Data normalization considerations for digital tumor dissection.

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Benchmarking: contexts and details matter Revisit linear regression-based deconvolution methods for tumor gene expression data.Quantifying tumor-infiltrating immune cells from transcriptomics data.

P2860

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P2860

Data normalization considerations for digital tumor dissection.

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2017 nî lūn-bûn

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2017年の論文

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2017年学术文章

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2017年学术文章

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2017年学术文章

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2017年学术文章

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2017年学术文章

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2017年學術文章

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2017年學術文章

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2017年學術文章

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name

Data normalization considerations for digital tumor dissection.

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Data normalization considerations for digital tumor dissection.

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Data normalization considerations for digital tumor dissection.

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P1476

Data normalization considerations for digital tumor dissection

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P2093

Andrew J Gentles

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Chih Long Liu

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Maximilian Diehn

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P2860

Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis

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Optimal deconvolution of transcriptional profiling data using quadratic programming with application to complex clinical blood samples

Adjusting batch effects in microarray expression data using empirical Bayes methods

The immune contexture in human tumours: impact on clinical outcome

Inferring tumour purity and stromal and immune cell admixture from expression data

Immune response in silico (IRIS): immune-specific genes identified from a compendium of microarray expression data.

Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus

Cell intrinsic alterations underlie hematopoietic stem cell aging.

Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer.

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s13059-017-1257-4

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scholarly article

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10.1186/S13059-017-1257-4

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Aaron M. Newman

A Alizadeh

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2017-07-05T00:00:00Z

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1090384317

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Data normalization considerations for digital tumor dissection.

about

P2860

P2860

Data normalization considerations for digital tumor dissection.

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