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FAQs
Below we will collect all frequently ask questions and provide answers. We will also collect tips and tricks that will help you in applying slideToolKit
.
- You can use many scripts individually on single WSIs or a folder containing many WSIs.
- Most slides are scanned and will have a barcode sticker. This allows for the scanner to automatically create a proper filename. You can also do this manually using
slideRename.py
. Check it out. - Sometimes you want to quickly get PNGs of a specific magnification for one or multiple images, or perhaps you want to see the thumbnail image. You can use the scripts
slideExtract.py
,slideMacro.py
, andslideThumbnail.py
to do just that. - You may want to inspect the metadata of a given image,
slideInfo.py
provides for that.
Below some initial questions and answers. You have a question? Create an Issue and we'll add it the list below.
slideToolKit
is specifically designed to process large quantities of WSIs. So, we designed it to work optimally on a Linux-based system which used SLURM to control and submit jobs. That said, almost all scripts are made such that they can be used individually on any Linux-based system with bash
and python3
installed. For macOS users we even created a standalone version of slideQuantify
to be run locally: slideQuantifyOSX
.
Good question, ambivalent answer. Depending on whether you use the bash
- or the python
-version of slide2Tiles
, the tiling process takes the longest when running slideQuantify
. In the event you think it is likely you will rerun CellProfiler
analyses it is recommended to keep the masked and tiled images. A downside might be the use of space.
The reason for the bash
-version of slide2Tiles
to take such a long time is also partly related to the magnification that is used, by default this is set at 20x in slideQuantify
. You can also run the slideToolKit
step-by-step providing more control, for instance you can use --layer
in slide2Tiles
to select the specific magnification.
Many of the computational tools to process, manage, and analyse large quantities of WSIs are written specifically for Linux-based systems.
Licence. The MIT License (MIT): http://opensource.org/licenses/MIT.
Copyright (c) 2014-2023, Bas G.L. Nelissen & Sander W. van der Laan, UMC Utrecht, Utrecht, the Netherlands.
Introduction
General instructions
slide2Tiles
slideAppend.sh
slideAppendGCT.sh
slideConvert
slideDirectory
slideDupIdentify.py
slideEMask
slideEntropySegmentation.py
slideExtract.py
slideExtractTiles.py
slideInfo
slideInfo.py
slideJobChecker
slideLookup
slideMacro
slideMacro.py
slideMask
slideNormalize
slideRename
slideRename.py
slideThumb
slideThumb.py
slideQuantify_v1
slideQuantify_v1_1_expresshist_mask.sh
slideQuantify_v1_2_expresshist_tile.sh
slideQuantify_v1_3_tile_normalizing.sh
slideQuantify_v1_4_cellprofiler.sh
slideQuantify_v1_5_wrapup.sh
slideQuantify_v2
slideQuantify_v2_1_entropy_segmentation.sh
slideQuantify_v2_2_extract_tiles.sh
slideQuantify_v2_3_tile_normalizing.sh
slideQuantify_v2_4_cellprofiler.sh
slideQuantify_v2_5_wrapup.sh
slideQuantifyOSX
slideQuantify_cellprofiler.sh
slideQuantify_mask.sh
slideQuantify_normalizing.sh
slideQuantify_tiling.sh
slideQuantify_wrapup.sh
Conda version (default/preferred)
Homebrew version
Rocky 8 Conda version (default/preferred)
Ubuntu 16.04 LTS
Ubuntu 12.04
CentOS7 Conda version with modules
Administrator version