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LieberInstitute / Visium_SPG_AD

Visium SPG AD project (n = 10) using Visium Spatial Proteogenomics (Visium-SPG) on dissections from the inferior temporal cortex (ITC) from Alzheimer's disease cases and controls.
https://research.libd.org/Visium_SPG_AD
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Segment cells, plaques and tangles with dotdotdot #5

Closed lcolladotor closed 2 years ago

lcolladotor commented 3 years ago

Once the images from #3 are ready and we have run spaceranger in #4, Maddy can use VistoSeg to compute the number of cells per spot as shown at http://research.libd.org/VistoSeg/step-4-gui-to-count-nuclei-in-a-visium-spot.html.

madhavitippani commented 3 years ago

We will be using dotdotdot for segmenting signal as this is IF.

lcolladotor commented 3 years ago

Ok! Thanks for the clarification =)

madhavitippani commented 2 years ago

The background suppression step in dotdotdot removes artifacts with low intensity in the MAP2 and GFAP channel (which actually has to be masked out from Abeta and pTau channels). So I adapted new segmentation code, made the functions segmentIF and refineIF. The refineIF allows the user to manually set thresholds on Abeta, GFAP, MAP2, and pTau channels if the user feels the automatic thresholds generated from segmentIF do not work well. The final outputs of these functions are for every capture area, described below.

  1. VIFAD1_V10A27-004_A1_segmentation.mat = final segmentation of (capture area A1, experiment/slide 1) all channels stored in mat format(reuired input for countSpots and spotspotcheck functions).
  2. VIFAD1_V10A27-004_A1_test.png = a test field picked by @shkwon17 that represents the entire capture area, displays the segmentation results. The image has 4 rows of tiles (raw, segmented, masked, size thresholded) and the columns correspond to different channels (Abeta, DAPI, GFAP, Lipofuscin, MAP2, pTau).
  3. VIFAD1_V10A27-004_A1_Abeta.png = segmented binary image of the entire Abeta channel
  4. VIFAD1_V10A27-004_A1_pTau.png = segmented binary image of the entire pTau channel