Table 1.
Deconvolution methods with an available user interface. Methods are grouped by classes, which are identified using the labels from Figure 3. Methods are ordered by class. Methods matching more than one class are classified by the highest resolution they provide. For each method, the type of input required and output generated is listed. The color bullets match the labels of relevant blocks in Figure 3.
| In | Out | Name | Description | Availability | Tissue | |
|---|---|---|---|---|---|---|
 |
 |
|
SPEC* | Predicts the most likely cellular source of a given gene expression signature [11] |
clip.med.yale.edu/SPEC R:SPEC |
Blood |
|
|
CTEN* | Identifies enriched cell types in heterogeneous microarray data [12] | www.inuenza-x.org/jshoemaker/cten | - | |
|
|
ssGSEA | Single sample GSEA [10] | ssGSEAProjection in GenePattern [9] | Cancer | |
 |
|
|
collapseRows | Aggregates/selects a proportion proxy within cell type-specific co-expression modules [37] | R:WGCNA | - |
 |
|
Abbas | Estimates proportions of 17 immune cell subsets using IRIS-based signatures [15] | CellMix | Blood | |
|
|
DeconRNAseq | Similar to Abbas but uses quadratic programming instead of standard regression [38] | R + CellMix | - | |
|
|
PERT | Perturbation model that estimates proportions and a global condition effect the reflects deviance from reference pure profiles [26] | Octave code | Blood | |
|
|
methylSpectrum | Estimate proportions from DNA methylation reference profiles [28] | R:methylSpectrum | Blood | |
 |
|
qpure | Estimate tumor cellularity SNP microarray data from paired (tumor and normal) samples [17] | R:qpure | Cancer | |
|
|
ABSOLUTE | Infers tumor purity and malignant cell ploidy directly Copy-Number-Variation data and precomputed models of recurrent cancer karyotypes [18] | R | Cancer | |
 |
 |
|
csSAM* | Estimates cell/tissue specific signatures from known proportions using SAM [25] | R:csSAM + XLS plugin + CellMix | - |
|
|
PSEA* | Population-Specific Expression Analysis, using a regression model selection schema [30] | R function(s) | - | |
 |
|
|
DeMix* | Estimate tumor fraction and individual purified tumor profiles using normal tissue profiles [35] | R function(s) | Cancer |
|
|
ISOpure | Estimate tumor fraction and individual purified sample profiles using normal tissue profiles [34] | Matlab | Cancer | |
|
|
DSection* | Bayesian MCMC-based estimation from priors on cell proportions [31] | informatics.systemsbiology.net/DSection Matlab + CellMix | - | |
|
|
DSA | Digital Sorting Algorithm: complete deconvolution using a set of linear equations and quadratic programming [19] | R:dsa + CellMix | - | |
|
|
deconf | Alternate least-square NMF method, using heuristic constraints [33] | R:deconf + CellMix | Blood | |
|
|
ssNMF | Semi-supervised NMF algorithms, enforcing marker gene expression patterns [32] | CellMix | - |
denotes methods that provide built-in capabilities for statistical testing or confidence interval estimation. For each method, the type of software implementation is indicated. For R, the name of the package implementing the method is listed, if available. CellMix, is an R package compiling together in a standardized interface many of the published computational deconvolution methodologies for gene expression data. The tissue for which a method was developed and may be expected to perform best is also listed.