search

WayScience / phenotypic_profiling

Machine learning for predicting 15 single-cell phenotypes from cell morphology profiles
Creative Commons Attribution 4.0 International
1 stars 3 forks source link

Minor tweaks to sup fig 7 #58

Closed gwaybio closed 6 months ago

gwaybio commented 7 months ago

Main change is focus on AreaShape and Zernike features more closely in panels C and D, and also to combine old panels C and D into a single panel C

Supplementary Figure 7

jump_mitocheck_feature_space_analysis

Supplementary Figure 7. Comparing JUMP and Mitocheck nuclei feature spaces.

(A) Kolmogorov-Smirnov (KS) test results comparing JUMP and Mitocheck per common CellProfiler feature colored by specific CellProfiler feature group. The boxplot whiskers represent the interquartile range of 1,000 permutations of randomly subsampled JUMP single-cells from a single plate (JUMP Pilot plate BR00116991) compared to Mitocheck. Mitocheck and JUMP sample size is the same (n = 2,916). We show both raw and z-score normalized comparisons. (B) The same KS test results focused on AreaShape measurements, which showed the lowest differences in feature distributions across datasets. (C) Comparing variance of JUMP and Mitocheck for CellProfiler features. The dotted lines are the function y=x (anything below is a feature with higher variance in Mitocheck). The top plot showing all features, highlighting AreaShape, while the bottom plot focuses on 30 Zernike polynomial features.

review-notebook-app[bot] commented 7 months ago

Check out this pull request on  ReviewNB

See visual diffs & provide feedback on Jupyter Notebooks.

Powered by ReviewNB