Cluster plot Software User Guide

Environment requirements

R (Seurat, dplyr, tibble, ggplot2, getopt)

Python3 (numpy, cv2, pandas, seaborn, sys, getopt)

spots clustering

Script: Seurat_for_bmkmanu_S1000.R

Description: seurat_for_bmkmanu_S1000.R [-[-indir|i] <character>] [-[-resolution|r] <double>] [-[-MinCell|c] <double>] [-[-MinFeatures|g] <double>] [-[-outdir|o] <character>] [-[-help|h]]

-i|–indir Input directory (the 05.AllheStat/BSTViewer_project/subdata/ level directory in the downloaded data)

-r|–resolution Clustering resolution, default 0.5

-c|–MinCell Minimum number of cells, default 5

-g|–MinFeatures Minimum number of genes, default 100

-o|–outdir Output directory

-h|–help Help documentation

Example: Rscript seurat_for_bmkmanu_S1000.R

-i /home/05.AllheStat/BSTViewer_project/subdata/L13_heAuto/

-o /home/outdir

-r  0.5

-c  5

-g  100

Output:

outdir/

├ UMAP.p*

├ cluster.csv (required input for the next plotting step) └─t-SNE.p*

Plotting

Script: bmplot.py

Description: python bmplot.py

-e||–he_file Trimmed H&E image

-p||–pos_file Spatial positions of barcodes (barcodes_pos.tsv.gz in the 05.AllheStat/BSTViewer_project/subdata/ directory of the downloaded data)

-c||–cluster_file Clustering file produced in step 2

-o||–outfile Output file name

-h||–help Help documentation

Example: python3 bmplot.py

-e /root/05.AllheStat/BSTViewer_project/he.tif

-p /root/05.AllheStat/BSTViewer_project/subdata/L13_heAuto/barcodes _pos.tsv.gz

-c /root/outdir/test/cluster.csv (output file from the previous step)

-o /root/outdir/test/L13.tif

Output

Results:

Appendix:

Walkthrough of the Cluster plot script