Volume 13 Supplement 16
Knowledge-based analysis of proteomics data
© Bessarabova et al.; licensee BioMed Central Ltd. 2012
Published: 5 November 2012
As it is the case with any OMICs technology, the value of proteomics data is defined by the degree of its functional interpretation in the context of phenotype. Functional analysis of proteomics profiles is inherently complex, as each of hundreds of detected proteins can belong to dozens of pathways, be connected in different context-specific groups by protein interactions and regulated by a variety of one-step and remote regulators. Knowledge-based approach deals with this complexity by creating a structured database of protein interactions, pathways and protein-disease associations from experimental literature and a set of statistical tools to compare the proteomics profiles with this rich source of accumulated knowledge. Here we describe the main methods of ontology enrichment, interactome topology and network analysis applied on a comprehensive, manually curated and semantically consistent knowledge source MetaBase and demonstrate several case studies in different disease areas.
The analytical methods in proteomics can be roughly divided onto statistical and "functional" approaches. Some statistical methods are applied for identification and quantization of proteins in complex individual peptide profiles. Another set of tools (machine learning and classification algorithms ) helps to evaluate performance of selected proteins as descriptors in multi-sample studies, for instance as prognostic biomarkers in disease or drug response diagnostics. Technically, neither of these applications requires any knowledge of the protein relevance to disease pathology. Statistical analysis alone could be sufficient for most clinical applications of proteomics-derived biomarkers - provided their high enough performance on large populations (accuracy, sensitivity and specificity).
However, human (and mammalian in general) biology is too complex to be handled by statistical approaches alone. For one (just it is the case with other OMICs technologies like global gene expression or SNP genotyping), proteomics profiles in different samples are highly heterogeneous for the same clinical phenotype (e.g., disease survival rate or drug response). The best performing clinical protein biomarker, PSA, is elevated in less than 50% of prostate cancer patients, and variability of individual proteins in multi-variant proteomics profiles is usually substantially higher. Secondly, protein profiles derived either from bodily fluids or solid tissues biopsies are not sufficiently selective for most phenotypic endpoints and its statistical association with different diseases and drug responses can be misleading. For example, almost any of >5,000 human diseases is associated with inflammation. However, statistically processed proteomics profiles usually do not allow to distinguish between them. In order to deconvolute these complexities, one needs tools and databases for functional, or "pathway", analysis. Functional analysis utilizes accumulated knowledge about relationships between proteins in a living cell to interpret the experimental data instead of relying on data only.
There are two important technical aspects for biological interpretation of proteomics data. First, proteomics is "high-throughput", or OMICs, technology, meaning that the outcome of proteomics experiments is a list of proteins differentially modified or abundant in a certain phenotype. The mere size of proteomics datasets requires specialized analytical tools, which deal with large lists of objects, rather than individual proteins, one at a time. Second, proteomics profiles are usually "global" in terms of sample source, i.e. they represent snapshots of a whole blood profile or tissue biopsies, which defines a very complex temporal, cell type- and tissue-specific biological context for protein activity. On average, a human protein has over 20 physical interactions of different types with other proteins, nucleic acids and metabolites and participates in dozens of biological pathways and processes (MetaCore database, Thomson Reuters). Furthermore, alterations in the same protein/gene (mutations, epigenetic changes, RNA splice variants, phosphorylated proteins, isoforms etc.) can be associated with dozens of diseases and conditions. In most cases, the mechanisms of such associations are unknown. On the other hand, there are a huge number of facts and findings about different aspects of functionality of these proteins in different tissues, cell lines and conditions, scattered in hundreds of thousands of experimental articles. What is the value and relevance of this "accumulated knowledge" for the analysis of an individual proteomic profile and how could it be applied in meaningful way?
The first step is to assemble all relevant published data in a computer-readable form, then index and structure this content to make it accessible for automated search and analysis applications. Over the last decade, a variety of text mining algorithms and manual annotation techniques have been developed to extract primary and meta-data from experimental literature, patents and other written sources (MedScan by Ariadne Genomics ; I2E by Linguamatics ). In addition, several large-scale annotation or editorial projects in the public domain have been initiated, monitored and completed by the industry, and have lead to comprehensive "knowledge" databases. These knowledge bases include MetaCore (Thomson Reuters), IPA (Ingenuity), KEGG  and HPRD , to name a few. The main types of data stored in these databases deal with protein functionality represented as physical and functional protein interactions of different types (often assembled into multi-step pathways) and gene/protein - phenotype associations linking genes and protein variants to the diseases, toxic effects, drug responses and other "end points". Manually curated "knowledge" databases have rich semantics in a form of functional ontologies and controlled vocabularies of terms and synonyms. Genes, proteins, metabolic compounds and drugs are assigned to different entities, or terms, in multiple ontologies, for instance cellular processes or standardized protein functions as it is done by GeneOntology consortium . Sub-categorization of proteins and genes into ontologies and representation of protein functionality as binary interactions (please see "network analysis tools" section for a definition on an interaction) and multi-step pathways are the two pre-requisites needed for applying tools of functional, or "knowledge-based" analysis.
Functional analysis of proteomics data can be divided into two types, dealing with proteins as objects and protein interactions, correspondingly. The first one, known as "ontology enrichment analysis" shows how different ontology terms (pathways, processes, disease biomarkers etc.) are relatively represented in the proteomics profiles (i.e. lists, or sets of proteins revealed by proteomics experiments) . The second type of analysis evaluates protein's functionality represented as silos of its interactions with the proteins on the list of interest. The core assumption is that relative connectivity of a protein reflects its functional importance for the phenotype . Relative connectivity can be calculated as a number of interactions between the given protein with the proteins on the list of interest normalized to the number of interactions it has with all proteins. Relative connectivity with a given protein list of interest can be calculated for every protein in the organism's "proteome" (human proteome is defined as about 24,000 proteins with experimentally determined function) (interactome tools) and for the subsets of proteins and interactions represented as networks.
Here, we present the main statistical tools for enrichment and interactions-based network analysis applied in the MetaCore/MetaDrug analysis platform (Thomson Reuters) with demonstrated examples of proteomics studies analyzed with the system.
Enrichment analysis in functional ontologies
Ontology enrichment is the most ubiquitous type of functional analysis, which evaluates relative representation of biological functions, or ontology terms, such as pathways and cell processes, for the proteomics profile of interest. Enrichment analysis (EA) consists of "mapping" (matching identifiers) of experimental data (proteomics list or profiles) onto terms of functional ontologies (pathways, disease biomarkers etc.) followed by ranking the resulting ontology terms based on the size of the identifier's (ID) intersection between the term and the experimental data. "Enrichment" is thereby calculated as a probability of the observed overlap between the genes/proteins from the experiment and the selected ontology term. There are two main types of enrichment analysis algorithms. One, a "whole set" approach, ranks proteins by evidence of differential abundance only (without a decision of abundance cut-offs). Another set of algorithms requires a pre-calculated set of proteins, usually selected by abundance fold change and statistical significance thresholds. The former "whole set" approach is implemented in Gene Set Enrichment Analysis (GSEA)  or Parametric Analysis of Gene Set Enrichment (PAGE)  algorithms. Gene set approach is realized in several algorithms, such as hypergeometric test . In the whole data set approaches, the pathways and other ontology terms are ranked according to their association with the protein or gene expression changes between two sample groups for every protein/gene in the set. On the contrary, gene (protein) list based algorithms work only with a subset of proteins and, therefore, require pre-selection of significance threshold for expression change at the protein level. The algorithms then use only information about protein content of the list, regardless of protein expression values. A list of differentially abundant proteins identified by t-test between two groups of samples in the whole data set is an example of input data in this approach.
The hypergeometric test evaluates significance of an association between the two kinds of categorical classification for a set of objects (for example, presence of a protein in the list of interest and its belonging to a pathway or any other ontology term). In the case of enrichment analysis, the intersection between a protein list of interest and a list of proteins involved in a certain pathway is calculated. Under the null hypothesis of no association, the probability of occurrence of an intersection of a given size by chance follows the hypergeometric distribution.
Calculation of an enrichment distribution of ontology terms
It is essential that these equations are invariant in terms of exchange of n for R, which means that the "subset" and "marked" are equivalent and symmetrical sets.
If the p-value is sufficiently small (conventionally, less than 0.05), the null hypothesis is rejected and the ontology term is called significantly enriched with the proteins of interest. The test is repeated for all terms in a given ontology, and all significant terms are returned, ordered by p-value of enrichment.
As ontologies typically contain many terms, some of them may turn out to be significant for particular list of interest and given p-value threshold just by chance. Thus, proper false discovery rate (FDR) control of enrichment analysis findings is required. In MetaCore, the FDR is controlled using Benjamini & Hochberg approach , which ensures that no more than 5% of significant terms are false positives.
Selection of the background list for enrichment analysis
The null hypothesis of the hypergeometric test can be viewed as 'competitive' null hypothesis . Comparing the intersection of those assesses the association of an ontology term with the node list of interest and the expected intersection of the same list with random node sets sampled from the same background 'universe' of nodes. Therefore, the test result strongly depends on the choice of the 'universe'.
Selection of an appropriate background 'universe' is often challenging in the high-throughput studies and may cause misleading results. The most conservative approach is to define the 'universe' as the complete set of genes or proteins measured by a particular high-throughput assay. For example, a subset of genes differentially expressed in breast cancer has to be tested for enrichment using the gene content of the microarray it was generated on, not the whole set of human genes. In proteomics, the background list can be defined as a complete set of proteins known to be expressed in an organ/tissue/body liquid/cell line of sample origin. This is important, as only a fraction (about 10%) of human protein-encoding genes are noticeably expressed in any given tissue , and only a subset of the expressed genes can be detected in a proteomics experiment. In MetaCore, the gene content of commercial microarrays, custom gene/protein sets, species and orthologs, cell processes and other functional groups can be selected as the background lists.
The background list is limited by the protein content of the ontology applied for enrichment; for instance, a non-redundant union of all human "canonical" pathways. Proper selection of the ontology is very important, as the enrichment p-values vary depending on the size of the examined protein list and the selection of the background. The most complete ontology of human canonical pathways (1200 pathway maps in MetaCore) has about 9,000 proteins, as compared with >24,000 human proteins with experimentally determined function which have at least one interaction each.
The ontology term "n" can represent a number of proteins selected based on some common property, for instance, belonging to the same ontology term and sharing the same annotation. Therefore, enrichment is only as informative as the ontology behind it. Analysis with only one ontology (e.g., GO processes) provides a "one dimensional" overview of a dataset. Ideally, the ontologies should be specifically designed for an application. A toxicoproteomics dataset should be evaluated against ontology of organ-specific histopathology and prostate cancer datasets against an annotated ontology of prostate cancer biomarkers and pathology pathways.
Ontologies for enrichment analysis
On average, human proteins have over 20 direct interactions and can participate in dozens of pathways, cellular processes and complexes, depending on context. Moreover, several "hub" proteins such as p53 and NF-kB, are much more ubiquitous and have over 1,000 interactions. In order to deal with such complexity, each protein has to be well functionally annotated, i.e. its function assigned to certain ontology terms by experimental evidence. This can be achieved by expert manual curation of full text experimental literature, a time consuming and tedious process. There are hundreds of biologically relevant ontologies available, although only some of them are sufficiently populated with proteins for enrichment analysis. Arguably, the best-known public ontologies are the ones developed by Gene Ontology (GO) consortium  for cellular processes, protein functions and cellular localizations. KEGG  is a popular public ontology and database of metabolic and signalling pathways from multiple organisms including human. In general, ontologies are not well standardized, they often apply poorly overlapping ID systems. Currently, an industry-academy incentive known as Pistoia consortium, intends to unify and standardize public ontologies . Commercial functional analysis platforms typically use a combination of publically available and proprietary ontologies and run enrichment analysis in each separately, one at a time. Below, we summarize the functional ontologies featured in the MetaCore database, where each ontology corresponds to a certain "dimension" of biological functionality.
Signaling pathways. Multi-step chains of consecutive signaling interactions, typically consisting of a ligand-receptor interaction, an intra-cellular signal transduction cascade between receptor (R) and transcription factor (TF) and, finally, TF - target gene interaction. Signaling pathways are mainly used by network generation tools and for enrichment by direct access to the database.
Metabolic pathways. Multi-step chains of metabolic reactions, linked into functionally self-sufficient linear chains and cycles. Fragments of metabolic pathways are shown as static images reachable from the protein pages. Metabolic pathways are also used for network generation and visualized on the networks.
Canonical pathways maps. Pathway maps, or wire diagrams, is the most popular ontology for enrichment and the main type of pathway visualization in MetaCore. Pathway maps are interactive images drawn in a proprietary Java-based editor and typically contain 3-6 pathways. There are over 1200 pathway maps in MetaCore, comprehensively covering human signaling and metabolism, selected diseases and some drug targets mechanisms.
Canonical pathway maps folders. All canonical maps are assembled into a hierarchical tree folder structure. The folders typically correspond to higher-level processes, such as "apoptosis", "cell cycle", or "amino acid metabolism". The folder structure can be visualized in a Browser mode and from enrichment analysis distributions.
Cell process network models. This ontology represents Thomson Reuters' reconstruction of main signaling and metabolic processes in the cell, such as a "cell cycle checkpoints" or "innate immune response". The manually built process networks typically have over 100 nodes (proteins) belonging to a certain normal cellular processes. The edges are selected from MetaCore content.
GO processes. These are a GUI-supported representation of the Gene Ontology (GO) collection of cellular processes, which is supported by GO tree structure and access to proteins and interactions within a process. This ontology is updated with GO standard updates. GO processes are mostly used in enrichment analysis and for prioritization of genes on the built networks.
GO molecular functions. A GUI-supported ontology of standard protein functions from GO. Mostly used in enrichment analysis.
Disease biomarkers. These are a collection of genes genetically linked to over 500 diseases and conditions, supported by the hierarchical disease tree and GUI for gene retrieval. Disease biomarkers are mostly used in enrichment analysis.
GeneGo disease network models. GeneGo reconstruction of disease mechanisms in a form of manually built networks. These are mechanistic networks linking the disease-associated genes via physical and functional protein interactions.
GeneGo toxicity networks. GeneGo reconstruction of toxicity mechanisms in a form of manually built networks. These are mechanistic networks linking genes associated with a particular toxicity endpoint via physical and functional protein interactions.
Examples of ontology enrichment in proteomics studies
Protein interaction - based analysis
Ontology enrichment is a "low resolution" analysis, which is useful for an overall description of functionality in the proteomic dataset and for functional focusing of the dataset by exporting proteins hitting a certain pathway or a process. However, relative distribution of pathways or other ontology terms cannot directly pinpoint the most important proteins on the pathway or in the dataset, i.e. ontology enrichment is not a self-sufficient hypothesis-generation tool. Ontology enrichment cannot rank individual proteins based on significance and answer questions such as "what is the most important protein kinase for my dataset?" or "what is the 'master switch' transcription factor to be knocked out in an animal model?" In order to answer such questions, one needs to consider the "local interactome" for each protein in the dataset and compare it with the global human interactome. The main assumption behind "knowledge-based" analysis is that it is the set of interactions, which defines protein functionality. One can identify the set of interactions between all 24,000 human proteins of known function with all the proteins in the dataset of interest and evaluate relative enrichment in interactions space for each human protein. The proteins which are statistically significantly "overconnected" with the given proteomics profile can be considered as the most important. Interactions enrichment can be carried out by two sets of tools, i.e. interactome and network analysis. The main difference between the two is that interactome methods calculate general enrichment of binary interactions around each object, and network methods apply rules (network building algorithms) for connecting binary interactions into multi-step modules.
A causative effect for the interaction (positive, negative)
A mechanism associated with this effect (binding, catalysis, transcriptional regulation etc.
Species (human, mouse or rat)
For each network objects, the information is derived from high quality experimental data. The collection of network objects and their relationships (edges) are provided in the underlying database.
In MetaCore, "interactome" tools evaluate relative connectivity of each protein in a dataset of interest with every other human protein (within dataset or entire "global" human interactome). Since proteins work in groups (complexes and pathways), which are defined by interactions, it is assumed that relative connectivity reflects relevance, or importance, of a protein for a given dataset. For example, if a transcription factor has significantly more interactions than expected by chance with its targets in a proteomics profile from a primary prostate tumour, it is likely to be an important "master regulator" of cancerogenesis in this particular tumour. Identification of the whole set of over-connected proteins can help to reconstruct the biological mechanism the proteomics profile. The interactome methods are well suited for deducing signalling and regulation proteins which activation would lead to a phenotype that are connected to the proteomics dataset but undetectable by proteomics profiling. Proteomics datasets are often enriched in "effector" proteins, such as metabolic enzymes or structure proteins which encoding genes are constitutively expressed in a given sample and presented in higher abundance. Such functional bias is evident when proteomics profiles are compared with gene expression profiles from the same sample. On the contrary, regulatory genes are usually expressed transiently; many signalling proteins tend to degrade fast and are regulated on post-translational level leaving this subset of proteins undetectable. At the same time, signalling pathways are quite important as the source of conditional "triggers" (for instance, GPCRs, signal transduction protein kinases) and are often prime candidates for drug target development. Two "topology" methods described below help to deduce proteins, which are usually not present in proteomics datasets but closely connected with them by protein-protein interactions of different mechanisms.
Calculation of protein connectivity in interactome analysis
Connectivity between proteins is carried out as follows: At least 2 protein populations are considered: 1) the proteins in the uploaded dataset (i.e. proteomics list) of interest (local interactome) and 2) the proteins in a background list (global interactome). The algorithm calculates the relative connectivity between the local interactome and compares it to the general interactome. First, one-step (interaction) neighbours are identified around individual proteins. Then, the procedure calculates the main properties of the "local" interactome for the proteomics dataset of interest. The "local" interactome is defined either as the compilation of all interactions between the genes/proteins within the uploaded list/experiment or as a set of all direct network neighbours of the proteins from the uploaded list and interactions between them. The interactome properties include:
Degree , the average number of protein interactions per protein from a given set . Since the interactions are directed, the nodes can be characterized by IN and OUT-degree, i.e. the average number of outgoing and incoming interactions.
Clustering coefficient. This captures the density of connectivity between the protein's neighbours . It is defined as: , where Ni is the number of interactions between the k i neighbours of node I. As K i (k i -1)/2 is the maximum number of such interactions, the clustering coefficient is a number between 0 and 1. The average clustering coefficient for a list of genes is obtained by averaging over the clustering coefficient of individual nodes. A network with a high clustering coefficient is characterized by highly connected sub-graphs.
Evaluation of one-step over (under)-connectivity between a protein with the protein list of interest
N is the number of proteins (protein-based network objects) in our global interactome extracted from MetaCore; n - number of proteins derived from the sets of genes of interest; R - the degree of a given protein in the global interactome database; r - the degree of a given protein within the set of interest.
The probability of observing under-connected proteins can be calculated by (1 - p), where p is p-value for over-connection.
Interactome overconnectivity analysis of serum in ovarian cancer.
Distribution of GO processes in glaucomatous optic nerve astrocytes revealed by proteomics
Top 10 GO processes
(for 35 proteins)
Top 10 canonical maps
(for 20 mapped proteins)
Complement activation. Alternative pathway
CDC42 in cellular processes
Response to heat
Alternative complement pathway
Putative ubiquitin pathway
Role of ASK1 under oxidative stress
Role of IAP proteins in apoptosis
DNA DSB repair via homologous recombination
Glucocorticoid receptor signaling
Role of Akt in hypoxia
Parkin disorder under Parkinson's disease
Innate immune response
Role of Parkin in the ubiquitin-proteosomal family
Small GTPase mediated signal transduction
"Hidden nodes" topology analysis: algorithm for node prioritization and reconstructing significant pathway modules
Analysis of relative connectivity described above is limited to one-step interactions which define the local interaction "neighbourhood" for each protein. This topology evaluation procedure can be extended to encompass several steps of signalling mechanisms, eventually covering all human proteins with respect to the connectivity (expressed as p-values) within the proteomics dataset. We call this approach a "hidden nodes" analysis, as most of the highly ranked proteins revealed by this method miss from original proteomics data . As in the case of one-step interactome, the scoring is based on the role the protein nodes play in connectivity among genes or proteins of interest relative to their role in the global network. The method is neutral with respect to the node's degree or centrality, i.e., the role of nodes with a high degree of physical connections (such as p53 or NF-kB) is normalized on the entire interactome. The scores for truly significant nodes are enhanced, while the scores of those that appear in the networks by chance are reduced. The output of "hidden nodes" analysis is a set of prioritized proteins divided onto functions along with their possible regulatory effects on the proteins from a proteomics profile. A user receives a series of scored and testable hypotheses associating individual components of the identified molecular network(s) with the phenotype of interest.
Topological scoring of nodes
The null hypothesis of scoring test is that node i has no special role in connecting j with other nodes of interest via shortest paths. The p-value of the test is calculated as cumulative probability of observing 'Kij' or more paths by chance under the null hypothesis. We repeated this procedure for all nodes in K, calculating up to K p-values for each node i in the network of shortest paths connecting differentially expressed genes. Each of these p-values shows relevance of node i to individual members of the set K. As we want to identify the nodes that are statistically significant to at least one or more members of the experimental set, we define the "topological significance" score associated with node i as the minimum of the pij values. We note that our method, unlike betweenness centrality, does not count the actual number of shortest paths between the pairs of nodes, but rather it counts the number of instances a node is part of the shortest path network between the node pairs. More importantly, our technique considers fractions of differentially and non-differentially expressed genes connected by shortest paths containing the node that is being evaluated. In this context it is not concerned with the paths bypassing the node of interest. In contrast, the betweenness centrality measure is based on relative numbers of shortest paths going via the node of interest and those bypassing it.
For example, in Figure 5, the size of the global network N = 13, K = 7, and S = 5. The number of possible shortest path networks between node B and each of the other nodes in the global network, which can contain D, are 11 (N-2). The number of such networks which contain node D is 7 (NBD = 7). On the other hand, the number of shortest path networks containing D, among those connecting only nodes from the set K, is 5 (KBD = 5). The significance (p-value) for node D with respect to node B and set K can be calculated as pBD = p(N-2, NBD, K-1, KBD). Similarly, we can calculate the other p-values for D with respect to A, G, K, J, I, and L, and then pick the smallest value and assign it as the significance of node D in the sub-network defined by the nodes of interest (red nodes). The nodes can be classified as internal (F, D, C, H, and M), source (A, B, and L) and target (A, G, K, J, and I) nodes.
The highest scored topologically significant proteins from gene expression and proteomics data for androgen-stimulated LNCaP prostate cancer cells
Entrez Gene ID
aging -associated gene 11
ribosomal protein S6 kinase, 90kDa, polypeptide 3
catenin (cadherin -associated protein), beta 1, 88kDa
RAD54 -like 2
mitogen -responsive phosphoprotein
filamin A, alpha
fibroblast growth factor receptor 3
Entrez Gene ID
myc proto-oncogene protein
insulin receptor substrate 1
B-cell CLL/lymphoma 2
tight junction protein 2 (zona occludens 2)
interferon regulatory factor-1
sterol regulatory element binding transcription factor 1
ZNP-99 transcription factor
ets variant gene 3, ETS family transcriptional repressor
Network analysis of proteomics data
Network analysis is another method assessing connectivity within a given dataset such as a proteomics list. In MetaCore, networks are generated as a combination of binary single step interactions (edges or links), which connect network objects (nodes). In generating networks, a user's dataset (for instance, a proteomics profile) is considered as a list of root nodes (any protein, gene or metabolic ID) and MetaBase is used as the source of interactions as edges between them. As the root node lists are different, the generated networks are unique for the uploaded datasets and chosen conditions, which makes networks a quite flexible and precise research method. The same dataset (list of root IDs) can be connected by interactions in different ways, depending on a chosen network parameters and filters. The MetaCore's network toolbox features several network algorithms (each with a specific statistical method) and filters enabling generation of networks specific for cellular processes, species, orthologs, cellular processes, expression in human tissues, mechanisms of interactions and effects. The end nodes on the networks have only one edge; the internal nodes may have anywhere from two to several hundred edges.
Network generation algorithms
Direct interactions (DI) algorithm
Expand by One Interaction
This algorithm builds one-step sub-networks around any object from the list. The algorithm helps to identify direct upstream and direct downstream effectors, then finds "islands" of nodes from the user's list connected by no more than two bridging objects.
Shortest path (SP) algorithm
This is a less stringent algorithm than the DI as it allows for the addition of non-root nodes to the networks (objects from the database, not originally in the original user file). The SP algorithm works as follows: when there are two lists of nodes, one for the initial nodes, and another for end nodes, the lists are considered as almost always identical and corresponded to the input list. For every node from the initial list, the set of shortest paths (chains of consecutive directed interactions) to every other node from the 'end nodes list' is established. For every pair of nodes, all of the minimal paths are built and depicted as an inter-connected network of pathways. The number of steps defined by user options can limit the length of paths.
AE algorithm creates sub-networks around every object from the uploaded list. The expansion halts when the sub-networks intersect (whether it is 1-step or more). The objects that do not contribute to connecting sub-networks are automatically removed.
Analyze Network (AN)
This algorithm starts with building a super network by applying a simplified version of the "Auto Expand" algorithm to the initial list of objects. The network, which is never visualized as a whole, connects all objects from the input list with all other objects in the input list. Naturally, this process results in a super-connected large network, and is therefore "divided" into smaller fragments of a user-chosen size, from 2 to 100 nodes. The division process is conducted in a cyclical manner, i.e. fragments are created sequentially one by one where edges used in a fragment are never reused in subsequent fragments. Nodes may be reutilized, with different edges leading to them in different fragments. The end result of the AN algorithm is a list of multiple overlapping networks (usually ~30), which can be prioritized based on five parameters: the number of nodes from the input list among all nodes on the network, the number of canonical pathways on the network, and three statistical parameters: p-value, z-score and g-score (see network statistics below).
Analyze network' algorithm results for a plasma proteome from a mouse ovarian cancer model
Factor H, APOF, Cathepsin Z, TIG2, SSB-2
positive regulation of cell adhesion (15.6%; 1.020e-08), regulation of biological quality (44.4%; 1.296e-08), response to inorganic substance (22.2%; 7.552e-08), wound healing (17.8%; 2.420e-07), anatomical structure morphogenesis (35.6%; 3.159e-07)
Fetuin-A, IBP4, Thrombospondin 1, KNG, SERPINA3 (ACT)
response to wounding (39.6%; 8.503e-15), regulation of biological quality (54.2%; 2.556e-13), response to stimulus (72.9%; 3.053e-12), inflammatory response (29.2%; 3.365e-12), response to stress (52.1%; 1.913e-11)
Calgranulin A, Calgranulin B, VCAM1, Cathepsin D, NOTCH2
positive regulation of cellular process (67.4%; 1.338e-15), response to stress (62.8%; 5.497e-15), positive regulation of biological process (67.4%; 1.566e-14), multicellular organismal process (83.7%; 5.605e-14), developmental process (74.4%; 9.693e-14)
Thrombospondin 1, CNBP, AKR1C1, Coagulation factor XI, NOV
regulation of biological quality (58.8%; 1.894e-11), multicellular organismal process (79.4%; 8.590e-10), regulation of multicellular organismal process (44.1%; 1.560e-08), response to stress (50.0%; 7.234e-08), positive regulation of cellular component organization (23.5%; 1.432e-07)
Analyze network (Transcription Factors - TFs) and Analyze network (Receptors)
Both algorithms start with creating two lists of objects expanded from the initial list: the list of transcription factors and the list of receptors. Next, the algorithm calculates the shortest paths from the receptors to TFs. Then, the shortest paths are prioritized in a similar way. The first algorithm, AN (TFs), connects every TF with the closest receptor by all shortest paths and delivers one specific network per TF in the list. Similarly, the second algorithm AN (R ) delivers a network consisting of all the shortest paths from a receptor in the list to the closest TF; one network per receptor. Since all the edges, and therefore, paths are directional, the resulted networks are not reciprocal.
Every network built by an AN algorithm may be optionally enriched with the receptor's ligands and the TF's targets. The networks may be grouped, and merged within every group. Namely, if we are building one network for every transcription factor, then all such networks with the same receptors are grouped and merged within each group.
Transcription regulation (TR)
This algorithm starts with a small sub-network that consists of the initial list of objects plus all the "immediate transcription factors" for those initial objects, i.e. the objects that are linked to at least one of the initial objects by an edge of the "transcription regulation" type. Then, a separate network is built around every such transcription factor, using the AE algorithm with "upstream" option and limiting to the objects from the initial list. Then the transcription factor's targets from the initial list are added to network. The algorithm delivers a list of networks, one per transcription factor.
Model canonical pathways
Determine all objects from the input list that are involved in at least one canonical pathway;
For each selected object, retrieve all canonical pathways containing this object and merge them into a separate network (each network is built around a single root object from the input list;
Optionally, add objects from the input list that is regulated by transcriptional factor already present in network;
Optionally, add objects from the input list that binds to the receptor already present in network;
Rank networks by the number of input list objects they contain. Networks with equal number of such objects are ranked by size (the less total number of objects, the higher rank).
Prioritization of sub-networks
It is essential that these equations are invariant in terms of exchange of n for R. This means that the "subset" and "marked" are the equivalent and symmetrical sets.
N is the total number of nodes after filtration;
R is the number of nodes in the input list or the nodes associated with experimental data;
n is the number of the nodes in the network;
r is the number of the network's nodes associated with experimental data or included in the input list;
µ and σ are respectively, the mean and dispersion of the hypergeometric distribution described above.
Here, we described the basics of analysis of proteomics data by functional methods. Essentially, all functional analysis is divided to "protein-based" and "interaction-based" methods. The former consists of relative enrichment of a list of proteins (proteomics profile) with the terms of functional ontologies, such as pathways, cellular processes and disease biomarkers. This is a low resolution, descriptive analysis, helpful for the first look at the data and functional filtering of proteomics profiles. This type of analysis is useful for basic biology applications and relatively large protein sets; preferably over 200 proteins. Enrichment profiles can also be applied quantitatively, with one or several top scoring ontology terms representing a functional descriptor. Pathway descriptors can be applied for clinical sample clustering or prediction of disease prognosis or toxic effects (reviewed at ).
The interaction-based methods are based on the assumption that it is the set of physical interactions that defines the protein functionality in a living cell. Therefore, evaluation of the local "interactome" for each protein from proteomics datasets could be used as a flexible ad powerful research tool. High resolution interactome analysis is well applicable for drug target identification and biomarker discovery. Although technically there is no size limit, Interactome and network tools work best on relatively small protein sets and particularly useful when the sample size is too small for statistical analysis of sufficient power. Deducing companion biomarkers for drug response in clinical trials is a typical application. In most clinical study settings, drug sensitivity and resistance biomarkers have to be identified from a limited number of Phase I or II samples (or pre-clinical in vitro assays) and validated in much larger and more expensive Phase III studies. A small sample size often makes statistical tools for "gene signature" calculations irrelevant, and researchers have to rely on interactome-based methods, such as "causal reasoning" .
We are grateful to the reviewers for comments.
This article has been published as part of BMC Bioinformatics Volume 13 Supplement 16, 2012: Statistical mass spectrometry-based proteomics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/13/S16.
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