Package: DeMixT 2.1.0

Ruonan Li

DeMixT: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms

DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.

Authors:Zeya Wang [aut], Shaolong Cao [aut], Liyang Xie [aut], Ruonan Li [cre], Wenyi Wang [aut]

DeMixT_2.1.0.tar.gz
DeMixT_2.1.0.zip(r-4.7)DeMixT_2.1.0.zip(r-4.6)DeMixT_2.1.0.zip(r-4.5)
DeMixT_2.1.0.tgz(r-4.6-x86_64)DeMixT_2.1.0.tgz(r-4.6-arm64)DeMixT_2.1.0.tgz(r-4.5-x86_64)DeMixT_2.1.0.tgz(r-4.5-arm64)
DeMixT_2.1.0.tar.gz(r-4.7-arm64)DeMixT_2.1.0.tar.gz(r-4.7-x86_64)DeMixT_2.1.0.tar.gz(r-4.6-arm64)DeMixT_2.1.0.tar.gz(r-4.6-x86_64)
DeMixT_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
DeMixT/json (API)

# Install 'DeMixT' in R:
install.packages('DeMixT', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On BioConductor:DeMixT-2.1.0(bioc 3.24)DeMixT-2.0.0(bioc 3.23)

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

softwarestatisticalmethodclassificationgeneexpressionsequencingmicroarraytissuemicroarraycoveragecppopenmp

5.81 score 32 scripts 5 mentions 18 exports 91 dependencies

Last updated from:7874c43e7a. Checks:1 WARNING, 11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING208
linux-devel-arm64NOTE302
linux-devel-x86_64NOTE419
source / vignettesOK282
linux-release-arm64NOTE310
linux-release-x86_64NOTE375
macos-release-arm64NOTE203
macos-release-x86_64NOTE543
macos-oldrel-arm64NOTE212
macos-oldrel-x86_64NOTE465
windows-develNOTE490
windows-releaseNOTE419
windows-oldrelNOTE315
wasm-releaseOK185

Exports:batch_correctionDeMixNBDeMixNB_preprocessingDeMixTDeMixT_DEDeMixT_GSDeMixT_preprocessingDeMixT_S2detect_suspicious_sample_by_hierarchical_clustering_2compgene_selection_DEOptimum_KernelCplot_dimplot_sdscale_normalization_75th_percentilesimulate_2compsimulate_3compsubset_sdsubset_sd_gene_remaining

Dependencies:abindannotateAnnotationDbiaskpassbase64encBHBiobaseBiocGenericsBiocParallelBiostringsbitbit64blobcachemclicodetoolscpp11crayoncurlDBIDelayedArraydendextendedgeRevdfarverfastmapfitdistrplusformatRfutile.loggerfutile.optionsgenefiltergenericsGenomicRangesggplot2glueGPArotationgridExtragtablehttrIRangesisobandjsonliteKEGGRESTKernSmoothlabelinglambda.rlatticelifecyclelimmalocfitmagrittrMASSMatrixmatrixcalcMatrixGenericsmatrixStatsmemoisemgcvmimemnormtnlmeopensslpbapplypkgconfigpngpsychR6RColorBrewerRcpprlangRSQLiteS4ArraysS4VectorsS7scalesSeqinfosnowSparseArraystatmodSummarizedExperimentsurvivalsvasystruncdistvctrsviridisviridisLitewithrXMLxtableXVector

A Vignette for DeMixT
Introduction | Feature Description | Installation | Functions | Methods | Model | The DeMixT algorithm for deconvolution | Examples | Simulated two-component data | Simulated three-component data | Real data: PRAD in TCGA dataset | Obtain raw read counts for the tumor and normal RNAseq data | Data preprocessing | Deconvolution using DeMixT | Deconvolution using normal reference samples from GTEx | Reference | Session Info

Last update: 2026-03-26
Started: 2026-03-26

DeMixNB: Deconvolution for Sparse Count Data
Introduction | Feature Description | Functions | Methods | Model | The DeMixNB Algorithm for Deconvolution | Real data Example | Spatially Resolved Transcriptomics | Results | Session Information

Last update: 2026-03-26
Started: 2026-03-26