Read any spatial omics and transfer from other tools

Here is the list of input you need to prepare:

• count matrix

• spatial location

• image path (optional)

First, the count matrix should be an Pandas Dataframe with columns as gene names and rows as barcodes

count_matrix

gene	0610009B22Rik	1110004F10Rik	1110008P14Rik	1110032A03Rik	1110051M20Rik	1110059G10Rik	1110065P20Rik	1500009C09Rik	1500011B03Rik	1500015O10Rik	...	mt-Tc	mt-Tf	mt-Ti	mt-Tl1	mt-Tl2	mt-Tm	mt-Tp	mt-Tq	mt-Ts2	mt-Tv
index
c1	0	0	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
c2	0	0	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
c3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
c4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
c5	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
c12278	0	0	0	1	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
c12279	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
c12280	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	1	0	0	0	0
c12281	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
c12282	0	0	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0

12282 rows × 3235 columns

The spatial information of the data should also be a Pandas Dataframe with two columns imagecol (X) and imagerow (Y).

spatial

	imagecol	imagerow
0	2242.482759	1349.896552
1	1707.537313	3820.507463
2	1166.068421	4450.200000
3	4281.867925	4462.679245
4	1462.616000	2813.248000
...	...	...
12277	2395.422018	5990.504587
12278	2623.265060	4220.325301
12279	1808.611111	4375.761111
12280	2336.065693	5332.613139
12281	1124.329341	4428.023952

12282 rows × 2 columns

The image path is optional. If you don’t provide any image, the background will be 'black' or 'white'.

Create the object for stLearn

import stlearn as st
adata = st.create_stlearn(count=count_matrix,spatial=spatial,library_id="Sample_test", scale=1,background_color="white")

adata

AnnData object with n_obs × n_vars = 12282 × 3235
    obs: 'imagecol', 'imagerow'
    uns: 'spatial'
    obsm: 'spatial'

Quality control plot

st.pl.QC_plot(adata)

An example of gene plot

st.pl.gene_plot(adata,gene_symbols="Gfap")

Add metadata

stLearn core object is using AnnData then you can transfer the clustering results to adata.obs. For example, you have clustering results from Seurat seurat_results (should be a numpy array), and then you can plot it with stlearn.pl.cluster_plot and use_label == "seurat" :

adata.obs["seurat"] = pd.Categorical(seurat_results)