Alignment and integration of spatial transcriptomics data

Abstract

Spatial transcriptomics (ST) is a new technology that measures mRNA expression across thousands of spots on a tissue slice, while preserving information about the spatial location of spots. ST is typically applied to several replicates from adjacent slices of a tissue. However, existing methods to analyze ST data do not take full advantage of the similarity in both gene expression and spatial organization across these replicates. We introduce a new method PASTE (Probabilistic Alignment of ST Experiments) to align and integrate ST data across adjacent tissue slices leveraging both transcriptional similarity and spatial distances between spots. First, we formalize and solve the problem of pairwise alignment of ST data from adjacent tissue slices, or layers, using Fused Gromov-Wasserstein Optimal Transport (FGW-OT), which accounts for variability in the composition and spatial location of the spots on each layer. From these pairwise alignments, we construct a 3D representation of the tissue. Next, we introduce the problem of simultaneous alignment and integration of multiple ST layers into a single layer with a low rank gene expression matrix. We derive an algorithm to solve the problem by alternating between solving FGW-OT instances and solving a Non-negative Matrix Factorization (NMF) of a weighted expression matrix. We show on both simulated and real ST datasets that PASTE accurately aligns spots across adjacent layers and accurately estimates a consensus expression matrix from multiple ST layers. PASTE outperforms integration methods that rely solely on either transcriptional similarity or spatial similarity, demonstrating the advantages of combining both types of information.

STRAINS: A Big Data Method for Classifying Cellular Response to Stimuli at the Tissue Scale

“…Furthermore, STRAINS aligns with new techniques in multiplexed imaging and large-scale omics data collection in the push for spatially-resolved cell data. Recently developed methods such as PASTE can produce full tissuescale renderings of transcriptomic data, enabling identification of gene expression and cell type within tissues [52]. Similarly, techniques like IBEX [53] or Cell DIVE [54] make use of immunofluorescent imaging to detect protein-level spatial organization of cells and tissues [55].…”

“…Furthermore, STRAINS aligns with new techniques in multiplexed imaging and large-scale omics data collection in the push to enable spatially-resolved cell data. Recently developed methods such as spatial resolution in transcriptomics such as PASTE enable the measurement of mRNA expression in 2D slices of tissue, which can then be combined to provide a full tissue-scale rendering of transcriptomic data, enabling identification of gene expression and cell type within tissues [51]. Similarly, techniques like like IBEX [52] or Cell DIVE [53] have opened the door to spatial mapping of proteins in situ , making use of immunofluorescent imaging to detect protein-level spatial organization of cells and tissues [54].…”

Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

“…S1-S2). Specifically, we considered the following eight integration methods: Harmony [19], Seurat V3 [24], fastMNN [20], scGen [26], Scanorama [23], scVI [25], MEFISTO [27] and PASTE [28], all of which except PASTE can be used to estimate the aligned low-dimensional embeddings among samples but PASTE can only estimate the embeddings of the center slice. The simulation details are provided in the Methods section.…”

“…In this case, the true low-dimensional embeddings are unknown, so we did not evaluate the canonical correlation. Similar to Zeira et al [28], to generate count matrix, we randomly added a pseudocount for each raw count from a binomial distribution with size three and probability parameter 0.3. Finally, we obtained the count matrix as well as spatial coordinates as input for compared methods.…”

Image-based representation of massive spatial transcriptomics datasets

“…Recently, several software packages for the alignment of spatial sequencing data have been developed specifically within the field of spatial transcriptomics. Probabilistic Alignment of Spatial Transcriptomics Experiments (PASTE) 30 first solves an optimal transport problem to derive a probabilistic assignment of points for pairs of consecutive slices. Based on these, a rigid transformation model is sequentially estimated for each slice.…”