UNDO: a Bioconductor R package for unsupervised deconvolution of mixed gene expressions in tumor samples
暂无分享,去创建一个
Robert Clarke | Zhen Zhang | Niya Wang | Jianhua Xuan | Yue Joseph Wang | Ie-Ming Shih | Ting Gong | Lulu Chen | Douglas A. Levine | I. Shih | D. Levine | Y. Wang | J. Xuan | R. Clarke | Lulu Chen | Zhen Zhang | Niya Wang | Ting Gong
[1] M. Junttila,et al. Influence of tumour micro-environment heterogeneity on therapeutic response , 2013, Nature.
[2] Jianfeng Xu,et al. BACOM: in silico detection of genomic deletion types and correction of normal cell contamination in copy number data , 2011, Bioinform..
[3] E. Gehan,et al. The properties of high-dimensional data spaces: implications for exploring gene and protein expression data , 2008, Nature Reviews Cancer.
[4] Mark M. Gosink,et al. Electronically subtracting expression patterns from a mixed cell population , 2007, Bioinform..
[5] Jennifer Clarke,et al. Statistical expression deconvolution from mixed tissue samples , 2010, Bioinform..
[6] Wenyi Wang,et al. DeMix: deconvolution for mixed cancer transcriptomes using raw measured data , 2013, Bioinform..
[7] Eric Moreau,et al. A generalization of joint-diagonalization criteria for source separation , 2001, IEEE Trans. Signal Process..
[8] A. McKenna,et al. Absolute quantification of somatic DNA alterations in human cancer , 2012, Nature Biotechnology.
[9] Chong-Yung Chi,et al. Tissue-Specific Compartmental Analysis for Dynamic Contrast-Enhanced MR Imaging of Complex Tumors , 2011, IEEE Transactions on Medical Imaging.
[10] G. Getz,et al. Inferring tumour purity and stromal and immune cell admixture from expression data , 2013, Nature Communications.