Sparsifying penalties for high-dimensional regression: comparing performance in genomic data.

Benchmarking datasets used to support the findings of the study "Sparsifying penalties for high-dimensional regression: comparing performance in in genomic data".  Original genomic data was extracted from subsets of the Gene Expression Omnibus datasets "GSE73002", "GSE137140", "GSE103322", "GSE146026" and  "GSE89567". Gene names and observation names were removed. Data was preprocessed with non-paranormal transformation and QR factorisation to ensure full rank. 

Please cite the authors of the original datasets in any publication or use of this data

GSE73002:

Shimomura A, Shiino S, Kawauchi J, Takizawa S, Sakamoto H, Matsuzaki J, et al. Novel combination of serum microRNA for detecting breast cancer in the early stage. Cancer Sci. 2016;107(3):326–34.

GSE137140:

Asakura K, Kadota T, Matsuzaki J, Yoshida Y, Yamamoto Y, Nakagawa K, et al. A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy. Commun Biol. 2020;3(1).

GSE103322:

Puram S V., Tirosh I, Parikh AS, Patel AP, Yizhak K, Gillespie S, et al. Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer. Cell. 2017;171(7):1611-1624.e24.

GSE146026:

Izar B, Tirosh I, Stover EH, Wakiro I, Cuoco MS, Alter I, et al. A single-cell landscape of high-grade serous ovarian cancer. Nat Med. 2020;26(8):1271–9.

GSE89567:

Venteicher AS, Tirosh I, Hebert C, Yizhak K, Neftel C, Filbin MG, et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science (80- ). 2017;355(6332).