| Title: | IsoBayes: Single Isoform protein inference Method via Bayesian Analyses |
|---|---|
| Description: | 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. |
| Authors: | Jordy Bollon [aut],
Simone Tiberi [aut, cre] |
| Maintainer: | Simone Tiberi <[email protected]> |
| License: | GPL-3 |
| Version: | 1.3.2 |
| Built: | 2024-07-28 03:03:31 UTC |
| Source: | https://github.com/bioc/IsoBayes |
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.
The DESCRIPTION file:
| Package: | IsoBayes |
| Type: | Package |
| Title: | IsoBayes: Single Isoform protein inference Method via Bayesian Analyses |
| Version: | 1.3.2 |
| Description: | 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. |
| Authors@R: | c(person(given = "Jordy", family = "Bollon", role = c("aut"), email = "[email protected]"), person(given = "Simone", family = "Tiberi", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0002-3054-9964"))) |
| biocViews: | StatisticalMethod, Bayesian, Proteomics, MassSpectrometry, AlternativeSplicing, Sequencing, RNASeq, GeneExpression, Genetics, Visualization, Software |
| License: | GPL-3 |
| Depends: | R (>= 4.3.0) |
| Imports: | methods, Rcpp, data.table, glue, stats, doParallel, parallel, doRNG, foreach, iterators, ggplot2,HDInterval, SummarizedExperiment, S4Vectors |
| LinkingTo: | Rcpp, RcppArmadillo |
| Suggests: | knitr, rmarkdown, testthat, BiocStyle |
| SystemRequirements: | C++17 |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.2.3 |
| ByteCompile: | true |
| URL: | https://github.com/SimoneTiberi/IsoBayes |
| BugReports: | https://github.com/SimoneTiberi/IsoBayes/issues |
| Repository: | https://bioc.r-universe.dev |
| RemoteUrl: | https://github.com/bioc/IsoBayes |
| RemoteRef: | HEAD |
| RemoteSha: | 53c46f5384336f80af1d2288cc5183f5acfeddb2 |
| Author: | Jordy Bollon [aut], Simone Tiberi [aut, cre] (<https://orcid.org/0000-0002-3054-9964>) |
| Maintainer: | Simone Tiberi <[email protected]> |
Questions relative to IsoBayes should be reported as a new issue at https://github.com/SimoneTiberi/IsoBayes/issues.
To access the vignettes, type: browseVignettes("IsoBayes").
Index of help topics:
IsoBayes-package IsoBayes: Single Isoform protein inference
Method via Bayesian Analyses
generate_SE Generate SummarizedExperiment object
inference Run our two-layer latent variable Bayesian
model
input_data Load and process input data
plot_relative_abundances
Plot isoform results
Jordy Bollon [email protected], Simone Tiberi [email protected]
generate_SE converts the input files, required to run IsoBayes,
into a SummarizedExperiment object.
This object should then be passed to input_data function.
generate_SE( path_to_peptides_psm = NULL, path_to_peptides_intensities = NULL, input_type = NULL, abundance_type = NULL, PEP = TRUE, FDR_thd = 0.01 )generate_SE( path_to_peptides_psm = NULL, path_to_peptides_intensities = NULL, input_type = NULL, abundance_type = NULL, PEP = TRUE, FDR_thd = 0.01 )
path_to_peptides_psm |
a character string indicating the path to one of the following files: i) the psmtsv file from *MetaMorpheus* tool with PSM counts, ii) the idXML file from *OpenMS* toolkit, or iii) a |
path_to_peptides_intensities |
(optional) a character string indicating the path to the psmtsv file from *MetaMorpheus* with intensity values. Required if 'abundance_type' equals to "intensities" and input_type equals to "metamorpheus". |
input_type |
a character string indicating the tool used to obtain the peptides file: "metamorpheus", "openMS" or "other". |
abundance_type |
a character string indicating the type of input: "psm" or "intensities". |
PEP |
logical; if TRUE (default), the algorithm will account for the probability that peptides are erroneously detected. If FALSE, PEP is ignored. 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). |
FDR_thd |
a numeric value indicating the False Discovery Rate threshold to be used to discard unreliable peptides. |
A SummarizedExperiment object.
Jordy Bollon [email protected] and Simone Tiberi [email protected]
# Load internal data to the package: data_dir = system.file("extdata", package = "IsoBayes") # Define the path to the AllPeptides.psmtsv file returned by *MetaMorpheus* tool path_to_peptides_psm = paste0(data_dir, "/AllPeptides.psmtsv") # Generate a SummarizedExperiment object SE = generate_SE(path_to_peptides_psm = path_to_peptides_psm, abundance_type = "psm", input_type = "metamorpheus" ) # For more examples see the vignettes: # browseVignettes("IsoBayes")# Load internal data to the package: data_dir = system.file("extdata", package = "IsoBayes") # Define the path to the AllPeptides.psmtsv file returned by *MetaMorpheus* tool path_to_peptides_psm = paste0(data_dir, "/AllPeptides.psmtsv") # Generate a SummarizedExperiment object SE = generate_SE(path_to_peptides_psm = path_to_peptides_psm, abundance_type = "psm", input_type = "metamorpheus" ) # For more examples see the vignettes: # browseVignettes("IsoBayes")
inference runs our two-layer latent variable Bayesian model,
taking as input the data created by input_data.
inference( loaded_data, map_iso_gene = NULL, n_cores = 1, K = 2000, burn_in = 1000, thin = 1 )inference( loaded_data, map_iso_gene = NULL, n_cores = 1, K = 2000, burn_in = 1000, thin = 1 )
loaded_data |
|
map_iso_gene |
(optional) a character string indicating
the path to a csv file with two columns:
the 1st one containing the isoform id, and the 2nd one with the gene name.
This argument is required to return protein isoform relative abundances,
normalized within each gene
(i.e., adding to 1 within a gene), to plot results via |
n_cores |
the number of cores to use during algorithm execution. We suggest increasing the number of threads for large datasets only. |
K |
the number of MCMC iterations. Minimum 2000. |
burn_in |
the number of initial iterations to discard. Minimum 1000. |
thin |
thinning value to apply to the final MCMC chain. Useful for decreasing the memory (RAM) usage. |
A list of three data.frame objects: 'isoform_results',
and (only if ‘map_iso_gene' is provided) ’normalized_isoform_results'
(relative abundances normalized within each gene)
and 'gene_abundance'. For more information about the results stored
in the three data.frame objects, see the vignettes:
#browseVignettes("IsoBayes")
Jordy Bollon [email protected] and Simone Tiberi [email protected]
input_data and plot_relative_abundances
# Load internal data to the package: data_dir = system.file("extdata", package = "IsoBayes") # Define the path to the AllPeptides.psmtsv file returned by MetaMorpheus tool path_to_peptides_psm = paste0(data_dir, "/AllPeptides.psmtsv") # Generate a SummarizedExperiment object SE = generate_SE(path_to_peptides_psm = path_to_peptides_psm, abundance_type = "psm", input_type = "metamorpheus" ) # Define the path to the jurkat_isoform_kallisto.tsv with mRNA relative abundance tpm_path = paste0(data_dir, "/jurkat_isoform_kallisto.tsv") # Load and process SE object data_loaded = input_data(SE, path_to_tpm = tpm_path) # Define the path to the map_iso_gene.csv file path_to_map_iso_gene = paste0(data_dir, "/map_iso_gene.csv") # Run the algorithm set.seed(169612) results = inference(data_loaded, map_iso_gene = path_to_map_iso_gene) # Results is a list of 3 data.frames: names(results) # Main results: head(results$isoform_results) # Results normalized within genes # (relative abunances add to 1 within each gene): # useful to study alternative splicing within genes: head(results$normalized_isoform_results) # Gene abundance head(results$gene_abundance) # For more examples see the vignettes: # browseVignettes("IsoBayes")# Load internal data to the package: data_dir = system.file("extdata", package = "IsoBayes") # Define the path to the AllPeptides.psmtsv file returned by MetaMorpheus tool path_to_peptides_psm = paste0(data_dir, "/AllPeptides.psmtsv") # Generate a SummarizedExperiment object SE = generate_SE(path_to_peptides_psm = path_to_peptides_psm, abundance_type = "psm", input_type = "metamorpheus" ) # Define the path to the jurkat_isoform_kallisto.tsv with mRNA relative abundance tpm_path = paste0(data_dir, "/jurkat_isoform_kallisto.tsv") # Load and process SE object data_loaded = input_data(SE, path_to_tpm = tpm_path) # Define the path to the map_iso_gene.csv file path_to_map_iso_gene = paste0(data_dir, "/map_iso_gene.csv") # Run the algorithm set.seed(169612) results = inference(data_loaded, map_iso_gene = path_to_map_iso_gene) # Results is a list of 3 data.frames: names(results) # Main results: head(results$isoform_results) # Results normalized within genes # (relative abunances add to 1 within each gene): # useful to study alternative splicing within genes: head(results$normalized_isoform_results) # Gene abundance head(results$gene_abundance) # For more examples see the vignettes: # browseVignettes("IsoBayes")
input_data reads and processes a SummarizedExperiment object collecting
input data and metadata required to run IsoBayes model.
input_data(SE, path_to_tpm = NULL)input_data(SE, path_to_tpm = NULL)
SE |
a |
path_to_tpm |
(optional) a |
A list of data.frame objects, with the data needed
to run inference function.
Jordy Bollon [email protected] and Simone Tiberi [email protected]
# Load internal data to the package: data_dir = system.file("extdata", package = "IsoBayes") # Define the path to the AllPeptides.psmtsv file returned by *MetaMorpheus* tool path_to_peptides_psm = paste0(data_dir, "/AllPeptides.psmtsv") # Generate a SummarizedExperiment object SE = generate_SE(path_to_peptides_psm = path_to_peptides_psm, abundance_type = "psm", input_type = "metamorpheus" ) # Load and process SE object data_loaded = input_data(SE) # For more examples see the vignettes: # browseVignettes("IsoBayes")# Load internal data to the package: data_dir = system.file("extdata", package = "IsoBayes") # Define the path to the AllPeptides.psmtsv file returned by *MetaMorpheus* tool path_to_peptides_psm = paste0(data_dir, "/AllPeptides.psmtsv") # Generate a SummarizedExperiment object SE = generate_SE(path_to_peptides_psm = path_to_peptides_psm, abundance_type = "psm", input_type = "metamorpheus" ) # Load and process SE object data_loaded = input_data(SE) # For more examples see the vignettes: # browseVignettes("IsoBayes")
plot_relative_abundances plots protein isoforms results,
obtained by inference,
for a specific gene, together with transcripts abundances if available.
plot_relative_abundances( res_inference, gene_id, plot_CI = TRUE, normalize_gene = TRUE )plot_relative_abundances( res_inference, gene_id, plot_CI = TRUE, normalize_gene = TRUE )
res_inference |
|
gene_id |
a character string indicating the gene to be plotted. |
plot_CI |
logical; if TRUE (default), plot 0.95 level Credibility Intervals for each isoform. |
normalize_gene |
logical; if TRUE (default), plot isoform relative abundances, normalized within the specified gene (they add to 1 within a gene). |
A ggplot object, showing isoform relative abundances for a specific gene.
Jordy Bollon [email protected] and Simone Tiberi [email protected]
# Load internal data to the package: data_dir = system.file("extdata", package = "IsoBayes") # Define the path to the AllPeptides.psmtsv file returned by MetaMorpheus tool path_to_peptides_psm = paste0(data_dir, "/AllPeptides.psmtsv") # Generate a SummarizedExperiment object SE = generate_SE(path_to_peptides_psm = path_to_peptides_psm, abundance_type = "psm", input_type = "metamorpheus" ) # Define the path to the jurkat_isoform_kallisto.tsv with mRNA relative abundance tpm_path = paste0(data_dir, "/jurkat_isoform_kallisto.tsv") # Load and process SE object data_loaded = input_data(SE, path_to_tpm = tpm_path) # Define the path to the map_iso_gene.csv file path_to_map_iso_gene = paste0(data_dir, "/map_iso_gene.csv") # Run the algorithm set.seed(169612) results = inference(data_loaded, map_iso_gene = path_to_map_iso_gene) # results is a list of 3 data.frames: names(results) # main results: head(results$isoform_results) # gene abundance head(results$gene_abundance) # results normalized within genes (total abundance of each gene), # useful to study alternative splicing within genes: head(results$normalized_isoform_results) # Plotting results, normalizing within genes # (relative abundances add to 1 within each gene): plot_relative_abundances(results, gene_id = "TUBB", normalize_gene = TRUE ) # Plotting results, NOT normalized # (relative abundances add to 1 across all isoforms in the dataset): plot_relative_abundances(results, gene_id = "TUBB", normalize_gene = FALSE ) # For more examples see the vignettes: # browseVignettes("IsoBayes")# Load internal data to the package: data_dir = system.file("extdata", package = "IsoBayes") # Define the path to the AllPeptides.psmtsv file returned by MetaMorpheus tool path_to_peptides_psm = paste0(data_dir, "/AllPeptides.psmtsv") # Generate a SummarizedExperiment object SE = generate_SE(path_to_peptides_psm = path_to_peptides_psm, abundance_type = "psm", input_type = "metamorpheus" ) # Define the path to the jurkat_isoform_kallisto.tsv with mRNA relative abundance tpm_path = paste0(data_dir, "/jurkat_isoform_kallisto.tsv") # Load and process SE object data_loaded = input_data(SE, path_to_tpm = tpm_path) # Define the path to the map_iso_gene.csv file path_to_map_iso_gene = paste0(data_dir, "/map_iso_gene.csv") # Run the algorithm set.seed(169612) results = inference(data_loaded, map_iso_gene = path_to_map_iso_gene) # results is a list of 3 data.frames: names(results) # main results: head(results$isoform_results) # gene abundance head(results$gene_abundance) # results normalized within genes (total abundance of each gene), # useful to study alternative splicing within genes: head(results$normalized_isoform_results) # Plotting results, normalizing within genes # (relative abundances add to 1 within each gene): plot_relative_abundances(results, gene_id = "TUBB", normalize_gene = TRUE ) # Plotting results, NOT normalized # (relative abundances add to 1 across all isoforms in the dataset): plot_relative_abundances(results, gene_id = "TUBB", normalize_gene = FALSE ) # For more examples see the vignettes: # browseVignettes("IsoBayes")