huntingtons

Huntington's disease blood transcriptomics

Source: Borovecki F et al., 2005. Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease. Proc Natl Acad Sci U S A. PMID: 16043692 | GEO: GSE8762

Background

Huntington's disease (HD) is a fatal neurodegenerative disorder caused by an expanded CAG repeat in the HTT gene. This study profiled blood from HD patients and matched controls to identify peripheral expression signatures that could serve as biomarkers for disease progression and therapeutic response.

Experimental design

Organism	Human
Tissue	Peripheral blood lymphocytes
Conditions	Ctrl (10), HD (12)
Samples	22
Platform	Affymetrix Human Genome U133 Plus 2.0 Array (GPL570)

File: huntingtons.tsv

• Shape: 54,675 genes × 22 samples
• Scale: $\log_2$ (no transformation needed)

Notes

Two-condition comparison; ports a standard differential-expression pipeline cleanly.

Load

Python

import pandas as pd

URL = ("https://media.githubusercontent.com/media/ahmedmoustafa/"
       "gene-expression-datasets/main/datasets/huntingtons/huntingtons.tsv")
data = pd.read_table(URL, index_col=0)
data.shape

R

url <- paste0("https://media.githubusercontent.com/media/ahmedmoustafa/",
              "gene-expression-datasets/main/datasets/huntingtons/huntingtons.tsv")
data <- read.delim(url, row.names = 1, check.names = FALSE)
dim(data)

First exploration

Beyond the load, get an immediate sense of the data:

Python

data.describe().T[["min", "50%", "max"]].head(8)

R

sapply(data[, 1:min(8, ncol(data))],
       function(x) c(min = min(x), median = median(x), max = max(x)))

A min near zero with max in the thousands indicates raw intensities (apply log2 before any test). A min near 0 to 5 with max around 10 to 18 indicates log2-transformed data, ready for analysis. A negative min indicates a per-gene-centered or log-ratio matrix (deviations rather than absolute expression).