Package: ADImpute 1.15.0

Ana Carolina Leote

ADImpute: Adaptive Dropout Imputer (ADImpute)

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.

Authors:Ana Carolina Leote [cre, aut]

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ADImpute.pdf |ADImpute.html✨
ADImpute/json (API)

# Install 'ADImpute' in R:

install.packages('ADImpute', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/anacarolinaleote/adimpute/issues

Datasets:

demo_data - Small dataset for example purposes
demo_net - Small regulatory network for example purposes
demo_sce - Small dataset for example purposes
network.coefficients - Transcriptome wide gene regulatory network
transcript_length - Table for transcript length calculations

On BioConductor:ADImpute-1.15.0(bioc 3.20)ADImpute-1.14.0(bioc 3.19)

bioconductor-package

6 exports 0.61 score 55 dependencies 1 mentions

Last updated 2 months agofrom:5d42b66d1a

Exports:EvaluateMethods Impute NormalizeRPM NormalizeTPM WriteCSV WriteTXT

Dependencies:abind askpass backports BH Biobase BiocGenerics BiocParallel checkmate codetools cpp11 crayon curl data.table DelayedArray doParallel DrImpute foreach formatR futile.logger futile.options GenomeInfoDb GenomeInfoDbData GenomicRanges glmnet httr IRanges iterators jsonlite kernlab lambda.r lattice MASS Matrix MatrixGenerics matrixStats mime openssl R6 Rcpp RcppArmadillo RcppEigen rsvd S4Arrays S4Vectors SAVER shape SingleCellExperiment snow SparseArray SummarizedExperiment survival sys UCSC.utils XVector zlibbioc

ADImpute tutorial

Rendered fromADImpute_tutorial.Rmdusingknitr::rmarkdownon Jun 30 2024.

Last update: 2020-10-20
Started: 2020-06-03

Help page	Topics
Data trimming	ArrangeData
Data centering	CenterData
Argument check to Impute()	CheckArguments_Impute
Method choice per gene	ChooseMethod
Combine imputation methods	Combine
Computation of MSE per gene	ComputeMSEGenewise
Argument check	CreateArgCheck
Preparation of training data for method evaluation	CreateTrainData
Data check (matrix)	DataCheck_Matrix
Data check (network)	DataCheck_Network
Data check (SingleCellExperiment)	DataCheck_SingleCellExperiment
Data check (transcript length)	DataCheck_TrLength
Small dataset for example purposes	demo_data
Small regulatory network for example purposes	demo_net
Small dataset for example purposes	demo_sce
Imputation method evaluation on training set	EvaluateMethods
Get dropout probabilities	GetDropoutProbabilities
Get dropout probabilities	HandleBiologicalZeros
Dropout imputation using different methods	Impute
Impute using average expression across all cells	ImputeBaseline
Use DrImpute	ImputeDrImpute
Network-based parallel imputation	ImputeNetParallel
Network-based imputation	ImputeNetwork
Helper function to PseudoInverseSolution_percell	ImputeNPDropouts
Helper function to PseudoInverseSolution_percell	ImputePredictiveDropouts
Use SAVER	ImputeSAVER
Masking of entries for performance evaluation	MaskData
Helper mask function	MaskerPerGene
Transcriptome wide gene regulatory network	network.coefficients
RPM normalization	NormalizeRPM
TPM normalization	NormalizeTPM
Network-based parallel imputation - Moore-Penrose pseudoinversion	PseudoInverseSolution_percell
Data read	ReadData
Wrapper for return of EvaluateMethods()	ReturnChoice
Wrapper for return of Impute()	ReturnOut
Set biological zeros	SetBiologicalZeros
Selection of samples for training	SplitData
Table for transcript length calculations	transcript_length
Write csv file	WriteCSV
Write txt file	WriteTXT

Package: ADImpute 1.15.0

ADImpute: Adaptive Dropout Imputer (ADImpute)

ADImpute tutorial

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Help Manual

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