# A sampling framework for incorporating quantitative mass spectrometry data in protein interaction analysis

- George Tucker
^{1}, - Po-Ru Loh
^{1}and - Bonnie Berger
^{1}Email author

**14**:299

https://doi.org/10.1186/1471-2105-14-299

© Tucker et al.; licensee BioMed Central Ltd. 2013

**Received: **18 May 2013

**Accepted: **14 September 2013

**Published: **4 October 2013

## Abstract

### Background

Comprehensive protein-protein interaction (PPI) maps are a powerful resource for uncovering the molecular basis of genetic interactions and providing mechanistic insights. Over the past decade, high-throughput experimental techniques have been developed to generate PPI maps at proteome scale, first using yeast two-hybrid approaches and more recently via affinity purification combined with mass spectrometry (AP-MS). Unfortunately, data from both protocols are prone to both high false positive and false negative rates. To address these issues, many methods have been developed to post-process raw PPI data. However, with few exceptions, these methods only analyze binary experimental data (in which each potential interaction tested is deemed either observed or unobserved), neglecting quantitative information available from AP-MS such as spectral counts.

### Results

We propose a novel method for incorporating quantitative information from AP-MS data into existing PPI inference methods that analyze binary interaction data. Our approach introduces a probabilistic framework that models the statistical noise inherent in observations of co-purifications. Using a sampling-based approach, we model the uncertainty of interactions with low spectral counts by generating an ensemble of possible alternative experimental outcomes. We then apply the existing method of choice to each alternative outcome and aggregate results over the ensemble. We validate our approach on three recent AP-MS data sets and demonstrate performance comparable to or better than state-of-the-art methods. Additionally, we provide an in-depth discussion comparing the theoretical bases of existing approaches and identify common aspects that may be key to their performance.

### Conclusions

Our sampling framework extends the existing body of work on PPI analysis using binary interaction data to apply to the richer quantitative data now commonly available through AP-MS assays. This framework is quite general, and many enhancements are likely possible. Fruitful future directions may include investigating more sophisticated schemes for converting spectral counts to probabilities and applying the framework to direct protein complex prediction methods.

## Background

*Saccharomyces cerevisiae*, initially using yeast two-hybrid screens (Y2H) [1, 2] and subsequently by affinity purification combined with mass spectrometry (AP-MS, Figure 1) [3, 4]. Similarly, high throughput approaches have been applied to comprehensively map the

*Drosophila melanogaster*interactome, initially using Y2H [5] and more recently by AP-MS [6]. With advances in experimental protocols and decreasing costs, medium-scale AP-MS studies have become ubiquitous in proteomics for targeted investigation of specific pathways or interactions. The PPI networks these analyses generate have provided exciting insights into biological pathways and protein complexes, e.g., with relevance to human disease [7]. However, raw AP-MS data includes many false positive and false negative interactions, which are serious confounding factors in their interpretation [8, 9].

*et al.*[6] reported significantly improved inference on the

*Drosophila melanogaster*interactome using a matrix model approach as compared to state-of-the-art spoke methods.

The existing literature on matrix approaches has almost exclusively considered only binary experimental data (i.e., data sets in which bait-prey interactions are deemed either observed or unobserved, with no additional information about propensity of proteins to interact). An exception is the HGSCore method [6], which to our knowledge is the first to use quantitative information from AP-MS experiments in the form of bait-prey spectral counts. In contrast, spoke models have successfully used quantitative information (e.g., spectral counts [10-14, 20] and MS1 intensity data [15]) to filter contaminants and assign confidence scores to interactions.

In this study, we propose a novel approach for incorporating quantitative interaction information into AP-MS PPI inference. Our approach aggregates scores over an ensemble of binary data sets that represents the quantitative data, capturing the uncertainty of interactions with low spectral counts. Importantly, the sampling-based framework we propose allows us to directly harness previous binary methods without modification, thus extending previous methods to use quantitative information. We validate our results on a large-scale PPI network and two medium-scale networks. Our approach improves all binary methods that we tested across a broad range of parameter values. In many cases, the improved performance is comparable to or better than state-of-the-art methods that have been developed to leverage spectral counts. Additionally, in the Discussion we characterize previous approaches and identify a common mathematical framework that several successful approaches have used, providing insights that may be valuable in continuing to refine PPI inference techniques.

## Results

### Sampling framework

Explicitly, our framework takes as input a matrix of spectral counts (*n*_{
i
j
}), where columns correspond to purification experiments and rows to prey proteins. We convert a spectral count of *n* to the probability 1 - (1 - *p*)^{
n
}, where *p* is a user-defined parameter representing the probability that a single spectral count is the result of a true observation, and we view the *n* observed spectral counts as arising independently. Using these probabilities, we generate binary data sets of the same size as the original spectral count input matrix by putting a 1 in each matrix cell independently with probability 1 - (1 - *p*)^{
n
}_{
i
j
}. The resulting distribution of alternative binary realizations of the spectral count matrix thus reflects the range of confidences in different bait-prey interactions, in contrast to the common approach of converting the spectral count matrix to a single binary matrix simply by replacing all positive spectral counts with 1s.

Given an ensemble of alternative binary realizations and an existing PPI scoring algorithm that operates on binary data, we apply the PPI scoring algorithm to each realization, in each case producing a score for every possible PPI. We then produce an aggregate score for every PPI by taking the mean of the ensemble of scores for that PPI, possibly after applying an appropriate transformation. (A slight subtlety can arise in aggregating scores because depending on the shape of the score distribution, taking the mean may not be robust. Among the algorithms we evaluated, we observed that the SAI score [21] could produce unbounded negative values, so we lower-bounded SAI scores at 0 before aggregation in order to prevent a single realization from having an extreme effect on the ensemble score.)

An additional consideration is the size of the ensemble required to produce stable results. In the tests we describe below, we ran 120 independent trials and found reasonable score separation between low, medium and high confidence interactions (Additional file 1: Figure S7). Then we further verified that increasing the ensemble size by a factor of four had a negligible impact on the results, indicating that 120 trials was sufficient to average out the stochasticity of the method. Although the minimum number of trials required will vary with the specific data set, our experiments suggest that in general, such a number of trials should sufficiently explore the space of binary realizations without presenting a computational burden, especially because the ensemble computations can be easily parallelized.

### Validation on three AP-MS data sets

We benchmarked our method by producing predictions from three AP-MS data sets: the recently published Drosophila Protein interaction Map (DPiM) [6] which includes over 3000 baits, a medium-scale human data set (TIP49) with 27 baits [10], and a *Drosophila* study focusing on the MAPK pathway with 21 baits [14]. On each evaluation data set, we applied our sampling framework to three previously published binary matrix methods for PPI inference: Hart et al. [16], PE [9], and SAI [21]. Each method produced a ranked list of interactions.

A standard approach to evaluating inferred interactions is to compare predictions with a high-confidence gold standard set. However, such a reference is challenging to construct. Few large-scale databases are available, and even the largest are understood to be incomplete and include false positive interactions. In light of these concerns, we follow the validation strategy used in Guruharsha et al. [6] of considering the overlaps between multiple curated data sets, obtaining subsets of PPIs with increasingly stringent thresholds on the number of supporting sources. The idea is that we can have high confidence in interactions supported by multiple lines of medium-confidence evidence, reducing the false positive rate in the gold standard data set (with the caveat that this approach may be biased toward well-studied pathways). We applied this procedure to create validation data sets from the Drosophila Interactions Database (DroID) [22] for *Drosophila* PPI predictions and BioGRID [23] for human PPI predictions. (See Methodsfor details.)

*p*that represents the reliability of a single peptide observation. We suggest a default value of

*p*= 0.3, but the performance improvements obtained using our sampling framework are robust across a wide range of values of

*p*(Figure 6; Additional file 1: Figures S4, S5) and for different confidence cutoffs (Additional file 1: Figure S6).

## Discussion

The literature of published methods for PPI inference from AP-MS data is substantial, and in continuing to develop methodological improvements, it is valuable to understand the similarities and differences among existing approaches and identify key ideas.

### Characterization of methods

Broadly speaking, methods can be broken down into two classes of models—spoke and matrix models—and by their scoring method. Spoke models make predictions solely on bait-prey interactions, while matrix models infer prey-prey interactions as well. Because prey-prey relationships are never directly observed, matrix models use the co-occurrence of pairs of proteins over multiple experiments to make inferences. Methods can also be characterized by their scoring functions, which generally fall into two classes: evidence-based scoring and null model-based scoring. In evidence-based scoring, models are built that estimate the likelihoods of observations under interacting and non-interacting pairs. Typically, a log likelihood ratio is then summed across experiments, implicitly assuming independence. Evidence-based scoring approaches, such as the PE [9] and C2S [18] scores, can easily combine direct bait-prey observations and prey-prey observations in the same model. However, because likelihood models for interacting and for non-interacting pairs must be constructed, these scores tend to have more tuning parameters that must be estimated from scarce gold standard validation data. In null model based approaches, such as Hart et al. [16], HGSCore [6], and SAI [21], a model for non-interacting pairs is assumed and fit from the data. This forms an empirical null distribution under which observations can be scored. The advantage of such an approach is that only the null distribution has to be tuned, so in many cases tuning with gold standard validation sets is unnecessary.

An additional consideration for any method that combines spoke and matrix information is the balance between information from direct bait-prey observations and prey-prey co-occurrences. These sources of information are clearly distinct, so the weighting between the two must be carefully calibrated, potentially requiring gold standard validation data. Proper calibration is critical to performance and may explain why Hart et al. and HGSCore, which seemingly sub-optimally ignore spoke information, perform significantly better on our tests than SAI [21], which uses both spoke and matrix information.

For experiments with a handful of baits, we expect that methods relying on spoke information will have the best performance because matrix methods rely on analyzing co-occurrences of pairs of proteins across many experiments. However, even for the medium-scale experiments that we analyzed, methods that rely solely on matrix information performed competitively with methods that used spoke information. We foresee that as experiment sizes grow, matrix relationships will be increasingly informative, so it will be crucial to consider both spoke and matrix information. Although our approach is applicable to any binary method, in our experiments, we found that for nearly all experiments PE was the top performer amongst the binary methods. In addition, because PE uses spoke and matrix information, we recommend using it in our framework.

### Low rank plus sparse matrix framework

*A*and

*B*is well approximated using a Poisson cumulative distribution function (CDF), taking the form

where *X*_{
A
B
} is the number of experiments that protein *A* and protein *B* co-purify in, *N*_{
A
} (resp. *N*_{
B
}) is the number of co-purifying pairs that protein *A* (resp. *B*) is observed in, and *N* is the total number of co-purifying pairs.

In the above form, *λ* factors as a rank-1 matrix, so that the method can be seen as modeling the co-occurrence matrix *X*_{
A
B
}as the sum of a rank-1 “background” matrix (blurred by Poisson noise) and a sparse matrix indicating true interactions. Notably, *X*_{
A
B
}ignores quantitative information, simply counting experiments in which proteins were co-purified. HGSCore [6] is an extension of the Hart et al. score that incorporates spectral count information through a transformation of the spectral counts (instead of directly using the co-occurrence matrix) and then analyzes the pseudo co-occurrence matrix in a similar manner. For the same reasons as above, we can view HGSCore as a rank-1 null model plus sparse true interactions, where the rank-1 component is estimated from a transformation of the spectral count data.

Similarly, SAINT [13] uses a probabilistic formulation to decompose a matrix of observed counts as a sum of: a rank-1 matrix, a sparse true interaction matrix, and generalized Poisson noise. Interestingly, SAINT decomposes the matrix of spectral counts—as opposed to co-occurrences—and has an entirely different justification for using a low rank model. Hart et al. and HGSCore assume that interaction partners are chosen at random in the null model, which gives rise to a low rank structure in the co-occurrence observations. Alternatively, SAINT assumes that contaminant proteins produce similar spectral counts across all bait experiments, which gives rise to a low rank structure in the spectral count observations. SAINT uses solely spoke evidence while Hart et al. and HGSCore use only co-occurrence evidence, suggesting that some combination of these approaches under a common framework may be an interesting direction for future investigation.

### Moving toward complexes

As protein biology is ultimately driven by the interactions of protein complexes—not just pairwise protein interactions—recent work has begun inferring protein complexes directly from AP-MS data [10, 24-28]. Traditionally, methods have first inferred PPIs and then clustered proteins into complexes (e.g., Guruharsha et al. [6]); however, information may be lost in this two-step procedure that first post-processes the data into high-confidence pairwise interactions. As with matrix models, some recent methods that bypass this first step have considered only binary experimental data [24, 25], whereas others have successfully used spectral count information [10, 26-28]. A similar sampling approach could be used to extend methods that consider only binary data to leverage spectral counts.

## Conclusions

As large-scale AP-MS experiments have become more common, an opportunity to leverage indirect co-occurrence information for PPI inference has arisen. Our sampling framework harnesses existing matrix methods for PPI inference that could previously only be applied to binary interaction data, achieving robust improvements across a range of data sets and enabling comparable or better performance versus current state-of-the-art methods. This framework extends the existing body of work on binary interaction analysis to apply to richer spectral count data now commonly available. Moreover, it is sufficiently general to have potential for future application in related protein interaction inference studies.

## Methods

### AP-MS data sets

The main data set we analyzed, DPiM, is a large-scale AP-MS study of the *Drosophila* proteome with 3485 experiments, which collectively pulled down 4927 distinct proteins ([6], Table S1). The DPiM data set is unique among publicly available AP-MS data sets because of its large size, which gives us confidence that the results we observed are not the result of random noise or overfitting. We also tested our approach on two medium-scale AP-MS data sets. One is another *Drosophila* study that focused on the MAPK pathway [14]; this data set contained 63 experiments, which collectively pulled down 1078 distinct proteins and included 9 control experiments. The other is a human data set referred to as TIP49 and originally published in Sardiu et al. [10]. We obtained the interaction data set, consisting of 35 experiments, which collectively pulled down 1207 distinct proteins and included 9 control experiments, from Choi et al. ([13], Table S1).

### Validation data sets

To validate *Drosophila* PPI inferences, we used the data sets in the DroID database [22]. We excluded the Perrimon co-AP complex and DPiM co-AP complex data sets to avoid contaminating our test sets with training data, leaving 7 other PPI data sets that we used in the above validation procedure. The validation set contained 58,657 interactions supported by at least one source, 3,310 interactions supported by at least two sources, 289 interactions supported by at least three sources, and 67 interactions supported by at least four sources.

To validate human PPI inferences, we used BioGRID v3.1.79 [23], which contains 40,680 interactions supported by at least one source, 11,054 interactions supported by at least two sources, 4,879 interactions supported by at least three sources, and 2,271 interactions supported by at least four sources.

### Implementation

We re-implemented the SAI [21], PE [9], Hart et al. [16], and HGSCore [6] methods; each is described in its reference but code is not provided. The PE score uses two parameters, *r*, representing the probability of detecting a true association in a purification experiment, and *n*_{pseudo}, the number of pseudocounts added for each prey. Since Collins et al. [9] estimates values of *r* = 0.51,0.62, and 0.265 on three example data sets and suggests using *n*_{pseudo} = 20,10, or 5, we set *r* = 0.3 and *n*_{pseudo} = 10. We downloaded and ran SAINT [13] with default parameters.

We also implemented the C2S score [18] but found its performance to be highly sensitive to the *tpr* (true positive rate) parameter; some values of *tpr*—including the default 0.6 in at least one of our tests—result in inferred values of the probabilistic parameters *r*_{
b
p
}and *r*_{
p
p
} that exceed 1, causing improper values in subsequent calculations (e.g., logarithms of negative numbers). We therefore excluded C2S from our analysis.

When we applied our sampling framework to data sets containing replicates, we treated columns corresponding to replicates independently. When we tested all of the methods on data sets containing controls, only SAINT, which explicitly models control data, used the controls.

## Availability of supporting data

The C++ code for our implementations is provided in Additional file 2.

## Declarations

### Acknowledgements

GT acknowledges support from National Human Genome Research Institute (NHGRI) Grant Number T32 HG002295. PL acknowledges support from the NSF Graduate Research Fellowship Program. BB acknowledges support from NIH R01 grant GM081871. We thank N. Perrimon and M. Kulkarni for providing the MAPK dataset and for helpful discussions.

## Authors’ Affiliations

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