• Bin is a regular NxN square area on the chip. The summary of expression information in the area is the expression information of Bin.
• The values of Bin20, 50, 100 and 200 can be adjusted several times according to the cell size of different tissue types and the downstream analysis effect. Where Bin20 is similar to the size of animal cells, Bin50 and Bin100 are Binsize sizes commonly used for analysis. Bin200 is generally used for quick visualization.

• The main reason is that the calculating logic of the statistical information differs between StereoMap and SAW lasso.
• To analyze lasso statistics, first determine if the specified areas contains the center of a given bin. If they do, the data from that bin is statistically counted; otherwise, it is not counted.

• SAW extracts the sub-matrix of target by transforming the specified contour region into bin 1 (finest granularity) and then to larger bins.     

• Since the sample slices are situated at the chip's edges, the tissue segmentation will extend beyond the matrix, creating the illusion of "spillover".
• Second, when showing the spatial expression distribution, bin sizes such as bin20 or bin50 are commonly used (used as different resolutions to define a single bin of the analysis unit). The coordinates of these bins are primarily dictated by the bin1 point in the top-left corner of the bin areas. Although the net expression are actually arised from a small fraction of the total capture spots within a given bin, the bin will nevertheless be significantly colored to represent the aggregated expression, resulting in the appearance of "expansion" of expression beyond the chip boundaries.

GEF is a hierarchical data file format (essentially an HDF5 file) that allows you to flexibly store dense matrices and sparse visualization matrices of different bin sizes to adapt to multiple scenarios of data archiving and visualization. Depending on the usage scenario and frequency, each GEF may not contain the sparse matrix necessary for visualization to reduce storage pressure.

• SAW >= 8.0
• For more information, please see the [SAW User Manual> Analysis> Outputs > Matrices].

• SAW < 8.0

The use of this function needs to be differentiated according to the SAW version.

SAW >= 8.0:

• For format conversion, use SAW convert. Refer to [SAW User Manual > Tutorials > Format conversion].
• More details regarding the GEF format and other expression matrix formats, refer to [SAW User Manual > Advanced > Expression Matrix Format]

SAW < 8.0:

• Option1: geftools compiled with C ++:
• https://github.com/STOmics/geftools
• Option2: Use the python package gefpy (e. G. v0.6.1):
• https://pypi.org/project/gefpy/
• https://gefpy.readthedocs.io/en/latest/index.html
• pip install gefpy==0.6.1
• Option3: If SAW sif is installed (e. G. v5.1.3):
• https://hub.docker.com/repository/docker/stomics/saw
• singularity exec SAW_v5.1.3.sif cellCut

• Please use singularity 3.8 and above

export HDF5_USE_FILE_LOCKING=FALSE
## gef2gem using geftools
geftools view -i <SN>.gef -o <SN>.gem -s <SN>
# -i input square bin GEF, e.g.SN.raw.gef or SN.gef
# -o output GEM
# -s SN

## gef2gem using gefpy
python
>>> from gefpy.bgef_reader_cy import BgefR
>>> bgef=BgefR(filepath='<SN>.gef',bin_size=200,n_thread=4)
>>> bgef.to_gem('<SN>.bin200.gem')

## gef2gem using SAW sif
## export SINGULARITY_BIND="/path/to/input/dir,/path/to/output/dir"
singularity exec SAW_v5.1.3.sif cellCut view -i <SN>.gef -o <SN>.gem -s <SN>

## cgef2cgem
geftools view -i <SN>.cellbin.gef -o <SN>.cellbin.gem -d <SN>.raw.gef -s <SN>
# -i input cellbin GEF
# -o output cellbin GEM
# -d input square bin GEF, e.G. sn. raw. GEF or Sn. GEF
#-S SN

# # Gem2gef
GEF tools bgef -i <SN>.gem-O <SN>. GEF-B 1,20,50 -O Transcriptomics
#-I input square bin gem
#-O output Square bin GEF
#-B bin sizes seqarate by comma, default: 1,10,20,50,100,200,500
# -O OMICS name

SAW >= v8.0:

• Use the 'SAW reanalyze cluster' process to set the parameter '-- bin-size' to perform cluster analysis of the specified bin size.

• Please refer to the [SAW User Manual > Tutorials > Secondary analysis > Clustering].

• Different binsize options can be set through the '-s' parameter in the spatialCluster pipeline. However, note that the clustering results directly output by SAW only support Bin200 result display by default, which is only used as a rough reference.
• If you want to do more precise clustering, recommend you use Stereopy for downstream analysis, this part of the analysis is outside the SAW.

Note: This feature is not applicable to issues caused by human factors such as incorrect parameter settings or input data errors.

• The magnification of the objective lens identification of the microscope may differ from the actual magnification due to various reasons, resulting in the image imaged by the microscope not being the real 10x. While the image QC algorithm of stereoomap gadget module is more prone to misjudgment or false detection when the magnification difference is too large,Therefore, when similar problems occur, it is necessary to check whether the actual magnification of the microscope camera meets the requirements.
• The acceptable actual magnification range of the image QC algorithm of the stereoomap gadget module is: [0.28~1] µM/pixel. Obtain the necessary information through the following steps.
• Get the magnification manually. Take a picture of a chip. It is known that the specification of the chip is 1cm * 1cm. Use imageJ to select the width of the chip to obtain the number of chip pixels in the image, and the magnification equals to the theoretical width (µm) divided by the actual measured width (pixel).
• If your actual magnification is not within the acceptable range of the algorithm: it is recommended to first confirm whether the magnification of the selected objective lens is 10X, and then contact the microscope engineer for adjustment.
• Here is the example, please see the picture. Theoretically, the width of an S1 chip is 1cm. As shown, the actual measured chip width is 20065.36 pixel, and the actual magnification Scale = 1 cm / 20065.36 pixel = 10,000 µm / 20065.36 pixel = 0.498 µm/pixel
• 0.498 µm/pixel within the acceptable actual magnification Scale [0.28~1]  µM/pixel of the QC algorithm.

For SAW < v8.0：

• The presence of a fluorescent signal in immunofluorescence image indicates the presence of cells expressing the protein of interest in that region. A strong fluorescent signal indicates that the cells expressing the protein of interest are numerous and dense.
• The 'register' module in the SAW process uses an image processing algorithm to automatically calculate the threshold according to the grayscale of the IF image, filter and extract the foreground area of the image, and eliminate the background area with low brightness. Then use the mask file extracted according to the threshold to access the 'tissueCut' module to obtain the expression matrix of the corresponding area of the IF image.
• IF you are not satisfied with the segmentation result obtained by the grayscale threshold automatically calculated according to the algorithm, you can manually adjust the threshold value of the IF image through the "tissue segmentation" of StereoMap image processing module, and manually obtain the image segmentation result.