Statistical Assessment of Depth Normalization for Small RNA Sequencing | Zendy

LiXuan Qin | Zendy; Jian Zou | Zendy; Jiejun Shi | Zendy; Ann Lee | Zendy; Aleksandra Mihailović | Zendy; Thalia A. Farazi | Zendy; Thomas Tuschl | Zendy; Samuel Singer | Zendy

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Statistical Assessment of Depth Normalization for Small RNA Sequencing

Author(s) -

LiXuan Qin,

Jian Zou,

Jiejun Shi,

Ann Lee,

Aleksandra Mihailović,

Thalia A. Farazi,

Thomas Tuschl,

Samuel Singer

Publication year - 2020

Publication title -

jco clinical cancer informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.188

H-Index - 12

ISSN - 2473-4276

DOI - 10.1200/cci.19.00118

Subject(s) - normalization (sociology) , false positive paradox , computer science , data set , benchmark (surveying) , data mining , database normalization , statistics , artificial intelligence , pattern recognition (psychology) , mathematics , geodesy , sociology , anthropology , geography

Methods for depth normalization have been assessed primarily with simulated data or cell-line-mixture data. There is a pressing need for benchmark data enabling a more realistic and objective assessment, especially in the context of small RNA sequencing.

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