Manual Sigma
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Package ‘SigMA’ December 5, 2018 Title Signature Multivariate Analysis Version 1.0.0.0 Description SigMA is a signature analysis tool optimized to detect the mutational signature associated to HR defect, Signature 3, from hybrid capture panels, exomes and whole genome sequencing. For panels with low SNV counts, conventional signature analysis tools do not perform well while the novel approach of SigMA allows it to detect Signature 3-positive tumors with 74% sensitivity at 10% false positive rate. One novelty of SigMA is a likelihood based matching: We associate a new patient's mutational spectrum to subtypes of tumors according to their signature composition. The subtypes of tumors are defined using the WGS data from ICGC and TCGA consortia, by a clustering of signature fractions with hierarchical clustering. The likelihood of the sample to belong to each tumor subtype is calculated, and the likelihood of Signature 3 is the sum of the likelihoods of all Signature 3-positive tumor subtypes. The second novel step is the multivariate analysis with gradient boosting machines, which allows us to obtain a final score for presence of Signature-3 combining likelihood with cosine similarity and exposure of Signature 3 obtained with nonnegativel-least-squares (NNLS) algorithm. The multivariate analysis allows us to automatically handle different sequencing platforms. For different platforms different methods for signature analysis become more efficient, e.g. for WGS data it is not necessary to associate the tumor to a subtype of tumors, because it is possible to determine Signature 3 with NNLS acurately. We have a new feature also for these cases and we calculate the likelihood of NNLS decomposition to be unique. This likelihood value was found to be the most influential feature in the multivariate analysis. Depends R (>= 3.4.0) License What license is it under? Encoding UTF-8 LazyData true RoxygenNote 6.1.0 Imports BSgenome, BSgenome.Hsapiens.UCSC.hg19, devtools, DT, GenomicRanges, ggplot2, gbm, grid, gridExtra, IRanges, nnls, 1 2 assignment reshape2, Rmisc, shinycssloaders, VariantAnnotation R topics documented: assignment . . . calc_llh . . . . . cosine . . . . . . decompose . . . lite_df . . . . . . make_matrix . . match_to_catalog plot_detailed . . plot_summary . . plot_tribase_dist . predict_mva . . . run . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Index assignment 2 3 3 4 4 5 6 6 7 7 8 8 10 Assigns a boolean based on a threshold on the likelihood or mva score for whether the signature is identified Description Assigns a boolean based on a threshold on the likelihood or mva score for whether the signature is identified Usage assignment(df_in, method = "mva", signame = "Signature_3", data = NULL, tumor_type = "breast", do_strict = T, weight_cf) Arguments df_in method signame data tumor_type do_strict input data.frame ’median_catalog’ for likelihood based selection or ’mva’ for multivariate analysis score based selection name of the signature that user wants to identify, ’Signature_3’ or ’Signature_msi’ ’msk’, ’seqcap’ or ’wgs’ tumor type as listed in https://github.com/parklab/SigMA/ because the thresholds are tumor_type specific sets whether a strict threshold should be applied or a loose one Value a data.frame with a single column which contains the boolean indicating the presence of the signature calc_llh calc_llh 3 Calculates likelihood of the genome with respect to the available signature probability distributions Description Calculates likelihood of the genome with respect to the available signature probability distributions Usage calc_llh(spectrum, signatures, counts = NULL, normalize = T) Arguments spectrum is the mutational spectrum signatures is the reference signature catalog with the probability distributions counts is the number of cases in each cluster that is represented in the catalog. They are used as weights for each signature in the catalog normalize is true by default only for when it is used together with NNLS in the match_to_catalog function it is not normalized here but outside of the function cosine calculates cosine similarity between the spectrum and a set of signatures Description calculates cosine similarity between the spectrum and a set of signatures Usage cosine(x, y) Arguments spectrum is the mutational spectrum signatures is the reference signature catalog with the probability distribution 4 lite_df decompose Decomposes the mutational spectrum of a genome in terms of tumor type specific signatures that were calculated through analysis of public WGS samples from ICGC and TCGA consortia, and contained as a list in the package. Non-negative-least squares algorithm is used and the number of signatures to be considered in the decomposition is increased gradually, first all pairs from among the available signatures are considered and minimal error pair is kept. Then all 3-signature combinations, 4-signature combinations and so on are considered. The result is updated if the error is smaller with larger number of signatures Description Decomposes the mutational spectrum of a genome in terms of tumor type specific signatures that were calculated through analysis of public WGS samples from ICGC and TCGA consortia, and contained as a list in the package. Non-negative-least squares algorithm is used and the number of signatures to be considered in the decomposition is increased gradually, first all pairs from among the available signatures are considered and minimal error pair is kept. Then all 3-signature combinations, 4-signature combinations and so on are considered. The result is updated if the error is smaller with larger number of signatures Usage decompose(spect, signatures, data) Arguments spect composite spectrum that is being decomposed signatures a data.frame that contains the signatures in its columns data sequencing platform that as in run(), used for setting the maximum number of signatures that is allowed in the decomposition lite_df produces a data.frame with fewer columns for easier use Description produces a data.frame with fewer columns for easier use Usage lite_df(merged_output) Arguments merged_output is the input data.frame make_matrix make_matrix 5 Converts somatic mutation call files in a directory either in the form of vcf or maf into a 96-dimensional matrix, it works for general number of context and for 1 or 2 strands Description Converts somatic mutation call files in a directory either in the form of vcf or maf into a 96dimensional matrix, it works for general number of context and for 1 or 2 strands Usage make_matrix(directory, file_type = "vcf", ref_genome = BSgenome.Hsapiens.UCSC.hg19::BSgenome.Hsapiens.UCSC.hg19, ncontext = 3, nstrand = 1, chrom_colname = NULL, pos_colname = NULL, ref_colname = NULL, alt_colname = NULL) Arguments directory pointer to the directory where input vcf maf files reside file_type ’maf’, ’vcf’ or ’custom’ ref_genome name of the BSgenome currently set by default to BSgenome.Hsapiens.UCSC.hg19 ncontext number of bases in the nucleotide sequence which makes up the spectrum, default 3 nstrand number of strands to be considered, 1 contracts to a single strand which for ncontext = 3 gives the commonly used 96 dimensions chrom_colname used only for custom files a character string defining the colname which holds the chromosome number pos_colname used only for custom files a character string defining the colname which holds the position information ref_colname used only for custom files a character string defining the colname which holds the ref allele alt_colname used only for custom files a character string defining the colname which holds the alt allele Examples by default runs on vcf input and produces 96 dimensional spectra make_matrix(directory = 'input') make_matrix(directory = 'input', file_type = 'vcf', ref_genome = BSgenome.Hsapiens.UCSC.hg19, ncontext = 5, nstrand = 2) 6 plot_detailed match_to_catalog Calculates the compatibility of a list of genomes to an input catalog based on likelihood and cosine similarity Description Calculates the compatibility of a list of genomes to an input catalog based on likelihood and cosine similarity Usage match_to_catalog(genomes, signatures, data, cluster_fractions = NULL, method = "median_catalog") Arguments genomes a data table or matrix with snv spectra in the first ntype columns and genomes in each row signatures the input catalog, a data table with signature spectra in each column data sets the type of sequencing platform used, options are ’msk’, ’seqcap’, ’wgs’ method can be ’median_catalog’, ’weighted_catalog’ ’cosine_simil’ or ’decompose. ’median_atalog’ uses the signature catalog formed by clustering genome SNV spectra and using it as a probability distribution. The ’median_catalog’ method can be used with any custom signatures data frame if the user intends to provide their own signature table. Value A data frame that contains the input genomes and in addition columns associated to each signature in in the catalog with likelihood and cosine simil values plot_detailed Generates a detailed plot per sample Description Generates a detailed plot per sample Usage plot_detailed(file = NULL, sample = NULL) Arguments file the csv file produced by SigMA sample name to be plotted plot_summary 7 plot_summary Generates summary plot Description Generates summary plot Usage plot_summary(file = NULL) Arguments file plot_tribase_dist the csv file produced by SigMA plots the 96 dimensional mutational spectrum Description plots the 96 dimensional mutational spectrum Usage plot_tribase_dist(df_snvs, file_name = "test.png", labely = "N SNVs", legend = T, text_size = 10, signame = "") Arguments df_snvs a data frame with 96-dimensional spectra on its columns file_name the name of the plot to be generated with the proper extension e.g. "test.pdf", "test.png", etc labely string for the label of the y axis legend boolean determining whether legend should be printed text_size size of the text of the x and y axis text and titles signame a text to be printed on the figure 8 run predict_mva This function uses the trained MVA, in particular gradient boosting models, inside the package to assign a probability for the existence of the signature of interest. Description This function uses the trained MVA, in particular gradient boosting models, inside the package to assign a probability for the existence of the signature of interest. Usage predict_mva(input, signame, data, tumor_type = "breast", weight_cf) Arguments input is a data frame that has likelihood cosine similarity and total snv values in it’s columns signame name of the signature which is being identified data determines the sequencing platform see run() tumor_type tumor type tag see ?run Value a data.frame with a single column with the score of MVA run Runs SigMA: (1) calculates likelihood, cosine similarity, NNLS exposures, and likelihood of the decomposition. (2) These features are later used in multivariate analysis. (3) Based on scores a final decision on existence of the signature. Description Runs SigMA: (1) calculates likelihood, cosine similarity, NNLS exposures, and likelihood of the decomposition. (2) These features are later used in multivariate analysis. (3) Based on scores a final decision on existence of the signature. Usage run(genome_file, output_file = NULL, do_assign = T, data = "msk", tumor_type = "breast", do_mva = T, check_msi = F, weight_cf = F, lite_format = F, add_sig3 = F) run 9 Arguments genome_file a csv file with snv spectra info can be created from vcf file using @make_genome_matrix() function see ?make_genome_matrix output_file the output file name, can be NULL in which case input file name is used and appended with "_output" do_assign boolean for whether a cutoff should be applied to determine the final decision or just the features should be returned data the options are "msk" (for a panel that is similar size to MSK-Impact panel with 410 genes), "seqcap" (for whole exome sequencing), "seqcap_probe" (64 Mb SeqCap EZ Probe v3), or "wgs" (for whole genome sequencing) tumor_type the options are "bladder", "bone_other" (Ewing’s sarcoma or Chordoma), "breast", "crc", "eso", "gbm", "lung", "lymph", "medullo", "osteo", "ovary", "panc_ad", "panc_en", "prost", "stomach", "thy", or "uterus". The exact correspondance of these names can be found in https://github.com/parklab/SigMA do_mva a boolean for whether multivariate analysis should be run check_msi is a boolean which determines whether the user wants to identify micro-sattelite instable tumors weight_cf determines whether the likelihood calculation will take into account the number of tumors in each cluster when it is F the clusters get equal weights and when it’s T they are weighted according to the fraction of tumors in each cluster lite_format saves the output in a lite format when set to true add_sig3 should be set to T when the likelihood of Signature 3 is calculated for tumor types for which Signature 3 was not discovered by NMF in their WGS data Examples run(genome_file = "input_genomes.csv", data = "msk", tumor_type = "ovary") run(genome_file = "input_genomes.csv", data = "seqcap", tumor_type = "bone_other") Index assignment, 2 calc_llh, 3 cosine, 3 decompose, 4 lite_df, 4 make_matrix, 5 match_to_catalog, 6 plot_detailed, 6 plot_summary, 7 plot_tribase_dist, 7 predict_mva, 8 run, 8 10
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