High-Performance Open-Source Archive
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().get_sig_db("latest_RNA-SBS_GRCh37")
get_sig_db("latest_SV_GRCh38")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.
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