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

Anghel, Catalina V; Quon, Gerald; Haider, Syed; Nguyen, Francis; Deshwar, Amit G; Morris, Quaid D; Boutros, Paul C

doi:10.1186/s12859-015-0597-x

BMC Bioinformatics

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
					profile
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	\(\checkmark \)	\(\checkmark \)		\(\checkmark \)			\(\checkmark \)

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.
Notes:
^†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.
^*The original code for Lu (Java-based) [22] and Ghosh [16] is no longer available.

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