This data set is the object return by
It contains miRNA count data from 14 samples: 7 control individuals (pc) and
7 patients with Parkinson's disease in early stage (Pantano et al, 2016).
colData to see the experiment design.
Lorena Pantano, 2016-04-07
Data is available from GEO dataset under accession number GSE97285
Every sample was analyzed with seqbuster tool, see http://seqcluster.readthedocs.org/mirna_annotation.html for more details. You can get same files running the small RNA-seq pipeline from https://github.com/chapmanb/bcbio-nextgen.
bcbio_nextgen was used for the full analysis.
files = list.files(file.path(root_path),pattern = "mirbase-ready",
recursive = T,full.names = T)
metadata_fn = list.files(file.path(root_path),
pattern = "summary.csv$",recursive = T, full.names = T)
metadata = read.csv(metadata_fn, row.names="sample_id")
condition = names(metadata)
mirData <- IsomirDataSeqFromFiles(files[rownames(design)], metadata)
Pantano L, Friedlander MR, Escaramis G, Lizano E et al. Specific small-RNA signatures in the amygdala at premotor and motor stages of Parkinson's disease revealed by deep sequencing analysis. Bioinformatics 2016 Mar 1;32(5):673-81. PMID: 26530722
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.