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Table 1 An overview of computational deconvolution algorithms for RNA profiles

From: ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles

Method Ref. Input Output Clinical data? Availability
    Prop. Expr. Individual Cancer Normal blood Other R CellMix MATLAB Other
ISOpure (Quon) [33] tumour & unmatched normal \(\checkmark \) \(\checkmark \) \(\checkmark \) \(\checkmark \)    \(\checkmark \)   \(\checkmark \)  
DeMix (Ahn) [32] tumour & unmatched normal \(\checkmark \) \(\checkmark \) \(\checkmark \)     \(\checkmark \)    
Clarke [30] paired mixed & pure profiles \(\checkmark \)   \(\checkmark \)     \(\checkmark \)    
Gosink [31] mixed profiles and known profile of one constituent \(\checkmark \)   \(\checkmark \)        
DeconRNASeq (Gong) [18] profiles of constituents \(\checkmark \)       \(\checkmark \)    
Gong [19] cell-type specific gene signatures \(\checkmark \)        \(\checkmark \)   
Abbas [20] cell-type specific gene signatures \(\checkmark \)     \(\checkmark \)    \(\checkmark \)   
Wang M. [21] cell-type specific gene signatures \(\checkmark \)          
Lu [22] cell-type specific gene signatures \(\checkmark \)          *
PERT (Qiao) [46] reference profiles of constituents \(\checkmark \)   \(\checkmark \)      \(\checkmark \)
ESTIMATE (Yoshihara) [47] prior data used to derive cell-type specific gene signatures \(\checkmark \)    \(\checkmark \)    \(\checkmark \)    
DSection (Erkkilä) [12] prior knowledge of proportions \(\checkmark \)       \(\checkmark \) \(\checkmark \)  
csSAM (Shen-Orr) [13] proportions of constituents   \(\checkmark \)    \(\checkmark \)   \(\checkmark \) \(\checkmark \)   
Bar-Joseph [14] proportions of consitutents, one expression profile   \(\checkmark \)   \(\checkmark \)   \(\checkmark \)     
Ghosh [16] proportions, tumour & unmatched normal   \(\checkmark \)   \(\checkmark \)    *    
Stuart [17] proportions of constitutents   \(\checkmark \)   \(\checkmark \)       
TEMT (Li) [48] prior knowledge of proportions, paired mixed-pure profiles    \(\checkmark \)        \(\checkmark \)
DSA (Zhong) [23] cell markers \(\checkmark \) \(\checkmark \)   \(\checkmark \)    \(\checkmark \) \(\checkmark \)   
ssNMF (Gaujoux) [25] cell markers \(\checkmark \) \(\checkmark \)    \(\checkmark \)    \(\checkmark \)   
PSEA (Kuhn) [24] cell markers \(\checkmark \) \(\checkmark \)     \(\checkmark \) \(\checkmark \)    
deconf (Repsilber) [26] cell markers \(\checkmark \) \(\checkmark \)    \(\checkmark \)   \(\checkmark \) \(\checkmark \)   
Tolliver [49] tumour profile, number of constituents \(\checkmark \) \(\checkmark \)   \(\checkmark \)       
Roy [50] prior estimate of number of constituents \(\checkmark \) \(\checkmark \)         
Lähdesmäki [15] mixed expression profiles \(\checkmark \)         
Venet [27] mixed expression profiles, number of constituents \(\checkmark \) \(\checkmark \)   \(\checkmark \)       
UNDO (Wang N.) [51] mixed expression profiles \(\checkmark \) \(\checkmark \)   \(\checkmark \)    \(\checkmark \)    
  1. Most of the algorithms are applied to microarray mRNA abundance data, although TEMP and ESTIMATE use high-throughput RNA-Seq data and ISOpure and DeconRNASeq can be applied to both [52]. The possible outputs of the algorithms are proportions of constituent cell-types (Prop.), average expression profiles (Expr.), or patient-specific expression profiles (Individual Profile) of constituent cell-types. The two main sources of clinical data were cancer-related gene expression data (including human Hodgkin’s lymphomas) or normal blood expression data. PSEA was applied to expression data from patients with Huntington’s disease, and Bar-Joseph also studied cell cycle synchronized foreskin fibroblast cells. In terms of availability, the summary package CellMix [28] is also an R package but is listed as a separate category. The only algorithms not available for either R or MATLAB are PERT (Octave) and TEMT (Python). Algorithms which were described as using built-in MATLAB or R functions were not included, as reproducible example code is not available for them. The currently available source code is summarized in Additional file 2.
  2. Notes:
  3. Prior information about proportions or expressions is needed, but these values are re-estimated during the execution of the algorithm. For PERT, the individual profiles are adjusted (perturbed) versions of the reference profiles.
  4. *The original code for Lu (Java-based) [22] and Ghosh [16] is no longer available.