sig_fit() related documents for better usage (#454).cluster_col to show_group_enrichment().cluster_row = TRUE & return_list = TRUE in function show_group_enrichment().samps option to show_sig_exposure().Example:
load(system.file("extdata", "toy_mutational_signature.RData",
package = "sigminer", mustWork = TRUE
))
# Show signature exposure
p1 <- show_sig_exposure(sig2, rm_space = TRUE)
p1
expo = sig_exposure(sig2)
show_sig_exposure(expo,
rm_space = TRUE,
samps = colnames(expo)[order(colSums(expo))])read_vcf().read_maf_minimal() to support a minimal MAF-like data as input.sig_tally().sigprofiler_extract() to help generate input matrix file for calling SigProfiler directly.sigprofiler_extract().sigprofiler_reorder() for utils in generating SigProfiler input matrix file with standard mutation types order.latest_CN_GRCh37 (#412).get_sig_similarity() now uses “SBS” as default reference.show_cn_circos().group_enrichment2().sig_tally().CNS_TCGA.group_enrichment() with reference group support.Example:
set.seed(1234)
df <- dplyr::tibble(
g1 = rep(LETTERS[1:3], c(50, 40, 10)),
g2 = rep(c("AA", "VV", "XX"), c(50, 40, 10)),
e1 = sample(c("P", "N"), 100, replace = TRUE),
e2 = rnorm(100)
)
x1 = group_enrichment(df, grp_vars = c("g1", "g2"),
enrich_vars = c("e1", "e2"),
ref_group = c("B", "VV"))
x1read_copynumber_seqz() to include minor copy number. (Thanks to yancey)range check in sig_estimate(). (#391)output_* function by adding option sig_db.sigminer::get_genome_annotation() before loading it.get_pLOH_score() return nothing for sample without LOH.sig_unify_extract() as an unified signature extractor.CNS_TCGA database.y_limits option in show_sig_profile() (#381).get_pLOH_score() for representing the genome that displayed LOH.read_copynumber_ascat() for reading ASCAT result ASCAT object in .rds format.get_intersect_size() for getting overlap size between intervals.get_Aneuploidy_score() to remove short arms of chr13/14/15/21/22 from calculation.show_sig_feature_corrplot() (#376).read_vcf().sig_tally() (#370).sigprofiler_extract() extracting copy number signatures and rolled up sigprofiler version (#369).output_sig() error in handling exposure plot with >9 signatures (#366).limitsize = FALSE for ggsave() or ggsave2() for handling big figure.mm9 genome build.call_component.read_vcf() with ## commented VCF files.for (i in c("latest_SBS_GRCh37", "latest_DBS_GRCh37", "latest_ID_GRCh37",
"latest_SBS_GRCh38", "latest_DBS_GRCh38",
"latest_SBS_mm9", "latest_DBS_mm9",
"latest_SBS_mm10", "latest_DBS_mm10",
"latest_SBS_rn6", "latest_DBS_rn6")) {
message(i)
get_sig_db(i)
}keep_only_pass to FALSE at default.get_sig_rec_similarity().output_tally() and show_catalogue().show_group_enrichment() (#353) & added a new option to cluster rows.bp_show_survey().torch check.read_sv_as_rs() and sig_tally.RS() for simplified genome rearrangement classification matrix generation (experimental).bp_extract_signatures() with lpSolve package instead of using my problematic code.mm10 in read_vcf().bp_extract_signatures() (#332). PAY ATTENTION: this may affect results.show_sig_profile_loop().sig_names option.https://anaconda.org/bioconda/r-sigminer/ms strategy in sig_auto_extract() by assigning each signature to its best matched reference signatures.get_shannon_diversity_index() to get diversity index for signatures (#333).get_sig_exposure().bp_get_clustered_sigs() to get clustered mean signatures.highlight is added to show_sig_number_survey() and bp_show_survey2() to highlight a selected number.cut_p_value is added to show_group_enrichment() to cut continous p values as binned regions.sig_extract() is provided.sig_extract() and sig_auto_extract() instead of loading NMF package firstly.auto_reduce in sig_fit() is modified from 0.99 to 0.95 and similarity update threshold updated from >0 to >=0.01.pConstant option from sig_extract() and sig_estimate(). Now a auto-check function is created for avoiding the error from NMF package due to no contribution of a component in all samples.bp_show_survey2() to plot a simplified version for signature number survey (#330).read_xena_variants() to read variant data from UCSC Xena as a MAF object for signature analysis.get_sig_rec_similarity() for getting reconstructed profile similarity for Signature object (#293).bp_ which are combined to provide a best practice for extracting signatures in cancer researches. See more details, run ?bp in your R console.future warnings.show_cor(), thanks to @Miachol.y_tr option in show_sig_profile() to transform y axis values.read_copynumber().
complement = FALSE as default.use_all and complement.show_sig_bootstrap() (#298).group_enrichment() and show_group_enrichment() (#277).?sigminer documentation.ms strategy to select optimal solution by maximizing cosine similarity to reference signatures.same_size_clustering() for same size clustering.show_cosmic() to support reading COSMIC signatures in web browser (#288).rel_threshold behavior in sig_fit() and get_sig_exposure(). Made them more consistent and allowed un-assigned signature contribution (#285).SBS_mm9.data.frame as input object for sig in get_sig_similarity() and sig_fit().g_label option in show_group_distribution() to better control group names.test option and variable checking in show_cor().output_sig() to output signature exposure distribution (#280).show_cor() for general association analysis.show_group_distribution() to control segments.add_labels(), thanks to TaoTao for reporting., seperated indices in show_cosmic_signatures.set_order in get_sig_similarity() (#274).output_sig().show_sig_bootstrap_error(), now it is “Reconstruction error (L2 norm)”auto_reduce option in sig_fit* functions to improve signature fitting.sig_fit().sig_auto_extract() to ‘optimal’.get_sig_cancer_type_index().sigprofiler_extract() to reduce failure in when refit is enabled.output_sig().show_group_distribution().optimize option in sig_extract() and sig_auto_extract()., in sig_fit() and sig_fit_bootstrap* functions.output_* functions from sigflow.sig_tally().highlight_genes in show_cn_group_profile() to show gene labels.get_sig_cancer_type_index() to get reference signature index.show_group_distribution() to show group distribution.show_cn_profile() to show specified ranges and add copy number value labels.nnls instead of pracma for NNLS implementation in sig_fit().BSgenome.Hsapiens.1000genomes.hs37d5 in sig_tally().MT to M in mutation data.show_sig_exposure().letter_colors as an unexported discrete palette.transform_seg_table().show_cn_group_profile().show_cn_freq_circos().sig_orders option in show_sig_profile() function now can select and order signatures to plot.show_sig_profile_loop() for better signature profile visualization.read_copynumber(), got 200% improvement.read_copynumber(), got 20% improvement.cosine() function.get_sig_db() to let users directly load signature database.sigprofiler_extract() and sigprofiler_import() to call SigProfiler and import results.read_vcf() for simply reading VCF files.show_sig_profile_heatmap().read_copynumber_seqz() to read sequenza result directory.read_copynumber().read_maf().sig_fit() to ‘NNLS’ and implement it with pracma package (#216).use_all option in read_copynumber() working correctly.MRSE to RMSE.show_sig_bootstrap_*() for plotting aggregated values.get_groups() for clustering.highlight_size for show_sig_bootstrap_*().sig_fit() function to better visualize a few samples.lsei package was removed from CRAN, here I reset default method to ‘QP’ and tried best to keep the LS usage in sigminer (#189).show_sig_profile() and added input checking for this function.furrr, solution is from https://github.com/DavisVaughan/furrr/issues/107.sig_fit() for methods QP and SA.show_sig_fit() and show_sig_bootstrap_* functions.sig_fit_bootstrap_batch for more useful in practice.show_groups() to show the signature contribution in each group from get_groups().get_groups() to result of sig_fit().sig_fit_bootstrap_batch().sig_tally().cores > 1 (#161).sig_fit().sig_fit_bootstrap() for bootstrap results.Imports to Suggests.report_bootstrap_p_value() to report p values.data().fuzzyjoin package from dependency.ggalluvial package to field suggsets.All users, this is a break-through version of sigminer, most of functions have been modified, more features are implemented. Please read the reference list to see the function groups and their functionalities.
Please read the vignette for usage.
I Hope it helps your research work and makes a new contribution to the scientific community.