This is the software pertaining to the publication
Xiaoyu Chen, Timothy R. Hughes & Quaid Morris (2007). . Bioinformatics 23(13):i72-i79.
It should compile and run on any distribution of Linux. The g++ compiler for C++ is required. See
detailed usage instructions.
ISOpure: a general algorithm for gene expression deconvolution and purification
Quon, G., Haider, S., Deshwar, A.G., Cui, A., Boutros, P.C., Morris, Q.D. (2013) Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction. Genome Medicine, 5:29.
ISOpure is an algorithm for reducing inter-tumor variance in gene expression due to
contaminating non-cancerous cells in the tumor samples. ISOpure is generally applicable
to situations in which expression profiles are collected from case and control individuals
and the samples are mixtures of cell types, where the goal is to remove the effect of
cell types not relevant to the disease from the case gene expression profiles. We are
currently working on demonstrating ISOpure for deconvolving gene expression data collected
from blood samples from cases and controls in a juvenile arthritis study, where we are
removing expression variation due to blood cell types not relevant (afflicted) by the disease.
ISOpure can be downloaded from here.
ISOLATE is a statistical model that simultaneously predicts the primary site of origin of cancers and
addresses sample heterogeneity, while taking advantage of new high throughput sequencing
technology that promises to bring higher accuracy and reproducibility to gene expression profiling
experiments. ISOLATE makes predictions de novo, without having seen any training expression
profiles of cancers with identified origin.
The ISOLATE algorithm is now subsumed by the ISOpure algorithm; ISOLATE is now the output of
Step 1 of ISOpure -- see the README.txt file in the ISOpure download to run ISOLATE.
RNAcontext is a motif-finding algorithm to infer binding preferences of RNA binding proteins
(RBP) from experimental
affinity data. The input to RNAcontext consists of a set of sequences
together with their associated structure
annotation profiles (estimated using SFOLD) and
RNA-binding affinity estimates for the given RBP. By learning
a motif model that predicts
the input affinities, RNAcontext infers the sequence and RNA secondary structure preferences
of the RBP.
The code and instructions can be found on