Single object profiles regression analysis (SOPRA): a novel method for analyzing high-content cell-based screens

ABSTRACT:

Background

High-content screening (HCS) experiments generate complex data from multiple object features for each cell within a treated population. Usually, these data are analyzed by using population-averaged values of the features of interest, increasing the amount of false positives and the need for intensive follow-up validation. Therefore, there is a strong need for novel approaches with reproducible hit prediction by identifying significantly altered cell populations.

Results

Here we describe SOPRA, a workflow for analyzing image-based HCS data based on regression analysis of non-averaged object features from cell populations, which can be run on hundreds of samples using different cell features. Following plate-wise normalization, the values are counted within prede

SUBMITTER: Gurumurthy R 

PROVIDER: S-EPMC9587636 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature