:=>< :9<=; 5,-.,610)178+9 4+,-+50/(067*8 " ! $ # # !" + !$ , !"# $$" %"# %%$ &# %"# %%$ &# !"# $$" %"# %%$ &# ()*+,-+./0(1(12+30.0 ()*+,-.,/01)2)23,41/1Figure 1: Number of variables (y-axis) on which one method was significantly better than the otheras function of the size of the training data (x-axis). Left is for the two networks (HIGHSIM) thatshare 60% edges and right is for the two networks (LOWSIM) that share 20% of their edges. Thetop and bottom graphs are for networks from the individual conditions. GOSLIM  TFNET  GOPROC  16  INDEP>NIPD  16  INDEP>NIPD  80  INDEP>NIPD  NIPD>INDEP  NIPD>INDEP  # of Categories  # of Categories  # of Categories  12  12  60  NIPD>INDEP  8  8  40  4  4  20  0  0  0  QUIESCENT  NON‐QUIESCENT  QUIESCENT  NON‐QUIESCENT  QUIESCENT  NON‐QUIESCENT Figure 2: Network quality comparison based on coverage of GOSlim (GOSLIM), targets of tran-scription factors (TFNET) and GO process (GOPROC). Each bar represents the number of cat-egories on which INDEP had better coverage than NIPD (INDEP>NIPD) or NIPD had bettercoverage than INDEP (NIPD>INDEP).References [1] C. Allen, S. B¨ttner, A. D. Aragon, J. A. Thomas, O. Meirelles, J. E. Jaetao, D. Benn, u S. W. Ruby, M. Veenhuis, F. Madeo, and M. Werner-Washburne. Isolation of quiescent and nonquiescent cells from yeast stationary-phase cultures. J Cell Biol, 174(1):89–100, July 2006. [2] Anthony D. Aragon, Angelina L. Rodriguez, Osorio Meirelles, Sushmita Roy, George S. David- son, Chris Allen, Ray Joe, Phillip Tapia, Don Benn, and Margaret Werner-Washburne. Charac- terization of differentiated quiescent and non-quiescent cells in yeast stationary-phase cultures. Molecular Biology of the Cell, 2008. [3] M. Ashburner, C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, 16
  17. 17. RAND (NIPD)  7  7  QUIESCENT  NON‐QUIESCENT  RAND (NIPD)  NIPD  NIPD  6  6  RAND (INDEP)  RAND (INDEP)  5  INDEP  5  INDEP  log(# of Edges)  log(# of Edges)  4  4  3  3  2  2  1  1  0  0  ‐1  ‐1  0  0.2  0.4  0.6  0.8  1  1.2  1.4  0  0.2  0.4  0.6  0.8  1  1.2  1.4  Seman1c Similarity  Seman1c Similarity Figure 3: Network quality comparison based on semantic similarity. The dashed lines representsthe background distribution generated from random networks and the solid lines represents thedistribution of the semantic similarity in the inferred networks. HAP4_TF HAP2_TF SIP4_TF LPD1 NDE2 ATP3 CCW12 KNS1 MIR1 YGL088W ATX2 IDP2 YGR001C YNL194C SDS23 YOR052C SNC2 UBC8 COX13 ATP2 COX7 QCR8 COX8 NDI1 ATP16 PCK1 FAS1 SDH2 YET3 NBP2 PIN3 ILV1 CDC48 AVT7 INH1 AAT2 QCR7 ERV46 PTR2 THO1 ICL1 QCR6 KGD1 QCR9 acetyl-CoA metabolic process organelle ATP synthesis coupled electron transport oxidative phosphorylation aerobic respiration MSN2_TF MSN4_TF HSF1_TF SKO1_TF AZF1_TF YDJ1 IRA2 STI1 PRB1 HSP30 HSP42 HSP104 HSP78 XBP1 OM14 YDR266C FAA1 HXT5 SIS1 BIO2 protein folding SBE22 UBP10 YMR144W ADH2 PDC5 YMR187C EMP46 GDH3 YMR090W PUF4 SWP1 REG2 FOX2 GAC1 PDC1 CTA1 DOA4 YJL016W SIP18 CAT2 ALD4 PXA1 ISW2 PAI3 ALD3 ALD2 ATO3 ADY2 UTR1 YDR154C regulation of gene expression, epigenetic MUQ1 nitrogen utilization ammonium transport regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process ethanol metabolic process polyamine catabolic process beta-alanine biosynthetic process LSC2 MDH3 FMP37 MSS18 CUP2 MAP1 PEX11 SOD1 FDH1 SOL4 YMR118C GND2 ACS1 RPL2A SFA1 ETR1 CRS5 FTH1 AYR1 PAT1 HSP26 TKL2 RDH54 YJR096W FYV7 YDL218W carboxylic acid biosynthetic process NADH regeneration response to metal ion fatty acid metabolic process pentose-phosphate shunt pentose metabolic processFigure 4: GO processes and TF targets for subgraphs from the NIPD-inferred networks using thequiescent population. The text below each subgraph indicates the process. The diamonds representthe TFs. A TF is connected to the subgraph which is enriched in the targets of the TF. The circularnodes represent the genes in the network and color represents the extent of differential expression,red: up-regulated, green: down-regulated. 17
  18. 18. HAP4_TF MSN4_TF MSN2_TF HSF1_TF SIP4_TF KGD2 MIR1 PTR2 PMT1 ATP1 HSP42 CDC48 STI1 HSP104 PCK1 ATP2 SOD1 ATP16 HSP12 PIN3 CCW12 HSP30 HSP26 URA6 SIS1 SDH2 ICL1 RIP1 BSD2 PGM2 SSA2 YJR096W TDH1 IDP2 HSP78 SSE2 ion transport oxidative phosphorylation protein folding PST2 PUS5 YER121W ACS1 RPS14A AYR1 MDH3 ADH2 FOX2 PXA1 CYB2 PEX11 ADY2 ATO3 YKL187C FMP37 RPL25 LSC2 ETR1 ammonium transport nitrogen utilization energy derivation by oxidation of organic compounds fatty acid metabolic process UTR1 YGR201C CRC1 SOL4 YIR035C APJ1 ARO3 GSC2 EMP46 COX13 YDR154C YMR114C TPS2 YAT2 PYC2 ILV1 ALD3 COX7 AVT6 ALD2 QCR9 URA2 QCR8 YAT1 GDH3 QCR6 beta-alanine biosynthetic process mitochondrial electron transport, ubiquinol to cytochrome c polyamine catabolic process aerobic respiration carnitine metabolic processFigure 5: GO processes and TF targets for subgraphs from the NIPD-inferred networks using thenon-quiescent population. Legend is similar to Fig 4 K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin, and G. Sherlock. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25(1):25–29, May 2000. [4] S. Bergmann, J. Ihmels, and N. Barkai. Similarities and differences in genome-wide expression data of six organisms. PLoS Biol, 2(1), January 2004. [5] Sven Bergmann, Jan Ihmels, and Naama Barkai. Iterative signature algorithm for the analysis of large-scale gene expression data. Physical review. E, Statistical, nonlinear, and soft matter physics, 67(3 Pt 1), March 2003. [6] Julian Besag. Efficiency of pseudolikelihood estimation for simple gaussian fields. Biometrika, 64(3):616–618, December 1977. [7] Richard Bonneau, David J Reiss, Paul Shannon, Marc Facciotti, Leroy Hood, Nitin S Baliga, and Vesteinn Thorsson. The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biology, 2006. 18
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  22. 22. Appendix1 Generation and analysis of simulated dataWe first obtained a sub-network of n = 68 nodes, G1 , from the E. coli regulatory network [23]. Wethen generated two networks, G2 and G3 , by flipping 20% and 60% of G1 ’s edges, respectively.{G1 , G2 } comprised networks in HIGHSIM and {G1 , G3 } comprised networks in LOWSIM. Foreach pair of networks, we generated initial datasets using a differential equation-based gene regu-latory network simulator [19]. We then split the data into two parts, learned two INDEP modelsfor each partition, and generated data from these models. We repeated this procedure four timesproducing eight sets of simulated data with different parameters but the same network topology.It was possible to generate all eight sets from the regulatory network simulator by perturbing thekinetic constants, but our current data generation procedure was faster. We compared the structure of the networks inferred by INDEP and NIPD using a per-variableneighborhood comparison. Assume we are comparing the INDEP-inferred networks against the truenetworks in HIGHSIM. We compare each of the true networks, {G1 , G2 } one at a time. Let GINDEP 1and GINDEP be the two inferred networks inferred by INDEP using datasets from HIGHSIM. For 2each variable, Xi , we compare Xi ’s neighborhood in G1 to its inferred neighborhoods in bothGINDEP and GINDEP to obtain match score Fi1 1 2 INDEP and F INDEP , respectively. INDEP’s match of i2 INDEP and F INDEP . We obtain a match score for differentXi ’s neighborhood in G1 is the max of Fi1 i2folds of the data. Similarly we obtain a match score for NIPD for all variables from different foldsof the data. We then obtain the number of variables on which NIPD has a significantly highermatch score compared to INDEP as a function of training data size. We repeat this procedurefor all eight datasets for HIGHSIM to obtain the average number of variables NIPD is better thanINDEP. We repeat this procedure for G2 and then for the NIPD. 22
  23. 23. 2 Semantic similarity based-validationWe use the definition of semantic similarity from Lord et al. using [16]. Semantic similarity betweentwo annotation terms is defined as a function of the maximal amount of information present in acommon ancestor of the terms. For GO terms the information is inversely proportional to thenumber of genes that are annotated with a term, that is a very specific term with few genes hasmore information than a broader term that has many more genes annotated with it. The functionalsimilarity between two genes is given by the average semantic similarity of sets of GO process termsassociated with the genes. Let gi and gj be two genes connected by an edge in our inferred network.Let Ti and Tj be the set of GO process terms associated with gi and gj , respectively. The averagesemantic similarity, sim(gi , gj ) for all pairs of terms is 1 sim(gi , gj ) = semsim(tp , tq ) |Tp | ∗ |Tq | tp ∈Ti ,tq ∈TjSemantic similarity, semsim(tp , tq ) = −log(mina∈Ppq pa ), where Ppq is the set of common ancestorsof the terms tp and tq in the GO process “is-a” hierarchy. −log(pa ) is the amount of informationassociated with a term a, and pa is probability of the term defined as the ratio of the number ofgenes annotated with the term a to the total number of genes with a GO process assignment. We used semantic similarity for global validation of the inferred edges and also for assessingthe strength of association between combinations of single gene knock-outs and a target gene.In both cases, we generated random networks with the same degree distributions as the inferrednetworks and estimated a background semantic similarity distribution. For assessing the strengthof association between a gene, gi and the set of knock-out genes that are connected to it, Ki , wehad to compare the similarity of a gene with a set of genes. We assumed GO process terms forthe set Ki to be the union of all terms associated with the genes, gj ∈ Ki . We then computed thesemantic similarity between the term set associated with gene gi and the union of terms associatedwith Ki . 23
  24. 24. 3 Structure learning algorithm of NIPD in detailOur score for structure learning is based on the pseudo-likelihood of the data given model andrequires us to compute the conditional probability distribution of each variable in a condition c.We require that the parameters of this conditional distribution be dependent such that we can poolthe data from the different conditions to estimate the parameters. The conditional distribution,P (Xi |Mci ) in condition c is defined as a product: P (Xi = xid |Mci = mcid ) ∝ P (Xi = xdi |M∗ = m∗ ), Ei Ei (4) E∈powerset(C) : c∈Ewhere d is the data point index and M∗ is the Markov blanket (MB) of Xi exclusively in condition E 1set E. The proportionality term can be eliminated using the normalization term Zcid . In our 1 2 2 2conditional Gaussian case, Z1id = N (µ1id |µ3id , σ1i + σ3i ), where σ3i is the standard deviation fromthe condition set {1, 2}, µ1id = w1i m∗ , is the mean of the conditional Gaussian using the dth data T 1id 1point in condition 1. Thus, Z1id is the probability of µ1id from a Gaussian distribution with meanestimated from the pooled data. To make the product in Eq 4 a valid conditional distribution, weneed to subtract out the normalization term. However, working with the unnormalized form givesus three benefits. First, and most important, it enables our score to be a decomposable sum ontaking logarithms. Second the normalization term behaves as a smoothing term for a condition-specific mean, µ1id , preferring network structures with means µ1id closer to the shared mean µ3id .Third, avoiding the computation of the Zid for each data point, gives us some runtime benefits. Our structure learning algorithm begins with k empty graphs and proposes edge additions for allvariables, for all subsets of the condition set C. The while loop iteratively makes edge modificationsuntil the score no longer improves. The outermost for loop (Steps 4-17 ) iterates over variablesXi to identify new candidate MB variables Xj in a condition set E. We iterate over all candidateMBs Xj (Steps 5-15) and condition sets E (Steps 6-14) and compute the score improvement foreach pair {Xj , E} (Step 16). In Steps 7-9 we add a check that if a variable Xj is already presentin any subset or super set D of E, we do not include it as a candidate. If the current conditionset under consideration has more than one conditions, data from these conditions is pooled and 24
  25. 25. parameters for the new distribution P (Xi |M∗ ) is estimated using the pooled dataset (Steps 10- Ei12). A candidate move for a variable Xi is composed of a pair {Xj , E } with the maximal scoreimprovement over all variables and conditions (Step 16). After all candidate moves have beenidentified, we attempt all the moves in the order of decreasing score improvement (Step 18). Eachmove adds the edge {Xi , Xj } in condition set E . However, if either Xi or Xj was already updatedin a previous move, we ignore the move. Because not all candidate moves are made, by sorting themove order in decreasing score improvement, we enable moves with the highest score improvementsto be attempted first. The algorithm converges when no edge addition improves the score of the kgraphs.Algorithm 1 NIPD 1: Input: Random variable set, X = {X1 , · · · , X|X| } Set of conditions C Datasets of RV joint assignments, {D1 , · · · , D|C| } maximum neighborhood size, kmax 2: Output: Inferred graphs G1 , · · · , G|C| 3: while Score(G1 , · · · , G|C| ) does not stabilize do 4: for Xi ∈ X do {/*Propose moves*/ } 5: for Xj ∈ (X {Xi }) do 6: for E ∈ powerset(C) do 7: if Xj ∈ M∗ , s.t either D ⊂ E or E ⊂ D then iD 8: Skip Xj . 9: end if10: if |E| > 1 then11: Estimate parameters for new conditional P (Xi |M∗ Ei ∪ {Xj }) using pooled dataset DE obtained from merging all De s.t. e ∈ E.12: end if13: compute ∆Score{Xi Xj }E .14: end for15: end for16: Store {Xi , Xj , E } as candidate move for Xi , where {Xj , E } = arg max ∆Score{Xi Xj }E j,E17: end for18: Make candidate moves {Xi , Xj , E } in order of decreasing score improvement /*Attempt moves to modify graph structures*/19: end while 25
  26. 26. =>?=+>@ #* / E>FG >EG9F +843.7/976./:!;<03. #) #( #" #! / !"# $$" %"# %%$ &# +,-./01/234,5,56/7424 BCD+>@ #" / E>FG >EG9F+843.7/976./:!;<03. #!" #! #A" #A #$" / !"# $$" %"# %%$ &# +,-./01/234,5,56/7424 Figure 1: Shared edges in the HIGHSIM and LOWSIM networks METHOD POPULATION EDGE-CNT SHARED EDGE-CNT QUIESCENT 378 NIPD 271 NON-QUIESCENT 402 QUIESCENT 171 INDEP 25 NON-QUIESCENT 200 Table 1: Structure of the inferred networks using INDEP and NIPD. 26

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