Package: ADAPTS 1.0.22

ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific Cells

Tools to construct (or add to) cell-type signature matrices using flow sorted or single cell samples and deconvolve bulk gene expression data. Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells <doi:10.1371/journal.pone.0224693>.

Authors:Samuel A Danziger

ADAPTS_1.0.22.tar.gz
ADAPTS_1.0.22.zip(r-4.7)ADAPTS_1.0.22.zip(r-4.6)ADAPTS_1.0.22.zip(r-4.5)
ADAPTS_1.0.22.tgz(r-4.6-any)ADAPTS_1.0.22.tgz(r-4.5-any)
ADAPTS_1.0.22.tar.gz(r-4.7-any)ADAPTS_1.0.22.tar.gz(r-4.6-any)
ADAPTS_1.0.22.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
ADAPTS/json (API)

# Install 'ADAPTS' in R:
install.packages('ADAPTS', repos = c('https://sdanzige.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sdanzige/adapts/issues

Datasets:
  • Licenses - Licenses required by Celgene legal
  • LM22 - Leukocyte 22 data matrix
  • MGSM27 - Myeloma Genome Signature Matrix 27

On CRAN:

Conda:

6.72 score 3 stars 1 packages 39 scripts 512 downloads 6 mentions 34 exports 49 dependencies

Last updated from:67507831cb. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64NOTE133
source / vignettesOK219
linux-release-x86_64NOTE175
macos-release-arm64NOTE191
macos-oldrel-arm64NOTE215
windows-develNOTE145
windows-releaseNOTE107
windows-oldrelNOTE111
wasm-releaseOK156

Exports:AugmentSigMatrixbuildSeedbuildSpilloverMatcalcAccclustWspillOvercollapseCellTypesestCellCounts.nPassestCellPercentestCellPercent.DCQestCellPercent.DeconRNASeqestCellPercent.nnlsestCellPercent.proportionsInAdmixtureestCellPercent.spillOverestCellPercent.svmdecongetF1mccgetLM22cellsgListFromRFhierarchicalClassifyhierarchicalSplitloadMGSM27loadModMaploopTillConvergencematrixToGenelistmeanResultsmissForest.parplotKappasrankByTremakeLM22pscSampleshrinkByKappashrinkSigMatrixspillToConvergencesplitSCdatatestAllSigMatrices

Dependencies:BiobaseBiocGenericsclassclicodetoolsComICScurldigestdoParalleldoRNGe1071farverforeachgenericsglmnetgluegtableiteratorsitertoolsjsonlitelabelinglatticelifecycleMASSMatrixmissForestnnlspcaMethodspheatmappreprocessCoreproxyquantmodR6randomForestrangerrbibutilsRColorBrewerRcppRcppEigenRdpackrlangrngtoolsscalesshapesurvivalTTRviridisLitextszoo

ADAPTS Vignette #2: (DEPRECATED) Single Cell Analysis
Rendered fromADAPTS2.vignette.html.asisusingR.rsp::asis

Last update: 2022-09-07
Started: 2019-10-28

ADAPTS Vignette #3: Building a Better Signature Matrix From Single Cell Data
Rendered fromADAPTS3.vignette.html.asisusingR.rsp::asis

Last update: 2022-09-07
Started: 2022-09-07

ADAPTS Vignette #4: Build a Single Cell Based Matrix and Test on Independant Data
Rendered fromADAPTS4.vignette.html.asisusingR.rsp::asis

Last update: 2022-09-07
Started: 2022-09-07

ADAPTS Vignette #5: Automated Repeated Leave-Half-Out Cross-Validation for Robust Accuracy Estimates
Rendered fromADAPTS5.vignette.html.asisusingR.rsp::asis

Last update: 2022-09-07
Started: 2022-09-07

ADAPTS (Automated Deconvolution Augmentation of Profiles for Tissue Specific cells) Vignette
Rendered fromADAPTS.vignette.html.asisusingR.rsp::asis

Last update: 2019-10-28
Started: 2019-05-14

Readme and manuals

Help Manual

Help pageTopics
Make an augmented signature matrixAugmentSigMatrix
Build a deconvolution seed matrix, add the proportional optionbuildSeed
Build a spillover matrixbuildSpilloverMat
Calculate prediction accuracycalcAcc
Cluster with spilloverclustWspillOver
Collapse cell typescollapseCellTypes
Deconvolve with an n-pass spillover matrixestCellCounts.nPass
Wrapper for deconvolution methodsestCellPercent
DCQ DeconvolutionestCellPercent.DCQ
DeconRNASeq deconvolutionestCellPercent.DeconRNASeq
Non-negative least squares deconvolutionestCellPercent.nnls
WGCNA::proportionsInAdmixture deconvolutionestCellPercent.proportionsInAdmixture
Estimate cell percentage from spilloverestCellPercent.spillOver
SVMDECON deconvolutionestCellPercent.svmdecon
Find out at which iteration the results converge, i.e. the mean results are stable.findConvergenceIter
Get f1 / mccgetF1mcc
LM22 look up tablegetLM22cells
Build a gList using random forestgListFromRF
Hierarchical DeconvolutionhierarchicalClassify
Build hierarchical cell clusters.hierarchicalSplit
Licenses required by Celgene legalLicenses
Leukocyte 22 data matrixLM22
Load MGSM27loadMGSM27
LM22 to xCell LUTloadModMap
Loop testAllSigMatrices until convergenceloopTillConvergence
Make a GSVA genelistmatrixToGenelist
A meta analysis for the results from multiple iterationsmeanResults
Myeloma Genome Signature Matrix 27MGSM27
Use parallel missForest to impute missing values.missForest.par
Plot condition numbersplotKappas
Rank genes for each cell typerankByT
Make an Augmented Signature MatrixremakeLM22p
Build groupSize pools according to cellIDsscSample
Calculate conditions numbers for signature subsetsshrinkByKappa
Shrink a signature matrixshrinkSigMatrix
Spillover to convergencespillToConvergence
Split a single cell dataset into multiple setssplitSCdata
Support vector machine deconvolutionSVMDECON
Generate all the signature matrices one time with the option to leave out half of the data as a test settestAllSigMatrices
SVMDECONV helper functionweightNorm