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Comment
. 2017 Jul 5;18(1):127.
doi: 10.1186/s13059-017-1256-5.

Revisit linear regression-based deconvolution methods for tumor gene expression data

Bo Li  1   2 , Jun S Liu  3 , X Shirley Liu  4
Affiliations
Comment

Revisit linear regression-based deconvolution methods for tumor gene expression data

Bo Li et al. Genome Biol. .
No abstract available

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Figures

Fig. 1
Fig. 1
Simulations showing negative associations in CIBERSORT estimates. a, b CIBERSORT reported a negative correlation between unrelated features, CD8 T cells and neutrophils. The fractions of features were independently sampled from Uniform(0,0.5). c, d CIBERSORT reported a stronger negative association between closely correlated features, naïve and memory B cells; the sampling procedure used in a, b was applied. Simulated data were sent to the CIBERSORT online server using LM22 as a reference matrix and default parameters
Fig. 2
Fig. 2
Expression of LM22 signature genes in the malignant cells. For each cancer type, the expression level of each LM22 gene was compared with tumor purity. Positive correlation indicates higher expression in the malignant cells. Genes were ranked by their correlations with purity. As described in the text, a positive correlation between gene expression and tumor purity indicates that the gene is expressed in the malignant cells. Cancer name abbreviations follow The Cancer Genome Atlas nomenclature. BLCA bladder cancer, GBM glioblastoma, LGG lower-grade glioma, OV ovarian cancer, PCPG pheochromocytoma and paraganglioma, SKCM melanoma, STAD stomach cancer, UCEC uterine endometrial cancer
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References

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