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Alignment and integration of spatial transcriptomics data


Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. We introduce probabilistic alignment of ST experiments (PASTE), a method to align and integrate ST data from multiple adjacent tissue slices. PASTE computes pairwise alignments of slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots. PASTE further combines pairwise alignments to construct a stacked 3D alignment of a tissue. Alternatively, PASTE can integrate multiple ST slices into a single consensus slice. We show that PASTE accurately aligns spots across adjacent slices in both simulated and real ST data, demonstrating the advantages of using both transcriptional similarity and spatial information. We further show that the PASTE integrated slice improves the identification of cell types and differentially expressed genes compared with existing approaches that either analyze single ST slices or ignore spatial information.

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Fig. 1: Alignment and integration of ST slices with PASTE.
Fig. 2: PASTE results on simulated ST slices from a breast cancer ST slice from Ståhl et al.1.
Fig. 3: PASTE pairwise slice alignment of SCC7.
Fig. 4: PASTE center slice integration of SCC tumor7 into a center slice.
Fig. 5: PASTE pairwise alignment and stacked 3D alignment of DLPFC sample III.
Fig. 6: PASTE center alignment of DLPFC sample III improves identification of layers and differentially expressed genes.

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Data availability

The ST datasets for the breast cancer1, SCC7, spinal cord36, Her2 breast cancer37 and DLPFC12 were taken from the original publications. Preprocessed datasets to reproduce the results can be found at

Code availability

The PASTE methods are implemented in an open-source, publicly available Python package that is available at All the code to reproduce the analysis can be found at


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This work was supported by National Cancer Institute grants U24CA211000 and U24CA248453 to B.J.R. The funder had no role in the conceptualization, design, data collection, analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations



R.Z. conceived, designed and developed the method, analyzed the DLPFC and Her2 breast cancer datasets and wrote the manuscript with contributions from the coauthors. M.L. implemented the method and performed the simulation, SCC and spinal cord data analyses. A.S. contributed to the benchmarking of PASTE against Seurat and STUtility and the analyses of the DLPFC and SCC dataset. B.J.R. supervised the work, contributed to the design of the method and wrote the manuscript with contributions from the coauthors. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Benjamin J. Raphael.

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Competing interests

B.J.R. is a cofounder of, and consultant to, Medley Genomics. The other authors declare no competing interests.

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Nature Methods thanks Jean Yang and the other, anonymous, reviewers for their contribution to the peer review of this work. Lei Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Spatial organization of breast cancer ST slices.

(a-d) Spatial organization of the four breast cancer ST slices from35. Each slice in this dataset consists of 251-264 spots and 7453-7998 genes. (e) Spatial coordinates of the four breast cancer ST slices from35 after pairwise alignment via PASTE.

Extended Data Fig. 2 PASTE results on simulated data generated from each of the indicated breast cancer slices35.

Each line (color) corresponds to running PASTE with a specific value for alpha. Error bars represent the standard deviation across 10 simulated instances.

Extended Data Fig. 3 Comparison of published clusters and clusters obtained by PASTE on ST data from SCC patients 2, 5, 9, and 10 in21.

(Left) The published cluster labels from21 of spots in slice A from each of the four patients. (Right) k-means clustering of inferred center slice from PASTE.

Extended Data Fig. 4 PASTE integration of Her2 breast cancer patient G from Andersson et al.

(a) Pathological annotations and (b) clustering results from PASTE integrated slice for a slice of breast cancer patient G from Andersson et al. Black circles indicate small region of spots of in situ cancer which are also clustered together in the PASTE integrated slice.

Extended Data Fig. 5 Dorsolateral prefrontal cortex ST data from31.

Each of the three samples is composed of four ST slices. The first two slices and last two slices are 10μm apart while the middle pair of slices is taken 300μm apart. Spots are colored by the six neocortical layers or the white matter according to the annotation of31.

Extended Data Fig. 6 Pairwise alignment of slices B and C from DLPFC Sample I.

Pairwise alignment using (a) PASTE, (b) Seurat, (c) Tangram and (d) STUtility. Gray lines connect the 1000 spot pairs with highest alignment values from each method. PASTE and STUtility alignments are more consistent with spatial organization of slices than Seurat and Tangram alignments.

Extended Data Fig. 7 Alignment accuracy of adjacent DLPFC slices using PASTE with different expression costs.

PASTE with: (Default) All genes and KL divergence, (Lib-Log-Norm) All genes with library size normalization and log transformation and Euclidean distance, (HVG) Same as Lib-Log-Norm but restricted to top 2000 highly variable genes.

Extended Data Fig. 8 TRABD2A expression in a single slice and PASTE integrated slice.

The boundaries between the layers are marked in green in a and c. WM and Layers 6 to 1 have 625, 614, 621, 247, 924, 224 and 380 spots respectively. Inner boxplots show the 25%, 50% and 75% quantiles of the distributions. p-values (rounded to the closest power of 10) for the difference in distribution (two-sided Mann-Whitney U test) between adjacent layers are indicated. TRABD2A was validated using smFISH in31 as a layer 5 marker gene.

Extended Data Fig. 9 Ranking of known layer-specific marker genes by differential expression analysis.

Gene ranking using: the pseudo-bulk approach of Maynard et al., PASTE center slice integration, Scanorama, and Seurat. Red lines indicate median rank of marker genes which are 1147 for Maynard et al, 427 for PASTE, 3380.5 for Scanorama, and 1852 for Seurat. Rank 1 is the highest rank.

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Zeira, R., Land, M., Strzalkowski, A. et al. Alignment and integration of spatial transcriptomics data. Nat Methods 19, 567–575 (2022).

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