Package: hahmmr 1.0.0

hahmmr: Haplotype-Aware Hidden Markov Model for RNA

Haplotype-aware Hidden Markov Model for RNA (HaHMMR) is a method for detecting copy number variations (CNVs) from bulk RNA-seq data. Additional examples, documentations, and details on the method are available at <https://github.com/kharchenkolab/hahmmr/>.

Authors:Teng Gao [aut, cre], Evan Biederstedt [aut], Peter Kharchenko [aut]

hahmmr_1.0.0.tar.gz
hahmmr_1.0.0.zip(r-4.7)hahmmr_1.0.0.zip(r-4.6)hahmmr_1.0.0.zip(r-4.5)
hahmmr_1.0.0.tgz(r-4.6-x86_64)hahmmr_1.0.0.tgz(r-4.6-arm64)hahmmr_1.0.0.tgz(r-4.5-x86_64)hahmmr_1.0.0.tgz(r-4.5-arm64)
hahmmr_1.0.0.tar.gz(r-4.7-arm64)hahmmr_1.0.0.tar.gz(r-4.7-x86_64)hahmmr_1.0.0.tar.gz(r-4.6-arm64)hahmmr_1.0.0.tar.gz(r-4.6-x86_64)
hahmmr_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
hahmmr/json (API)

# Install 'hahmmr' in R:
install.packages('hahmmr', repos = c('https://teng-gao.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

2.18 score 1 packages 4 scripts 316 downloads 16 exports 39 dependencies

Last updated from:466187fcff. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK189
linux-devel-x86_64OK176
source / vignettesOK246
linux-release-arm64OK179
linux-release-x86_64OK179
macos-release-arm64OK222
macos-release-x86_64OK375
macos-oldrel-arm64OK156
macos-oldrel-x86_64OK279
windows-develOK183
windows-releaseOK152
windows-oldrelOK141
wasm-releaseOK131

Exports:analyze_alleleanalyze_jointdbbinomdpoilogfit_lnpois_cppforward_back_alleleget_allele_bulkget_bulkl_bbinoml_lnpoislikelihood_allelelogSumExpplot_bulksplot_psbulkrun_allele_hmm_s5run_joint_hmm_s15

Dependencies:BiocGenericsclicpp11data.tabledplyrfarvergenericsGenomicRangesggplot2gluegtableIRangesisobandlabelinglatticelifecyclemagrittrpatchworkpillarpkgconfigR6RColorBrewerRcppRcppArmadillorlangroptimS4VectorsS7scalesSeqinfostringistringrtibbletidyselectutf8vctrsviridisLitewithrzoo

Readme and manuals

Help Manual

Help pageTopics
centromere regions (hg19)acen_hg19
centromere regions (hg38)acen_hg38
Analyze allele profileanalyze_allele
Analyze allele and expression profileanalyze_joint
example pseudobulk dataframebulk_example
chromosome sizes (hg19)chrom_sizes_hg19
chromosome sizes (hg38)chrom_sizes_hg38
Beta-binomial distribution density function A distribution is beta-binomial if p, the probability of success, in a binomial distribution has a beta distribution with shape parameters alpha > 0 and beta > 0 For more details, see extraDistr::dbbinomdbbinom
example allele count dataframedf_allele_example
Returns the density for the Poisson lognormal distribution with parameters mu and sigdpoilog
Fit MLE of log-normal Poisson modelfit_lnpois_cpp
Forward-backward algorithm for allele HMMforward_back_allele
genome gap regions (hg19)gaps_hg19
genome gap regions (hg38)gaps_hg38
example gene expression counts matrixgene_counts_example
Aggregate into pseudobulk alelle profileget_allele_bulk
Produce combined bulk expression and allele profileget_bulk
gene model (hg19)gtf_hg19
gene model (hg38)gtf_hg38
gene model (mm10)gtf_mm10
calculate joint likelihood of allele datal_bbinom
calculate joint likelihood of a PLN modell_lnpois
Only compute total log likelihood from an allele HMMlikelihood_allele
logSumExp functionlogSumExp
Plot a group of pseudobulk HMM profilesplot_bulks
Plot a pseudobulk HMM profileplot_psbulk
HMM object for unit testspre_likelihood_hmm
reference expression magnitudes from HCAref_hca
reference expression counts from HCAref_hca_counts
Run a 5-state allele-only HMM - two theta levelsrun_allele_hmm_s5
Run 15-state joint HMM on a pseudobulk profilerun_joint_hmm_s15
example CNV segments dataframesegs_example
example VCF headervcf_meta