Co-elution proteomics refers to a family of high-throughput methods to map protein-protein interaction networks and their dynamics in cellular stimulation or differentiation. These methods, also referred to as co-migration, co-fractionation, or protein correlation profiling, involve separating interacting protein complexes on the basis of their diameter or biochemical properties. Protein-protein interactions can then be inferred for pairs of proteins with similar elution profiles. PrInCE implements a machine-learning approach to identify protein-protein interactions given a set of labelled examples, using features derived exclusively from the data. This allows PrInCE to infer high-quality protein interaction networks from raw proteomics data, without bias towards known interactions or functionally associated proteins, making PrInCE a unique computational resource for discovery. We provide an overview of the key functionalities of the PrInCE R package, and demonstrate an example of the analysis of data from a co-elution experiment investigating the response of the cytoplasmic interactome to Fas-mediated apoptosis.
Proteins are the central players of life at the molecular level. Yet cellular functions are rarely accomplished by single proteins acting in isolation. Instead, most biological processes are accomplished by the dynamic organization of proteins and other biological macromolecules, such as RNA and DNA, into networks of physical interactions. Systematic maps of these protein interaction networks can provide a “wiring diagram” to complement the “parts list” revealed by genome sequencing, placing each protein into a functional context. However, historically, protein interaction networks were mapped primarily using labour-intensive methods that involved tagging each protein for affinity purification, or heterologously expressing them in yeast. Besides being labour intensive, these approaches also yielded static pictures of cellular networks that offered little insight into how these networks are rewired by stimulation or in differentiation.
Recently, a family of proteomic approaches, variously referred to as co-elution, co-migration, co-fractionation, or protein correlation profiling, has been developed that allow high-throughput mapping of protein interaction networks in native cellular conditions (1–3). A subset of these even enable investigators to identify dynamic rearrangements in the protein-protein interactome in response to cellular stimulation (1, 4), or across in vivo samples, such as mouse tissues (5). The underlying principle that unifies different experimental protocols is to separate protein complexes into a number of fractions, on the basis of their size (diameter) or biochemical properties, and to perform quantitative proteomic analysis of the fractions. Proteins with similar “profiles” across fractions can be inferred to physically interact. However, because the number of potential pairs grows quadratically with the number of proteins quantified, and the number of potential complexes grows even faster, specialized bioinformatic approaches are required to infer protein interaction networks from the raw proteomics data.
PrInCE is an R package that uses a machine learning approach to infer protein-protein interaction networks at a user-defined level of precision from co-elution proteomics data. The input to PrInCE consists of a matrix derived from a co-elution proteomics experiment, with quantitations for each protein in each fraction (PrInCE can also handle more than one such matrix, in the case of biological replicates). PrInCE also requires a set of ‘gold standard’ protein complexes to learn from. It then calculates a series of features for each possible protein pair; importantly, these are derived directly from the data, without incorporating any external knowledge, a step that minimizes bias towards the rediscovery of known interactions (6). These features, and the accompanying gold standard, are used as input to the classifier, which learns to distinguish interacting and non-interacting pairs. A cross-validation procedure is then used to score every potential protein pair in the dataset, which are then ranked by their score in descending order, and the precision (defined as the ratio of true positives to true positives plus false positives) is calculated at every point in this ranked list. The user can then apply a precision threshold of their choice to this ranked list to infer the protein-protein interaction network from their experiment.
To demonstrate the use of PrInCE, we will work through a small example that is derived from a subset of the data presented in Scott et al., 2017 (4). In this paper, the authors mapped rearrangements in the cytoplasmic and membrane interactome during Fas-mediated apoptosis. Control and stimulated cytoplasmic and membrane interactomes were quantified in three replicates each, meaning the complete dataset consists of twelve replicates. In practice, each set of replicates would be analyzed together (for a total of four networks). However, such a complete analysis of the dataset would take over an hour, so for this vignette we focus on a single replicate. The replicate in question is the first cytoplasmic replicate from the Fas-stimulated condition, and is bundled with the PrInCE package; it can be loaded with the following command:
The dataset consists of ratiometric protein quantitations, achieved by SILAC (stable isotope labelling by amino acids in cell culture), for 1,560 proteins in 55 size exclusion chromatography (SEC) fractions:
##  1560 55
Each protein was quantified in at least one fraction; however, many measurements are missing:
## SEC_1 SEC_2 SEC_3 SEC_4 SEC_5 ## A0AVT1 NaN NaN NaN NaN NaN ## A0MZ66-5 NaN NaN NaN NaN NaN ## A3KN83 NaN NaN NaN NaN NaN ## A5YKK6 NaN NaN NaN NaN NaN ## A6NDG6 NaN NaN NaN NaN NaN ## A6NG79 NaN NaN NaN NaN NaN ## A6NHL2-2 NaN NaN NaN NaN NaN ## A6NHR9 NaN NaN NaN 2.5125 1.8817 ## A6NIH7 NaN NaN NaN NaN NaN ## A6NN80 0.22313 NaN NaN NaN NaN
This scenario is common in co-elution data: for example, a protein will be absent entirely from a given SEC fraction if it does not form a complex with a molecular weight in the mass range of that fraction.
To predict protein-protein interactions using PrInCE’s machine-learning approach, we also need two additional pieces of information to train the classifier: a set of true positive interactions, and a set of true negative interactions. In practice, we recommend providing a list of experimentally verified protein complexes: PrInCE assumes intra-complex interactions represent true positives, and inter-complex interactions represent true negatives. These can be obtained from a number of sources, such as the CORUM database (7), or our own previously reported custom subset of CORUM that removes complexes which may not remain intact under co-elution conditions (8). In the PrInCE R package, we provide a third option which is distributed under a CC-BY license, consisting of a list of 477 human protein complexes from the Complex Portal (9).
## $`5-hydroxytryptamine-3A/B receptor complex` ##  "O95264" "P46098" ## ## $`5-hydroxytryptamine-3A/C receptor complex` ##  "P46098" "Q8WXA8" ## ## $`5-hydroxytryptamine-3A/D receptor complex` ##  "P46098" "Q70Z44" ## ## $`5-hydroxytryptamine-3A/E receptor complex` ##  "P46098" "A5X5Y0" ## ## $`6-phosphofructokinase, M2L2 heterotetramer` ##  "P08237" "P17858" ## ## $`ACF complex` ##  "Q9NRL2" "O60264"
The main function of the PrInCE package,
PrInCE, provides an end-to-end
workflow for predicting protein-protein interaction networks from the
raw co-elution data.
Briefly, this function first filters proteins with too little information
to permit data analysis, then cleans the profiles for the remaining proteins
and fits a mixture of Gaussians to each cleaned profile.
PrInCE then calculates six features for each protein pair, from either the raw
profiles, the cleaned profiles, or the fitted Gaussian mixture models, and
concatenates features across replicates if more than one replicate was used.
These features are used as input to a machine learning model, along with the
set of ‘gold standard’ true positive and true negative interactions, which
uses a ten-fold cross-validation procedure to assign scores to each protein
Protein pairs are ranked by their classifier scores and the precision at each
point in the ranked list is calculated.
The entire list is returned to a user, who can select a precision threshold
that matches their needs.
Once we have loaded a co-elution matrix and list of gold standard protein complexes into R, inferring the protein-protein interaction network with PrInCE is therefore as simple as the following command:
# set the seed to ensure reproducible output set.seed(0) ## not evaluated PrInCE(scott, gold_standard)
However, this command is not evaluated in order to provide some information
on a further parameter that the
PrInCE function takes.
One of the six features that PrInCE uses to score protein-protein interactions
is derived from fitting a mixture of Gaussians to each protein’s elution
The process of Gaussian fitting also allows PrInCE to filter proteins with
poor-quality elution profiles (i.e., proteins for which a Gaussian mixture
could not be fit with an r2 value above some minimum, set to 0.5 by default).
However, the process of fitting Gaussian mixture models to thousands of
curves is one of the more computationally intensive steps in PrInCE and
PrInCE function can also take a pre-computed list of
fitted Gaussians, fit using the command
# set the seed to ensure reproducible output set.seed(0) ## not evaluated build_gaussians(scott)
In practice, the ability to provide pre-computed Gaussians can also save time when trying different parameters in PrInCE, such as different types of classifiers (described in greater detail in the following section).
We provide a list of fitted Gaussians for the
scott dataset in the
## List of 5 ## $ n_gaussians: int 3 ## $ R2 : num 0.95 ## $ iterations : num 1 ## $ coefs :List of 3 ## ..$ A : Named num [1:3] 2.9 1.59 1.21 ## .. ..- attr(*, "names")= chr [1:3] "A1" "A2" "A3" ## ..$ mu : Named num [1:3] 16.05 4.91 40.95 ## .. ..- attr(*, "names")= chr [1:3] "mu1" "mu2" "mu3" ## ..$ sigma: Named num [1:3] 6.74 3.11 3.32 ## .. ..- attr(*, "names")= chr [1:3] "sigma1" "sigma2" "sigma3" ## $ curveFit : num [1:55] 0.348 0.701 1.161 1.581 1.789 ...
We therefore run PrInCE using our precomputed Gaussian curves with the
following command, allowing PrInCE to print information about the status of
the analysis (
verbose = TRUE) and limiting the number of cross-validation
folds for the sake of time:
# set the seed to ensure reproducible output set.seed(0) # one-step analysis interactions <- PrInCE(scott, gold_standard, gaussians = scott_gaussians, cv_folds = 3, verbose = TRUE)
## generating features for replicate 1 ...
## fit mixtures of Gaussians to 970 of 1560 profiles
## concatenating features across replicates ...
## making labels ...
## training classifiers ...
## protein_A protein_B score label precision ## P12956_P13010 P12956 P13010 0.9977337 1 1.0 ## P36578_P40429 P36578 P40429 0.9966806 NA 1.0 ## P62258_Q04917 P62258 Q04917 0.9966630 NA 1.0 ## P36578_P46778 P36578 P46778 0.9966609 NA 1.0 ## P40429_P46778 P40429 P46778 0.9966256 NA 1.0 ## D3YTB1_P62424 D3YTB1 P62424 0.9966104 NA 1.0 ## P18124_P46778 P18124 P46778 0.9965548 NA 1.0 ## P25786_P28066 P25786 P28066 0.9964794 NA 1.0 ## P11940_Q08211 P11940 Q08211 0.9964540 0 0.5 ## P14868_P41252 P14868 P41252 0.9964416 NA 0.5 ## P43686_P62195 P43686 P62195 0.9964398 NA 0.5 ## O00231_Q9UNM6 O00231 Q9UNM6 0.9964243 NA 0.5 ## P05388_P36578 P05388 P36578 0.9964118 NA 0.5 ## E7EPB3_P05388 E7EPB3 P05388 0.9963531 NA 0.5 ## P05388_P40429 P05388 P40429 0.9962900 NA 0.5 ## C9J4Z3_P36578 C9J4Z3 P36578 0.9962752 NA 0.5 ## P62424_Q9Y3U8 P62424 Q9Y3U8 0.9962596 NA 0.5 ## P47914_P62913 P47914 P62913 0.9962394 NA 0.5 ## P54136_Q15046 P54136 Q15046 0.9962349 NA 0.5 ## P24534_P26641 P24534 P26641 0.9962061 NA 0.5 ## P62750_P84098 P62750 P84098 0.9961731 NA 0.5 ## P35998_Q13200 P35998 Q13200 0.9961714 NA 0.5 ## P13796_Q96KP4 P13796 Q96KP4 0.9961656 NA 0.5 ## P18124_P36578 P18124 P36578 0.9961590 NA 0.5 ## O15067_Q9UNZ2 O15067 Q9UNZ2 0.9961433 NA 0.5 ## P61254_P62241 P61254 P62241 0.9961293 NA 0.5 ## P07195_P63104 P07195 P63104 0.9961071 NA 0.5 ## C9J4Z3_D3YTB1 C9J4Z3 D3YTB1 0.9961065 NA 0.5 ## P04075_P31946 P04075 P31946 0.9960961 NA 0.5 ## D3YTB1_P36578 D3YTB1 P36578 0.9960820 NA 0.5 ## P49207_P62906 P49207 P62906 0.9960814 NA 0.5 ## C9J4Z3_P05388 C9J4Z3 P05388 0.9960643 NA 0.5 ## P46778_P61313 P46778 P61313 0.9960462 NA 0.5 ## P62249_P62263 P62249 P62263 0.9960311 NA 0.5 ## E7EPB3_P05387 E7EPB3 P05387 0.9960072 NA 0.5 ## D3YTB1_P47914 D3YTB1 P47914 0.9959959 NA 0.5 ## P13796_P22392-2 P13796 P22392-2 0.9959923 NA 0.5 ## P30520_Q9NY33 P30520 Q9NY33 0.9959870 NA 0.5 ## P05387_P05388 P05387 P05388 0.9959828 NA 0.5 ## P47914_P62424 P47914 P62424 0.9959807 NA 0.5 ## P18124_P40429 P18124 P40429 0.9959782 NA 0.5 ## P07814_P54136 P07814 P54136 0.9959722 NA 0.5 ## P40429_P62906 P40429 P62906 0.9959695 NA 0.5 ## P18124_P61313 P18124 P61313 0.9959678 NA 0.5 ## P05388_P46778 P05388 P46778 0.9959540 NA 0.5 ## D3YTB1_P05388 D3YTB1 P05388 0.9959445 NA 0.5 ## P08590_P60660-2 P08590 P60660-2 0.9959252 NA 0.5 ## P26373_P32969 P26373 P32969 0.9959108 NA 0.5 ## P13639_P27348 P13639 P27348 0.9959085 NA 0.5 ## P05387_P46778 P05387 P46778 0.9958969 NA 0.5
The columns in the output are as follows:
protein_A: the identifier of the first protein in the pair;
protein_B: the identifier of the second in the pair;
score: the score assigned to the protein pair by the classifier
label: if the protein pair is in the reference set, this value will be
1(for true positives) or
0(for true negatives); for all other pairs, the value is
precision: the precision at this point in the ranked list
Note that at the very top of the list, the precision is not defined if no true positives and no true negatives have yet been encountered.
In this toy example, the small size of our dataset and the small size of our gold-standard complexes mean that the precision curve is unstable below about 2,000 interactions:
precision <- interactions$precision[1:10000] plot(precision)
In most real examples, the precision curve shows a smoother decline.
For illustrative purposes, we here threshold the network at 50% precision
network <- threshold_precision(interactions, threshold = 0.5) nrow(network)
##  7607
This results in an unweighted protein-protein interaction network with 7607 interactions.
PrInCE function accepts a large number of arguments that were
omitted from the preceding discussion.
We have strived to set reasonable defaults for each of these parameters,
based on analyses that have involved much of the human co-elution proteomics
data in the public domain.
However, users may wish to change some of these defaults, based on the
properties of their dataset or the biological questions motivating their
Here, we provide an alternative workflow for analyzing the
in a step-by-step manner, and a discussion of some of the most important
build_gaussians function in PrInCE can be broken down into three steps.
First, profiles are preprocessed by basic filtering and cleaning operations.
Single missing values are imputed as the mean of their two neighboring points,
and profiles with fewer than five consecutive points are filtered from
Profiles are then cleaned by replacing missing values with near-zero noise,
and smoothed with a moving average filter.
Finally, mixtures of one to five Gaussians are fit to each profile using
nonlinear least squares, and model selection is performed to retain the best
mixture model for each curve.
Proteins that could not be fit with a mixture of Gaussians without an r2
value above some minimum are omitted.
This function takes the following parameters:
min_consecutive: the minimum number of consecutive points, after imputing single missing values, to retain a profile; defaults to
min_points: the minimum number of total points to retain a profile; defaults to
1so that only the number of consecutive points is used to filter profiles
FALSE, skip imputation of single missing values
FALSE, skip curve smoothing with the moving average filter
smooth_width: width of the moving average filter, in fractions; defaults to
max_gaussians: the maximum number of Gaussians with which to fit each profile; defaults to
criterion: the criterion used for model selection; defaults to
AICc, the corrected Akaike information criterion; other options are
BIC(Bayesian information criterion) or
AIC(Akaike information criterion)
max_iterations: the maximum number of iterations to use for curve fitting with random restarts
min_R_squared: the minimum r2 value to retain the fitted curve; defaults to
0.5. Profiles that cannot be fit by a mixture of Gaussians are assumed to be low-quality and excluded from further analysis by default.
method: method used to select initial conditions for
nls; can select either random parameters (
random) or make an educated guess based on the maximum values in the profile (
guess, the default)
filter_gaussians_variance_max: heuristics used to filter poor-quality Gaussian fits. If
filter_gaussians_centerwill remove Gaussians whose mean falls outside the bounds of the chromatogram.
filter_gaussians_heightcontrols the minimum height of the Gaussians, while
filter_gaussians_variance_maxcontrol the range of their standard deviation.
All of these parameters except the last four are exposed through the
As an example, we will re-analyze the
scott dataset with stricter filtering
criteria, requiring the presence of at least ten (non-imputed) data points
in addition to five consecutive points; fitting with a maximum of three
Gaussians, instead of five; and requiring a better fit than the default
For the sake of time, we allow only 10 iterations for the curve-fitting
algorithm here, and we elect to fit only the first 500 profiles.
data(scott) # set the seed to ensure reproducible output set.seed(0) # fit Gaussians gauss <- build_gaussians(scott[seq_len(500), ], min_points = 10, min_consecutive = 5, max_gaussians = 3, min_R_squared = 0.75, max_iterations = 10)
## .. fitting Gaussian mixture models to 255 profiles
# filter profiles that were not fit scott <- scott[names(gauss), ]
By default, the profile matrix is filtered to exclude proteins whose elution profiles could not be fit by a mixture of Gaussians prior to featurization.
Having fitted our co-elution profiles with Gaussians and filtered them
accordingly, the next step is to calculate features for each protein pair.
This is done using the
By default, PrInCE calculates six features from each pair of co-elution
profiles as input to the classifier, including conventional similarity metrics
but also several features specifically adapted to co-elution proteomics.
The complete set of features includes:
In addition to the profile matrix and list of fitted Gaussian mixtures, the
calculate_features function takes six parameters that enable the user to
enable or disable each of these six features (in order,
By default, all six are enabled.
Continuing our example, if we wanted the classifier to omit the Euclidean distance, we could disable this feature using the following command:
feat <- calculate_features(scott, gauss, euclidean_distance = FALSE) head(feat)
## protein_A protein_B cor_R_raw cor_R_cleaned cor_P co_peak co_apex ## 1 A0AVT1 B3KNT8 1.239943e+00 1.097955 0.504313944 11 10.405282 ## 2 B3KNT8 B4DQ14 1.264748e+00 1.262210 0.382046523 32 31.566148 ## 3 A0AVT1 B4DQJ8 2.851543e-06 1.018199 0.001520321 2 2.714134 ## 4 B3KNT8 B4DQJ8 1.045782e+00 1.110313 0.827969878 13 7.815383 ## 5 B4DQ14 B4DQJ8 3.928140e-01 1.064420 0.110395487 45 23.819433 ## 6 B3KNT8 B4DVY1 1.577634e+00 1.114307 0.001033478 27 9.043456
If we had multiple replicates, we would here concatenate them into a single
feature data frame using the
## not run # concatenate features from three different `scott` replicates feat1 <- calculate_features(scott1, gauss1) feat2 <- calculate_features(scott2, gauss2) feat3 <- calculate_features(scott3, gauss3) feat <- concatenate_features(list(feat1, feat2, feat3))
With our features in hand and a list of gold standard protein complexes, we can
now provide these to a machine-learning classifier to rank potential
This is accomplished using the
In order to score interactions that are also part of the reference set, PrInCE
uses a cross-validation strategy, randomly splitting the reference data into
ten folds, and using each split to score interactions in one of the folds
without including them in the training data.
For interactions that are not part of the training set, the median score over
all ten folds is returned.
In addition, to ensure that the results are not sensitive to the way in which
the dataset is split, PrInCE averages predictions over an ensemble of ten
classifiers, each with different cross-validation splits.
By default, PrInCE uses a naive Bayes classifier.
However, the PrInCE R package also implements three other types of classifiers:
support vector machine, random forest, and logistic regression.
In addition, PrInCE offers an option to ensemble results over multiple
different classifiers (sometimes called “heterogeneous classifier fusion”
In this option, cross-validation and ensembling is performed for all four
types of classifiers independently, then the ranks of each protein pair
across all four classifiers are averaged to return the final ranked list.
These options are controlled using the following parameters:
classifier: the type of classifier to use; one of
ensemble, corresponding to the options described above
models: the size of the ensemble for each classifier type, i.e., the number of models to train, each with a different train-test split
cv_folds: the number of folds to use in k-fold cross-validation
trees: for random forest and heterogeneous classifier fusion only, the number of trees in each RF model
Continuing our example, we will demonstrate the use of a support vector
machine to rank potential interactions (
classifier = "SVM").
For the sake of time, we use a single model (omitting ensembling;
models = 1)
and only three-fold cross-validation folds (
cv_folds = 3).
To use our list of protein complexes as a gold standard, we must first convert
it to an adjacency matrix; this is done using the helper function
adjacency_matrix_from_list (see also the related function
data(gold_standard) reference <- adjacency_matrix_from_list(gold_standard) # set the seed to ensure reproducible output set.seed(0) # predict interactions ppi <- predict_interactions(feat, reference, classifier = "SVM", models = 1, cv_folds = 3)
We can now plot the precision curve over the first 20,000 interactions:
precision <- ppi$precision[seq_len(2e4)] plot(precision)
Finally, we will likely want to keep only the set of high-confidence
interactions for further analysis, where “confidence” is quantified using
This is accomplished using the
For example, the following command constructs a protein-protein interaction
network at 70% precision:
net <- threshold_precision(ppi, threshold = 0.7) nrow(net)
##  4118
The core functionality of PrInCE involves the use of a machine-learning
framework to predict binary interactions from co-elution data, with discovery
of novel interactions being a primary goal.
However, PrInCE also implements one alternative to this analytical framework,
which asks whether statistically significant co-elution is observed for
known protein complexes.
This is achieved using a permutation-based approach, inspired by methods developed for another proteomic method for interactome profiling, thermal proximity co-aggregation (TPCA) (11). Briefly, given a list of known complexes, PrInCE calculates the median Pearson correlation between all pairs of complex members. (To reduce the effect of spurious correlations between proteins that are rarely observed in the same fractions, PrInCE requires a certain minimum number of paired observations to include any given correlation in this analysis—by default, 10 pairs). Then, PrInCE simulates a large number of complexes of equivalent size (by default, 100), and calculates the median Pearson correlation between pairs of random ‘complexes’. The resulting null distribution is used to assess the statistical significance of the observed co-elution profile at the protein complex level.
To identify complexes from the Complex Portal dataset that are significantly
co-eluting in this replicate, we first use PrInCE’s
# analyze cleaned profiles data(scott) filtered = filter_profiles(scott) chromatograms = clean_profiles(filtered)
filter_profiles function uses a permissive set of filters to discard
chromatograms that do not contain enough information to make inferences
about that protein’s interaction partners.
clean_profiles applies some simple preprocessing steps to the
By default, this function is applied to calculate Pearson correlations
during interaction prediction in PrInCE.
It imputes single missing values as the average of the two neighbors,
remaining missing values with near-zero noise, then passes a moving-average
filter over the chromatogram to smooth it.
We can now test for complex co-elution in the preprocessed chromatogram matrix
# detect significantly co-eluting complexes set.seed(0) z_scores = detect_complexes(chromatograms, gold_standard)
Complexes that could not be tested (that is, with fewer than three complex
members present in the elution matrix) are given
NA values, which we remove.
# remove complexes that could not be analyzed z_scores = na.omit(z_scores) # how many could be tested? length(z_scores)
##  23
# how many were significant at uncorrected, two-tailed p < 0.05? sum(z_scores > 1.96)
##  13
# print the top complexes head(sort(z_scores, decreasing = TRUE))
## COP9 signalosome variant 1 ## 9.083072 ## COP9 signalosome variant 2 ## 6.744865 ## CRD-mediated mRNA stability complex ## 5.806526 ## MCM complex ## 5.779922 ## Condensin I complex ## 4.568462 ## Embryonic stem cell-specific SWI/SNF ATP-dependent chromatin remodeling complex ## 4.256243
Of the 23 complexes that could be tested in this (unusually sparse) replicate, 13 were significant at an uncorrected, two-tailed p-value threshold of 0.05
As a second example, we can reanalyze another dataset bundled with the PrInCE R package. This dataset consists of a subset of the data presented by Kristensen et al., 2012 (1), who applied SEC-PCP-SILAC to monitor the interactome of HeLa cell lysates, then mapped interactome rearrangements induced by epidermal growth factor (EGF) stimulation. Three biological replicate experiments were performed, and in practice, all three replicates from each condition would be analyzed together. However, for the purposes of demonstrating the use of the PrInCE R package, we limit our analysis to the first replicate from the unstimulated condition.
We first load the data matrix and fitted Gaussians, provided with the PrInCE R package:
data("kristensen") data("kristensen_gaussians") dim(kristensen)
##  1875 48
##  1117
The co-elution matrix contains quantifications for 1,875 proteins across 48 SEC fractions. Mixtures of Gaussians were fit to 1,117 of these. For the sake of time, we subset this matrix further to the first 500 proteins:
kristensen <- kristensen[names(kristensen_gaussians), ] kristensen <- kristensen[seq_len(500), ] kristensen_gaussians <- kristensen_gaussians[rownames(kristensen)]
We also have to load our reference set of binary interactions or protein complexes, which in this case is derived from the Complex Portal human complexes.
data("gold_standard") head(gold_standard, 5)
## $`5-hydroxytryptamine-3A/B receptor complex` ##  "O95264" "P46098" ## ## $`5-hydroxytryptamine-3A/C receptor complex` ##  "P46098" "Q8WXA8" ## ## $`5-hydroxytryptamine-3A/D receptor complex` ##  "P46098" "Q70Z44" ## ## $`5-hydroxytryptamine-3A/E receptor complex` ##  "P46098" "A5X5Y0" ## ## $`6-phosphofructokinase, M2L2 heterotetramer` ##  "P08237" "P17858"
We can predict interactions in a single step using the main
here using a single model (instead of the default ensemble of ten) and five
cross-validation folds (instead of the default of ten) for time:
# set seed for reproducibility set.seed(0) # predict interactions interactions <- PrInCE(profiles = kristensen, gold_standard = gold_standard, gaussians = kristensen_gaussians, models = 1, cv_folds = 5)
Finally, we can subset our list of interactions to obtain set of high-confidence interactions for further analysis, using a relaxed precision cutoff of 50%.
network <- threshold_precision(interactions, 0.5) nrow(network)
##  1404
PrInCE predicts a total of 1,047 interactions at a precision of 50%.
## R Under development (unstable) (2022-10-25 r83175) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 22.04.1 LTS ## ## Matrix products: default ## BLAS: /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 ## ## locale: ##  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C ##  LC_TIME=en_GB LC_COLLATE=C ##  LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 ##  LC_PAPER=en_US.UTF-8 LC_NAME=C ##  LC_ADDRESS=C LC_TELEPHONE=C ##  LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: ##  stats graphics grDevices utils datasets methods base ## ## other attached packages: ##  PrInCE_1.15.0 BiocStyle_2.27.0 ## ## loaded via a namespace (and not attached): ##  Rdpack_2.4 DBI_1.1.3 deldir_1.0-6 ##  gridExtra_2.3 rlang_1.0.6 magrittr_2.0.3 ##  clue_0.3-62 tseries_0.10-52 compiler_4.3.0 ##  png_0.1-7 vctrs_0.5.0 quadprog_1.5-8 ##  stringr_1.4.1 ProtGenerics_1.31.0 crayon_1.5.2 ##  pkgconfig_2.0.3 fastmap_1.1.0 magick_2.7.3 ##  ellipsis_0.3.2 backports_1.4.1 utf8_1.2.2 ##  rmarkdown_2.17 preprocessCore_1.61.0 purrr_0.3.5 ##  xfun_0.34 zlibbioc_1.45.0 cachem_1.0.6 ##  jsonlite_1.8.3 progress_1.2.2 highr_0.9 ##  BiocParallel_1.33.0 jpeg_0.1-9 prettyunits_1.1.1 ##  parallel_4.3.0 cluster_2.1.4 R6_2.5.1 ##  bslib_0.4.0 stringi_1.7.8 RColorBrewer_1.1-3 ##  ranger_0.14.1 limma_3.55.0 rpart_4.1.19 ##  lmtest_0.9-40 jquerylib_0.1.4 Rcpp_1.0.9 ##  bookdown_0.29 assertthat_0.2.1 iterators_1.0.14 ##  knitr_1.40 zoo_1.8-11 base64enc_0.1-3 ##  IRanges_2.33.0 Matrix_1.5-1 splines_4.3.0 ##  nnet_7.3-18 tidyselect_1.2.0 rstudioapi_0.14 ##  yaml_2.3.6 timeDate_4021.106 doParallel_1.0.17 ##  codetools_0.2-18 affy_1.77.0 curl_4.3.3 ##  lattice_0.20-45 tibble_3.1.8 plyr_1.8.7 ##  quantmod_0.4.20 Biobase_2.59.0 urca_1.3-3 ##  evaluate_0.17 foreign_0.8-83 survival_3.4-0 ##  xts_0.12.2 pillar_1.8.1 affyio_1.69.0 ##  BiocManager_1.30.19 checkmate_2.1.0 foreach_1.5.2 ##  stats4_4.3.0 TTR_0.24.3 MSnbase_2.25.0 ##  MALDIquant_1.21 ncdf4_1.19 generics_0.1.3 ##  forecast_8.18 hms_1.1.2 S4Vectors_0.37.0 ##  ggplot2_3.3.6 munsell_0.5.0 scales_1.2.1 ##  glue_1.6.2 Hmisc_4.7-1 LiblineaR_2.10-12 ##  tools_4.3.0 speedglm_0.3-4 interp_1.1-3 ##  robustbase_0.95-0 mzID_1.37.0 data.table_1.14.4 ##  vsn_3.67.0 mzR_2.33.0 tester_0.1.7 ##  XML_3.99-0.12 grid_4.3.0 impute_1.73.0 ##  tidyr_1.2.1 rbibutils_2.2.9 latticeExtra_0.6-30 ##  MsCoreUtils_1.11.0 colorspace_2.0-3 nlme_3.1-160 ##  fracdiff_1.5-2 htmlTable_2.4.1 Formula_1.2-4 ##  cli_3.4.1 fansi_1.0.3 dplyr_1.0.10 ##  DEoptimR_1.0-11 pcaMethods_1.91.0 gtable_0.3.1 ##  sass_0.4.2 digest_0.6.30 BiocGenerics_0.45.0 ##  naivebayes_0.9.7 htmlwidgets_1.5.4 htmltools_0.5.3 ##  lifecycle_1.0.3 MASS_7.3-58.1
1. Kristensen AR, Gsponer J, Foster LJ (2012) A high-throughput approach for measuring temporal changes in the interactome. Nature Methods 9(9):907–909.
2. Havugimana PC, et al. (2012) A census of human soluble protein complexes. Cell 150(5):1068–1081.
3. Kirkwood KJ, Ahmad Y, Larance M, Lamond AI (2013) Characterisation of native protein complexes and protein isoform variation using size-fractionation based quantitative proteomics. Molecular & Cellular Proteomics:mcp–M113.
4. Scott NE, et al. (2017) Interactome disassembly during apoptosis occurs independent of caspase cleavage. Molecular Systems Biology 13(1):906.
5. Skinnider MA, et al. (2018) An atlas of protein-protein interactions across mammalian tissues. bioRxiv:351247.
6. Skinnider MA, Stacey RG, Foster LJ (2018) Genomic data integration systematically biases interactome mapping. PLoS Computational Biology 14(10):e1006474.
7. Giurgiu M, et al. (2018) CORUM: The comprehensive resource of mammalian protein complexes—2019. Nucleic Acids Research.
8. Stacey RG, Skinnider MA, Chik JH, Foster LJ (2018) Context-specific interactions in literature-curated protein interaction databases. BMC Genomics 19(1):758.
9. Meldal BH, et al. (2018) Complex portal 2018: Extended content and enhanced visualization tools for macromolecular complexes. Nucleic Acids Research.
10. Riniker S, Fechner N, Landrum GA (2013) Heterogeneous classifier fusion for ligand-based virtual screening: Or, how decision making by committee can be a good thing. Journal of chemical information and modeling 53(11):2829–2836.
11. Tan CSH, et al. (2018) Thermal proximity coaggregation for system-wide profiling of protein complex dynamics in cells. Science 359(6380):1170–1177.