Open AccessStatistical 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 , data mining , benchmark (surveying) , 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.