hints to chess extract

[cgirardot]

Dear,

in the chess extract doc I read we are planning to release a guide to this in the future.
Would you by any chance have a draft or few recommendations to give away ?

Between the matrix bin size, the window size and the different extract parameters; it is a lot to test. Any guidance would be greatly appreciated.

[sgalan]

Dear User, As you pointed out we are planning to add this information in the future. However in the Methods section of the paper you can have some hints. We can determine some parameters for the extraction, which have to be tuned according to the region size analysed: - *Bilateral filter:* used to denoise the image preserving the edges. It averages the pixels based on their spatial closeness and their radiometric similarity, which by default are set to a windows size of 3. The windows size parameter has this flag--windowsize WINDOWSIZE. The spatial closeness is obtained by the Gaussian function of the Euclidean distance between two pixels and its standard deviation. The radiometric similarity uses the Euclidean distance between two color values, which by default is set to the mean value of the matrix. The radiometric similarity has this tag --sigma-spatial. Larger values will average bins with larger differences. - *Median filter:* used to smooth the image. It scans the image using a square shaped array with an area automatically computed according to the matrix size (by default it is 9, to modify it use --size-medianfilter). It computes the median of the pixels within the square shaped array to smooth the signal. Higher values will smooth larger figures, while smaller values will consider subtle signals (i.e. loops). - *Otsu’s method:* used to binarize the image. The algorithm searches for a threshold to separate the pixels in two classes, minimizing the intra-class variance. By default it is calculated using whole matrix values to be more refined. - *Morphological closing:* this last filter is used to remove small dark spots and connect small bright cracks. It helps to remove remaining noise and to enclose structures. By default CHESS uses a square-shaped array of 8 bins (to modify it use the tag --closing-square). Higher values will enclose larger structures, while lower values will preserve smaller or more punctuated signals. If you are interested in this image filters you can check them in here: https://scikit-image.org/ Sincerely, S El mié., 28 oct. 2020 a las 18:38, cgirardot (<[email protected]>) escribió:

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Dear, in the chess extract doc I read we are planning to release a guide to this in the future. Would you by any chance have a draft or few recommendations to give away ? Between the matrix bin size, the window size and the different extract parameters; it is a lot to test. Any guidance would be greatly appreciated. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#11>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADZPOZGBV3YIFDLDLWLTRZ3SNBJJZANCNFSM4TCUWJAQ> .

-- *Silvia Galan Martínez - PhD student * *Centre Nacional d'Anàlisi Genòmica-Centre de Regulació Genòmica (CNAG-CRG)* *Structural Genomics Dpt.* Parc Científic de Barcelona – Torre I Baldiri Reixac, 4 08028 Barcelona Tel +34 9340 20580 Email [email protected]