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=Analyzing RNAscope Images= *Genes **PDE1A (expect in layers 2,4, and 5) [[File:ABI_ISH_PDE1A.JPG|350px]] **SLC17A7 (expect everywhere but not uniform) [[File:ABI_ISH_SLC17A7.JPG|350px]] **OLFM1 **RELN (expect in layer 1) [[File:ABI_ISH_RELN.JPG|350px]] *Three samples **PDE1A + OLFM1 + SLC17A7 **PDE1A + OLFM1 **RELN + SLC17A7 ==Strategy== **Images are RGB so need to convert to grayscale ***Green channel can just use green RGB values ***Magenta channel has equal Red and Blue RGB values at every pixel so just use blue RGB values ***Yellow channel has a lot of red background and even though green RGB values are lower at spots it is high enough; use green RGB values **Divide each tile (which is approx 5,600 x 49,660 pixels) into 48 2048x2048 sub-images ***With long edge aligned vertically, cut horizontally into 24 sub-images every 2048 pixels ***Cut vertically the middle 4096 pixels into two 2048 strips (indices 786-2833 and 2834-4881) **Count number of nuclei and spots for each gene in each sub-image **Expect to see some pattern or trend in vertical direction **Left and right sub-images are like replicate samples and should be similar ===Counting transcript dots=== [[Media:File:FindRNAscopeSpots.txt | FindRNAscopeSpots.m]] *Apply gaussian filter **Tried sigma = 0.5, 0.75, 0.8, and 1 and found 0.75 works best *Apply threshold **Tried 10 and 20 and found 20 to be best (avoids false positives from next step) *Find local maximum (peaks) *Count with bwconncomp ====Validate==== *c3_3-1 cropped (increased and brightness and contrast) -> marked local max after filtering [[File:C3 3-1 cropped.jpg|350px]] -> [[File:C3 3-1 cropped gaussfilt75.jpg|350px]] *c4_1-2 cropped -> marked local maximum after filtering [[File:C4 1-2 cropped.jpg|350px]] -> [[File:C4 1-2 cropped gaussfilt75.jpg|350px]] ===Counting nuclei=== [[Media:NucC.txt | NucC.m]] *nucC(Image,40) *Erode and dilate to remove noise within nuclei **Strel size = 40 *Convert to binary image *Segment with watershed transformation *NucC.m counts every "little" nuclei while another script: count_nuclei_63x.m counts only large "typical" nuclei **Using NucC since I think it more accurately represents number of cells in the image ==Results== *Divided transcript counts by number of nuclei in each 2048x2048 image *Calculated average and standard error of the two replicates (left and right images) *Plotted average and used error bars to represent SEM ===RELN+SLC17A7=== *Tile 1 **RELN has expected spike **Actually only a couple cells had all the RELN expression, not uniform in that image layer [[File:R%2BS_Tile_1_Spots_Per_Nuclei.JPG | 350px]] *Tile 2 **Y-slices 20-24 had very few or no cells because tissue ended there so removed them [[File:R%2BS_Tile_2_Spots_Per_Nuclei.JPG | 350px]] [[File:R%2BS_Tile_2_Spots_Per_Nuclei_RELNzoomed.JPG | 350px]] ===PDE1A+OLFM1=== *Yslice 24 had no nuclei **Yslices 20-24 had very little nuclei and nuclei counting algorithm tended to overestimate for these images, sometimes by >100% *I notice similarities in spikes at Y-Slice 5, 10, 15, and 18-19 *Tile 1 [[File:P%2BO_Tile_1_Spots_Per_Nuclei.JPG|350px]] [[File:P%2BO_Tile_1_Spots_Per_Nuclei_PDE1Azoomed.JPG|350px]] *Tile 2 [[File:P%2BO_Tile_2_Spots_Per_Nuclei.JPG|350px]] [[File:P%2BO_Tile_2_Spots_Per_Nuclei_PDE1Azoomed.JPG|350px]] ===PDE1A+OLFM1+SLC17A7=== *Tile 1 [[File:P%2BS%2BO_Tile_1_Spots_Per_Nuclei.JPG|350px]] [[File:P%2BS%2BO_Tile_1_Spots_Per_Nuclei_zoomedPDE1A.JPG|350px]] *Tile 2 [[File:P%2BS%2BO_Tile_2_Spots_Per_Nuclei.JPG|350px]] [[File:P%2BS%2BO_Tile_2_Spots_Per_Nuclei_zoomedPDE1A.JPG|350px]]
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