IsoBayes (original) (raw)

Contents

Introduction

IsoBayes is a Bayesian method to perform inference on single protein isoforms. Our approach infers the presence/absence of protein isoforms, and also estimates their abundance; additionally, it provides a measure of the uncertainty of these estimates, via: i) the posterior probability that a protein isoform is present in the sample; ii) a posterior credible interval of its abundance.IsoBayes inputs liquid cromatography mass spectrometry (MS) data, and can work with both PSM counts, and intensities. When available, trascript isoform abundances (i.e., TPMs) are also incorporated: TPMs are used to formulate an informative prior for the respective protein isoform relative abundance. We further identify isoforms where the relative abundance of proteins and transcripts significantly differ. We use a two-layer latent variable approach to model two sources of uncertainty typical of MS data: i) peptides may be erroneously detected (even when absent); ii) many peptides are compatible with multiple protein isoforms. In the first layer, we sample the presence/absence of each peptide based on its estimated probability of being mistakenly detected, also known as PEP (i.e., posterior error probability). In the second layer, for peptides that were estimated as being present, we allocate their abundance across the protein isoforms they map to. These two steps allow us to recover the presence and abundance of each protein isoform.

Bioconductor installation

IsoBayes is available on Bioconductor and can be installed with the command:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("IsoBayes")

To access the R code used in the vignettes, type:

browseVignettes("IsoBayes")

Questions, issues and citation

Questions relative to IsoBayes should be reported as a new issue at BugReports.

To cite IsoBayes, type:

citation("IsoBayes")
## To cite DifferentialRegulation in publications use:
## 
##   Jordy Bollon, Michael R. Shortreed, Ben T. Jordan, Rachel Miller,
##   Erin Jeffery, Andrea Cavalli, Lloyd M. Smith, Colin Dewey, Gloria M.
##   Sheynkman, and Simone Tiberi (2024). IsoBayes: a Bayesian approach
##   for single-isoform proteomics inference. bioRxiv. URL
##   https://doi.org/10.1101/2024.06.10.598223
## 
## A BibTeX entry for LaTeX users is
## 
##   @Article{,
##     title = {IsoBayes: a Bayesian approach for single-isoform proteomics inference},
##     author = {Jordy Bollon and Michael R. Shortreed and Ben T. Jordan and Rachel Miller and Erin Jeffery and Andrea Cavalli and Lloyd M. Smith and Colin Dewey and Gloria M. Sheynkman and Simone Tiberi},
##     eprint = {https://doi.org/10.1101/2024.06.10.598223},
##     journal = {bioRxiv},
##     year = {2024},
##     doi = {10.1101/2024.06.10.598223},
##     url = {https://doi.org/10.1101/2024.06.10.598223},
##   }

Load the package

Load IsoBayes:

library(IsoBayes)

Key options

Input data

IsoBayes works with the output of both MetaMorpheus (MM) (Solntsev et al. 2018), or Percolator (The et al. 2016) (via the OpenMS toolkit (Röst et al. 2016)). Additionally, users can also provide MS data obtained from any bioinformatics tool, as long as the input files follow the structure mentioned in the Input user-provided data Section. In our benchmarks, we tested our model using both MetaMorpheus and Percolator data, and obtained slightly better results, and a shorter runtime with MetaMorpheus.

At the bottom of the vignette, in Section OpenMS and Metamorpheus pipeline we provide example scripts for both OpenMS and Metamorpheus.

In this tutorial, we use a small dataset created by MetaMorpheus. The code to generate the data can be found here.

PSM counts vs. intensities

We can run IsoBayes on either PSM counts or intensities. In our benchmarks, we found that, although results are consistent between the two types of input data, intensities led to a marginal improvement in performance.

TPMs (optional)

If available, transcriptomics data (from short of long reads), can also be integrated in the model in the form of TPMs; this will enhance protein isoform inference. To correctly match proteomics and transcriptomics data, transcript isoform and protein isoform names must be consistent. Here, we also specify the path to TPMs (optional). TPMs must be stored in a data.frame object (with columns isoname and tpm), or in a .tsv file (with headers isoname and tpm); in either case, there should be 1 row per isoform, and 2 columns:

We set the directory of the data (internal in the package):

data_dir = system.file("extdata", package = "IsoBayes")

We set the path (optional) to the TPMs:

tpm_path = paste0(data_dir, "/jurkat_isoform_kallisto.tsv")

PEP vs. FDR cutoff

Additionally, we suggest to provide the probability that peptides are erroneously detected (usually called PEP): IsoBayes will use the PEP to sample the presence/absence of each peptide; this will propagate the peptide identification uncertainty throughout the inference process. Alternatively, unreliable peptides can be discarded by specifying a peptide False Discovery Rate (FDR) threshold (also known as qvalue). We suggest using PEP with a weak FDR threshold of 0.1 (default parameters options). This is because peptides with FDR > 0.1 are usually unreliable, and associated to high error probabilities (e.g., PEP > 0.9). In our benchmarks, we found that using PEP (with and FDR threshold of 0.1) provides a (minor) increase in performace compared to the classical FDR thresholding (0.01 threshold), at the cost of a (marginally) higher runtime. If we want to disable the PEP integration, and filter based on the FDR, we can set PEP = FALSE and specify a more stringent FDR cutoff, e.g., as FDR_thd = 0.01.

Generate SummarizedExperiment object

Before running the IsoBayes model, we structure all the required input data into a SummarizedExperiment object. Such object can be created in multiple ways;

Input Percolator data

IsoBayes is also compatible with the PSM file from Percolator (from the OpenMS toolkit). The README shows how to use OpenMS tools to generate an idXML file containing PSM data. Once the file has been created, we can pass its path to input_data and set input_type as “openMS”.

SE = generate_SE(path_to_peptides_psm = "/path/to/file.idXML",
                 abundance_type = "psm",
                 input_type = "openMS")

Please note that when using data generated by Percolator, the algorithm can only process PSM counts and not intensities.

Input user provided-data

We can also input MS data obtained from any bioinformatics tool. The data can be organized in a .tsv file, a data.frame or a SummarizedExperiment.

.tsv or data.frame format

In both .tsv and data.frame files, each row corresponds to a peptide, and columns refer to:

Note that, when using user-provided data, argument path_to_peptides_intensities is not considered, because users are free to set column Y to PSM counts or intensities.

To load user-provided data, we just need to pass the file path, the data.frame or the SummarizedExperiment object and set input_type as “other” .

# X can be a path to a .tsv file or a data.frame
SE = generate_SE(path_to_peptides_psm = X,
                 abundance_type = "psm",
                 input_type = "other")

SummarizedExperiment format

If the data is already stored in a SummarizedExperiment, it can be passed to input_data function directly. The SummarizedExperiment should contain assays, colData and metadata with the structure specified below.

Object assays:

Object colData, a DataFrame object with columns:

Object metadata, a list of objects:

IMPORTANT: input_data does not filter peptides based on their FDR, since it assumes that unreliable peptides have been already discarded. Therefore, FDR filtering must be performed by users before the SE oject is provided to input_data.

Input and pre-process data

Once we have generated an SE object, we can process it, together with the TPMs (if available), via input_data. For this vignette, we use the SE object generated above loading the output from MetaMorpheus.

data_loaded = input_data(SE, path_to_tpm = tpm_path)
## After filtering petides, we will analyze:
## 7106 protein isoforms.
## Percentage of unique peptides:
## 46.49%

Inference

Once we have loaded the data, we can run the algorithm using the inference function.

set.seed(169612)
results = inference(data_loaded)

By default, IsoBayes uses one single core. For large datasets, to speed up the execution time, the number of cores can be increased via the n_cores argument.

Gene Normalization (optional)

In order to analyse alternative splicing within a specific gene, we may want to normalize the estimated relative abundances for each set of protein isoforms that maps to a gene. To this aim, we need to input a csv file containing two columns denoting the isoform name and the gene name. Alternatively, if isoform-gene ids are already loaded, a data.frame can be used directly. In both cases, the csv file or data.frame must have 2 columns: the 1st column must contain the isoform name/id, while the 2nd column has the gene name/id.

path_to_map_iso_gene = paste0(data_dir, "/map_iso_gene.csv")
set.seed(169612)
results_normalized = inference(data_loaded, map_iso_gene = path_to_map_iso_gene, traceplot = TRUE)

Specifying the map_iso_gene argument, also enables users to plot results via plot_relative_abundances function.

Visualizing results

Results are stored as a list with three data.frame objects: ‘isoform_results’, ‘normalized_isoform_results’ and ‘gene_abundance’. If ‘map_iso_gene’ was not provided, only ‘isoform_results’ is returned.

names(results_normalized)
## [1] "isoform_results"            "normalized_isoform_results"
## [3] "gene_abundance"             "MCMC"

Inside the ‘isoform_results’ data.frame, each row corresponds to a protein isoform. The columns report the following results:

head(results_normalized$isoform_results)
##    Gene   Isoform Prob_present Abundance CI_LB CI_UB           Pi     Pi_CI_LB
## 1 AAGAB AAGAB-201        0.644     1.262     0     3 1.584713e-05 2.383631e-11
## 2 AAGAB AAGAB-203        0.793     1.738     0     3 2.153024e-05 6.702643e-08
## 3  AAK1  AAK1-201        0.736     1.565     0     3 1.489832e-05 3.930209e-26
## 4  AAK1  AAK1-203        0.355     0.701     0     3 6.720064e-06 3.967865e-28
## 5  AAK1  AAK1-211        0.797     3.443     0     6 3.240565e-05 7.724298e-11
## 6  AAK1  AAK1-212        0.624     2.291     0     6 2.185897e-05 3.862298e-15
##       Pi_CI_UB       TPM     Log2_FC Prob_prot_inc
## 1 4.702063e-05 17.284100 -1.55230266         0.050
## 2 5.471843e-05 26.384500 -1.72040513         0.015
## 3 4.220247e-05  1.625010  1.76955056         0.702
## 4 3.228300e-05  0.632271  1.98278141         0.428
## 5 7.634762e-05 11.637600  0.05037318         0.485
## 6 6.822451e-05  7.264770  0.16215810         0.430

In ‘normalized_isoform_results’ data.frame, we report the protein isoform relative abundances, normalized within each gene (i.e., adding to 1 within a gene).

head(results_normalized$normalized_isoform_results)
##    Gene   Isoform    Pi_norm Pi_norm_CI_LB Pi_norm_CI_UB Pi_norm_TPM
## 1 AAGAB AAGAB-201 0.42417351  4.935733e-07     0.9559613  0.39580156
## 2 AAGAB AAGAB-203 0.57582649  4.403868e-02     0.9999995  0.60419844
## 3  AAK1  AAK1-201 0.19820016  3.737326e-22     0.5085673  0.07679758
## 4  AAK1  AAK1-203 0.08748612  6.632691e-24     0.4136955  0.02988097
## 5  AAK1  AAK1-211 0.42462808  1.506936e-06     0.8609503  0.54999017
## 6  AAK1  AAK1-212 0.28968564  9.926213e-11     0.8071473  0.34333128

In ‘gene_abundance’ data.frame, for each gene (row) we return:

head(results_normalized$gene_abundance)
##    Gene Abundance CI_LB CI_UB
## 1 AAGAB     3.000     3     3
## 2  AAK1     8.000     8     8
## 3  AAMP     8.991     9     9
## 4  AAR2     8.000     8     8
## 5 AARS1   119.000   119   119
## 6 AARS2     4.000     4     4

Finally, IsoBayes provides the plot_relative_abundances function to visualize protein-level and transcript-level relative abundances across the isoforms of a specific gene:

plot_relative_abundances(results_normalized, gene_id = "TUBB")

By default plot_relative_abundances normalizes the relative abundances within genes (again, adding to 1 within a gene). To disable the normalization, set thenormalize_gene argument to FALSE:

plot_relative_abundances(results_normalized, gene_id = "TUBB", normalize_gene = FALSE)

Note that plot_relative_abundances can be used only when, in the map_iso_gene argument of the inference function, we provide a csv file that maps the protein isoforms to the corresponding gene (path_to_map_iso_gene in this case).

Assessing convergence via traceplots

If inference function was run with parameter traceplot = TRUE, we can visualize the traceplot of the posterior chains of the relative abundances of each protein isoform (i.e., PI). We use the plot_traceplot function to plot the 3 isoforms of the gene TUBB:

plot_traceplot(results_normalized, "TUBB-205")
plot_traceplot(results_normalized, "TUBB-206")
plot_traceplot(results_normalized, "TUBB-208")

The vertical grey dashed line indicates the burn-in (the iterations on the left side of the burn-in are discarded in posterior analyses).

Note that, although we are using normalized results, gene-level information is ignored in the traceplot.

Session info

sessionInfo()
## R version 4.5.0 (2025-04-11)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] IsoBayes_1.7.1   BiocStyle_2.37.0
## 
## loaded via a namespace (and not attached):
##  [1] SummarizedExperiment_1.39.0 gtable_0.3.6               
##  [3] xfun_0.52                   bslib_0.9.0                
##  [5] ggplot2_3.5.2               Biobase_2.69.0             
##  [7] lattice_0.22-7              vctrs_0.6.5                
##  [9] tools_4.5.0                 generics_0.1.4             
## [11] stats4_4.5.0                parallel_4.5.0             
## [13] tibble_3.2.1                pkgconfig_2.0.3            
## [15] Matrix_1.7-3                data.table_1.17.4          
## [17] RColorBrewer_1.1-3          S4Vectors_0.47.0           
## [19] rngtools_1.5.2              HDInterval_0.2.4           
## [21] lifecycle_1.0.4             compiler_4.5.0             
## [23] farver_2.1.2                tinytex_0.57               
## [25] codetools_0.2-20            GenomeInfoDb_1.45.4        
## [27] htmltools_0.5.8.1           sass_0.4.10                
## [29] yaml_2.3.10                 pillar_1.10.2              
## [31] crayon_1.5.3                jquerylib_0.1.4            
## [33] DelayedArray_0.35.1         cachem_1.1.0               
## [35] magick_2.8.6                doRNG_1.8.6.2              
## [37] iterators_1.0.14            abind_1.4-8                
## [39] foreach_1.5.2               tidyselect_1.2.1           
## [41] digest_0.6.37               dplyr_1.1.4                
## [43] bookdown_0.43               labeling_0.4.3             
## [45] fastmap_1.2.0               grid_4.5.0                 
## [47] cli_3.6.5                   SparseArray_1.9.0          
## [49] magrittr_2.0.3              S4Arrays_1.9.1             
## [51] dichromat_2.0-0.1           withr_3.0.2                
## [53] UCSC.utils_1.5.0            scales_1.4.0               
## [55] rmarkdown_2.29              XVector_0.49.0             
## [57] httr_1.4.7                  matrixStats_1.5.0          
## [59] evaluate_1.0.3              knitr_1.50                 
## [61] GenomicRanges_1.61.0        IRanges_2.43.0             
## [63] doParallel_1.0.17           rlang_1.1.6                
## [65] Rcpp_1.0.14                 glue_1.8.0                 
## [67] BiocManager_1.30.25         BiocGenerics_0.55.0        
## [69] jsonlite_2.0.0              R6_2.6.1                   
## [71] MatrixGenerics_1.21.0

References

Röst, Hannes L, Timo Sachsenberg, Stephan Aiche, Chris Bielow, Hendrik Weisser, Fabian Aicheler, Sandro Andreotti, et al. 2016. “OpenMS: A Flexible Open-Source Software Platform for Mass Spectrometry Data Analysis.” Nature Methods 13 (9): 741–48.

Solntsev, Stefan K, Michael R Shortreed, Brian L Frey, and Lloyd M Smith. 2018. “Enhanced Global Post-Translational Modification Discovery with Metamorpheus.” Journal of Proteome Research 17 (5): 1844–51.

The, Matthew, Michael J MacCoss, William S Noble, and Lukas Käll. 2016. “Fast and Accurate Protein False Discovery Rates on Large-Scale Proteomics Data Sets with Percolator 3.0.” Journal of the American Society for Mass Spectrometry 27: 1719–27.