Different tools for visualization, data processing and analysis (supervised and un-supervised learning, differential expression analysis, etc..) of Spatial Transcriptomics data (can also be used for single cell data).

The package is compatible with the output format of the data generated with the ST Pipeline (https://github.com/SpatialTranscriptomicsResearch/st_pipeline) and give full support to plot the data onto the tissue images but it is compatible with any single cell datasets where the data is stored as a matrix of counts (genes as columns and spot/cells as rows).

This package makes use of the following tools:

t-SNE https://github.com/lvdmaaten/bhtsne

Scran https://github.com/MarioniLab/Deconvolution2016

DESeq2 http://bioconductor.org/packages/devel/bioc/html/DESeq2.html

EdgeR https://bioconductor.org/packages/release/bioc/html/edgeR.html

MIT License, see LICENSE file.

See AUTHORS file.

For bugs, feedback or help you can contact Jose Fernandez Navarro jose.fernandez.navarro@scilifelab.se

The referred matrix format is the ST data format, a matrix of counts where spot coordinates are row names and the genes are column names.

The scripts that allow you to pass the tissue HE image can optionally take a 3x3 alignment file. If the images are cropped to the exact array boundaries the alignment file is not needed unless you want to plot the image in the original image size. If the image is un-cropped then you need the alignment file to convert from spot coordinates to pixel coordinates.

The alignment file should look like :

a11 a12 a13 a21 a22 a23 a31 a32 a33

Where each a correspondonds to a cell of the affine transformation matrix.

Note that the st_analysis package requires R (https://cran.r-project.org/) installed in your system. To install the ST Analsysis packate just clone or download the repository, cd into the cloned folder and type:

python setup.py install

A bunch of scripts will then be available in your system. Note that you can always type script_name.py --help to get more information about how the script works.

To see how spots cluster together based on their expression profiles you can run :

unsupervised.py --counts-table-files matrix_counts.tsv --normalization DESeq2 --num-clusters 5 --clustering KMeans --dimensionality tSNE --image-files tissue_image.JPG --use-log-scale 

The script can be given one or serveral datasets (matrices with counts). It will perform dimesionality reduction and then cluster the spots together based on the dimesionality reduced coordinates. It generates a scatter plot of the clusters. It also generates an image for each dataset of the predicted classes on top of the tissue image (tissue image for each dataset must be given and optionally an alignment file to convert to pixel coordiantes). It also generate a file with the predicted classes for each spot that can be used in other analysis.

To know more about the parameters you can type --help

You can train a classifier with the expression profiles of a set of spots where you know the class (cell type) and then predict on a new dataset of the same tissue. For that you can use the following script :

supervised.py --train-data data_matrix.tsv --test-data data_matrix.tsv --train-casses train_classes.txt --test-classes test_classes.txt --image tissue_image.jpg

This will generate some statistics, a file with the predicted classes for each spot and a plot of the predicted spots on top of the tissue image (if the image and the alignment matrix are given). The script can take several datasets for the training set and it allows to normalize the training and testing data.

To know more about the parameters you can type --help

Use the script st_data_plotter.py. It can plot ST data, it can use filters (counts or genes) it can highlight spots with reg. expressions of genes and it can highlight spots by giving a file with spot coordinates and labels. You need a matrix with the gene counts by spot and optionally the a tissue image and an alignment matrix. A example run would be :

st_data_plotter.py --cutoff 2 --filter-genes Actb* --image tissue_image.jpg --alignment alignment_file.txt data_matrix.tsv

This will generate a scatter plot of the expression of the spots that contain a gene Actb and with higher expression than 2 and it will use the tissue image as background. You could optionally pass a list of spots with their classes (Generated with unsupervised.py) to highlight spots in the scatter plot. More info if you type --help

You can perform a D.E.A using the output from unsupervised.py and a list of groups to where the D.E.A will be performed. The scripts generates different plots and the list of D.E genes in a text file. Basically the script needs one or more matrices of counts with ST data (genes as columns), a tab delimited file with two columns where the first column is a class and the second is a spot (for each input matrix) and finally the list of comparisions to be made from the classes present in the data (for example: 0:1-0:2 0:1-0:5). Where 0 refers to the first input dataseet and 1,2,5 refers to the classes defined the classes file.

differential_analysis.py --input-data stdata.tsv --data-classes spot_classes.txt --condition-tuples 1-2 1-3

To know more about the parameters you can type --help