alsData: Amyotrophic Lateral Sclerosis (ALS) dataset

In fernandoPalluzzi/SEMdata: Datasets for SEM-based network analysis using the SEMgraph R package

Amyotrophic Lateral Sclerosis (ALS) dataset

Description

Expression profiling through high-throughput sequencing (RNA-seq) of 139 ALS patients and 21 healthy controls (HCs), from Tam et al. (2019).

Usage

alsData

Format

alsData is a list of 4 objects:

1. "graph", ALS graph as the largest connected component of the "Amyotrophic lateral sclerosis (ALS)" pathway from KEGG database;

2. "exprs", a matrix of 160 rows (subjects) and 17695 columns (genes). Raw data from the GEO dataset GSE124439 (Tam et al., 2019) were pre-processed applying batch effect correction, using the sva R package (Leek et al., 2012), to remove data production center and brain area biases. Using multidimensional scaling-based clustering, ALS-specific and an HC-specific clusters were generated. Misclassified samples were blacklisted and removed from the current dataset;

3. "group", a binary group vector of 139 ALS subjects (1) and 21 healthy controls (0);

4. "details", a data.frame reporting information about included and blacklisted samples.

Source

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124439

References

Tam OH, Rozhkov NV, Shaw R, Kim D et al. (2019). Postmortem Cortex Samples Identify Distinct Molecular Subtypes of ALS: Retrotransposon Activation, Oxidative Stress, and Activated Glia. Cell Repprts, 29(5):1164-1177.e5. <https://doi.org/10.1016/j.celrep.2019.09.066>

Jeffrey T. Leek, W. Evan Johnson, Hilary S. Parker, Andrew E. Jaffe, and John D. Storey (2012). The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. Mar 15; 28(6): 882-883. <https://doi.org/10.1093/bioinformatics/bts034>

Examples

alsData$graph
dim(alsData$exprs)
table(alsData$group)

fernandoPalluzzi/SEMdata documentation built on June 27, 2022, 7:04 a.m.