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Created with Raphaël 2.2.021Sep16156327Aug2411105229Jul282721131298652125Jun22211716151110843128May2120181374328Apr221916update sample data and readme to be generic genotypemastermasterupdate READMEupdate README with hexstickerupdate R source, documentation and extdata to submitted versionupdate count_foci documentationupdate documentation for auto_crop_fastupdate documentation for auto_crop_fastupdate documentation for auto_crop_fastcrowded foci case now calculates mask with local backgroundadd package logocount_foci includes crisp criteria which user can set. No errors or warnings with BiocCheck()count_foci includes crisp criteria which user can set. No errors or warnings with BiocCheck()put on githubadd package contents from gitlab development versiontimeout error fixed. Colorlabels not used when the appropriate size and shaped cell objects are left in the maskchecks that number of objects is > 0 before plotting blobs to avoid vector memory exhausted errorfixed vector memory exhausted error. Seems that calling plot(colorlabels(retained)) on a BW mask with a single object causes itauto_crop_fast now has a new input called cropping_factor. It defaults to 1 (which is how the size of the cropped square around the blob was previously determined, using 1* characteristic blob radius). Likely need to set e.g. cropping_factor = 1.3, once blob_factor has been chosen etcauto_crop_fast has watershed optioncount_foci has individual mask routines. More modular and closer to bioconductor standards. Mask routines can be called with different settings for exceptional cases e.g. after removing XY, low background and low signalcount_foci has separate append_data_frame functioncount_foci now shows individual maskscount_foci now shows individual maskscoint_foci has option minimum fociauto_crop and get_pachytene gives same results for any input resolution, i.e. images found at the end of the pipeline are all the sameannotate functions their own helper functions. Amplification factors for RGB outputadded option to specify an output path path_out in auto_crop_fast, get_pachytene in case user wants output crops in a different place to the original image filesadd jupyter notebook, update python scriptadd instructions for installing python/launching jupyter notebook for data preparationadd instructions for installing python/launching jupyter notebook for data preparationget_pachytene outputs red and green channel images into a folder called pachytene-RGBauto_crop_fast outputs a folder called crops-RGB with strand and foci channeladd an inout parameter artificial_amp_factor in count_foci, which amplies the foci channel for annotation purposes only. Defaults to 1measure_distances_general prints file and shows original cell image before displaying individual strandsoverlay the original dna and foci channelsoverlay the original dna and foci channelsadd filename and 2 channel original images to annotations in measure_distances_generalinclude long description of main four functionstyposupdate vignette to emphasise that count_foci and measure_distances_general find the coincident foci mask with the exact same input parameters, and it is much faster to play around with/ calibrate your input parameters with count_foci, since at the moment measure_distances_general is relatively very slow