Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules
Fig 1
(a) Summary of model learning and classification pipeline. (b) Illustration of coordinate system for probability density function. For each pixel in an image, distance between it and the nearest point on the nuclear membrane (L1) and between it and the nearest point on the cell membrane (L2) are calculated and used to calculate the radial position (r) as L1/(L1+L2). In addition, the distance to the nearest point on a segmented microtubule (d) and the angle between the pixel and the major axis of the cell (α) are calculated. (c) A two-color image of a vesicular protein (TFRC, transferrin receptor, green) and microtubules (red) in a U-2OS cell. (d) Segmented image of microtubules (red) and puncta (green). (e) Remaining background intensity.