ColocML: machine learning quantifies co-localization between mass spectrometry images.

ABSTRACT: MOTIVATION:Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development. RESULTS:We present ColocML, a machine learning approach addressing this gap. With the help of 42 imaging MS experts from nine laboratories, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using term frequency-inverse document frequency and deep neural netw

SUBMITTER: Ovchinnikova K 

PROVIDER: S-EPMC7214035 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature