TY - JOUR
T1 - Unraveling the Thousand Word Picture
T2 - An Introduction to Super-Resolution Data Analysis
AU - Lee, Antony
AU - Tsekouras, Konstantinos
AU - Calderon, Christopher
AU - Bustamante, Carlos
AU - Presse, Steve
N1 - Funding Information:
C.B. and A.L. acknowledge support from the Nanomachines program (KC1203) funded by the Director, Office of Science, Office of Basic Energy Sciences of the U.S. Department of Energy (DOE) contract no. DE-AC02-05CH11231 (step-finding algorithms), by the National Institute of Health grants R01GM071552 and R01GM032543 (fluorescent protein characterization), and by the Howard Hughes Medical Institute (fluorescent protein counting). S.P. acknowledges the support of NSF MCB 1412259 as well as startup from IUPUI and ASU. C.C. was supported by internal R&D funds from Ursa Analytics, Inc. We thank Ioannis Sgouralis for many helpful suggestions.
Publisher Copyright:
© 2017 American Chemical Society.
PY - 2017/6/14
Y1 - 2017/6/14
N2 - Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light's diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we've termed the interpretation problem.
AB - Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light's diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we've termed the interpretation problem.
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U2 - 10.1021/acs.chemrev.6b00729
DO - 10.1021/acs.chemrev.6b00729
M3 - Review article
C2 - 28414216
AN - SCOPUS:85020781957
SN - 0009-2665
VL - 117
SP - 7276
EP - 7330
JO - Chemical reviews
JF - Chemical reviews
IS - 11
ER -