Automated Deconvolution Augmentation of Profiles for Tissue Specific Cells
Make an augmented signature matrix
Build a deconvolution seed matrix, add the proportional option
Build a spillover matrix
Calculate prediction accuracy
Cluster with spillover
Collapse cell types
Deconvolve with an n-pass spillover matrix
DCQ Deconvolution
DeconRNASeq deconvolution
Non-negative least squares deconvolution
WGCNA::proportionsInAdmixture deconvolution
Wrapper for deconvolution methods
Estimate cell percentage from spillover
SVMDECON deconvolution
Find out at which iteration the results converge, i.e. the mean result...
Get f1 / mcc
LM22 look up table
Build a gList using random forest
Hierarchical Deconvolution
Build hierarchical cell clusters.
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Load MGSM27
LM22 to xCell LUT
Loop testAllSigMatrices until convergence
Make a GSVA genelist
A meta analysis for the results from multiple iterations
Use parallel missForest to impute missing values.
Plot condition numbers
Rank genes for each cell type
Make an Augmented Signature Matrix
Build groupSize pools according to cellIDs
Calculate conditions numbers for signature subsets
Shrink a signature matrix
Spillover to convergence
Split a single cell dataset into multiple sets
Support vector machine deconvolution
Generate all the signature matrices one time with the option to leave ...
SVMDECONV helper function
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>.