MAUI (MBI Analysis User Interface)—An image processing pipeline for Multiplexed Mass Based Imaging
Fig 4
Removal of noise using a K-Nearest-Neighbor (KNN) algorithm.
(A-B) Signal density at each pixel is determined using its K nearest neighbor counts. Shown is an example of the 5 nearest counts (A) and their distances (B) to the central pixel. (C) An example of staining of Histone H3 with noise surrounding the nuclear signal. (D) Shown is the frequency of the average distance to the 25 nearest counts for all pixels in (C). Signal is characterized by short distances (high density), whereas noise is characterized by long distances (low density). The red line is the chosen threshold for denoising, and pixels with large distances to their neighbors will be set to zero. (E) Histone H3 signal after denoising. (F) The average KNN-distribution for Histone H3, same as in (D), after denoising. (G-H) Examples of the resulting image of Histone H3 when varying the algorithm parameters, including the K-value (G) and Threshold (H). (I-J) Examples of the resulting image of a membranous protein, CD8, when varying the algorithm parameters, including the K-value (I) and Threshold (J). (K) The GUI enables to rapidly evaluate these parameters across many points and channels.