testData1


  Gene expression datasets for 111 lung cancer patient samples, including 53 cases of lung adenocarcinoma and 58 cases of lung squamous carcinoma.


datasets

ASSIGN is a computational tool to evaluate the pathway
deregulation/activation status in individual patient samples.
ASSIGN employs a flexible Bayesian factor analysis approach
that adapts predetermined pathway signatures derived either
from knowledge-based literatures or from perturbation
experiments to the cell-/tissue-specific pathway signatures.
The deregulation/activation level of each context-specific
pathway is quantified to a score, which represents the extent
to which a patient sample encompasses the pathway
deregulation/activation signature.

Ying Shen

ASSIGN

Adaptive Signature Selection and InteGratioN (ASSIGN)

testData1 function

Data frame with 1000 genes/probes (rows) and 111 samples (columns) 

Format

Bild et al. (2006) Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature, 439, 353-357.

Source

Gene expression datasets for 111 lung cancer patient samples, including 53 cases of lung adenocarcinoma and 58 cases of lung squamous carcinoma.

testData1: Gene expression profiling from cancer patients (test dataset)

Description

Usage

Arguments

Format

Source