PersiST

PersiST is an exploratory method for analysing spatial transcriptomics (and other spatial 'omics) datsets. Given a spatial transcriptomics data set containing expression data on multiple genes resolved to a shared set of co-ordinates, PerisST computes a single score for each gene that measures the amount of spatial structure that gene shows in it's expression pattern, called the Coefficient of Spatial Structure (CoSS). This score can be used for multiple analytical tasks, as we show below.

Spatially Variable Gene Identification

For this tutorial, we shall be looking at spatial transcriptomics data on a sample from the Kidney Precision Medicine Project[1].

import pandas as pd
df = pd.read_csv('data/kpmp_30-10125_spatial_expression.csv')
df.head()

	x_position	y_position	TSPAN6	TNMD	DPM1	SCYL3	C1orf112	FGR	CFH	FUCA2	...	ENSG00000288156	ENSG00000288162	ENSG00000288172	ENSG00000288187	ENSG00000288234	ENSG00000288253	ENSG00000288302	ENSG00000288380	ENSG00000288398	SOD2
0	0.548810	0.834208	0.00000	0.0	0.000000	0.0	0.00000	117.633220	0.00000	0.00000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1058.6990
1	0.589610	0.809106	0.00000	0.0	0.000000	0.0	0.00000	86.865880	173.73177	86.86588	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1737.3176
2	0.571644	0.166174	75.90709	0.0	75.907090	0.0	0.00000	0.000000	151.81418	0.00000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2201.3057
3	0.539074	0.714422	382.89725	0.0	127.632416	0.0	0.00000	127.632416	0.00000	0.00000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1148.6918
4	0.570493	0.468741	82.88438	0.0	0.000000	0.0	82.88438	0.000000	82.88438	0.00000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1989.2250

5 rows × 26026 columns

This is a pandas DataFrame where the first two columns correspond to the well co-ordinates, and the remaining columns contain the expression of each gene. This is the format PersiST expects spatial transcriptomics data to come in.

Let's compute CoSS scores for all the genes in this sample (this will take about 10 - 20 minutes).

from compute_persistence import run_persistence
metrics = run_persistence(df)

Let's take a look at those genes with the highest CoSS scores

metrics = metrics.sort_values('CoSS', ascending=False)
metrics.iloc[:10,:]

	gene	CoSS	ratio	gene_rank	possible_artefact	svg
16443	IGLC1	0.141620	0.651803	1.0	No	Yes
16483	IGHG1	0.114255	0.467722	2.0	No	Yes
5372	MT1G	0.105850	0.335738	3.0	No	Yes
10798	DEFB1	0.103534	0.376595	4.0	No	Yes
12467	CCL19	0.101025	0.649770	5.0	No	Yes
22516	C17orf113	0.098336	0.574433	6.0	No	Yes
6980	ALDOB	0.096201	0.271491	7.0	No	Yes
5750	PODXL	0.095475	0.327815	8.0	No	Yes
1102	SLC12A3	0.095306	0.352575	9.0	No	Yes
11812	UMOD	0.094709	0.401716	10.0	No	Yes

from plotting_utils import plot_many_genes
plot_many_genes(df, list(metrics.gene)[:20])

We can see that PersiST effectively surfaces those genes with notable spatial structure.

From the CoSS scores PersiST automatically calles genes as SV or not (this is the 'svg' column in the results). Once the data set has been reduced to the comparatively small number of genes PersiST typically calls as SV, in our experience simple clustering methods, such as hierarchical clustering, were effective to pick out groups of co-expressed SVGs.

Here is such a group of genes all expressed in the glomeruli of this particular sample [2].

plot_many_genes(df, ['PODXL', 'PTGDS', 'IGFBP5', 'TGFBR2', 'IFI27', 'HTRA1'], numcols=3)

References

[1] Blue B Lake et al. “An atlas of healthy and injured cell states and niches in the human kidney”. In: Nature 619.7970 (2023), pp. 585–594.

[2] PersiST paper (not yet published)