Fig 1.
Here, we take two spatial transcriptomics data as an example. Tacos treats these two normalized gene expression matrices X1, X2 and their corresponding spatial coordinates Y1, Y2 as inputs. Tacos builds graphs G1 and G2 based on Y1 and Y2. Two augmented graph views are generated for G1 and G2. A two-layer GCN encoder is adopted to extract embeddings Z1 and Z2. To align the slices, we identified MNN pairs based on Z1 and Z2. Tacos adopts a triplet loss to pull the MNN pairs close and push the negative pairs away to align the embeddings, where the negative pair are two spots with one spot from MNN and another spot being randomly selected. The total loss of Tacos is composed with three different constraints (contrastive constraint , within-slice constraint
and across-slice constraint
). Red edges and red nodes are masked ones.
Fig 2.
Benchmarking Tacos with other methods on DLPFC datasets from 10X Visium.
(A) UMAP and PAGA visualization of aligned space of Scanpy, Harmony, SLAT, SPIRAL, STAligner and Tacos on slices 151674 and 151675. Spots are colored by slice numbers and annotated layers respectively. In a PAGA graph, thicker edges indicate stronger connections. (B) Bar plots of different metric scores of aligned performances of these methods on slices of 151674 and 151675. Batch Entropy Score, Graph connectivity, bASW and bLISI are metrics used for evaluating batch correction, while cASW and cLISI are used for evaluating biological conservation. (C) Visualization of the reconstructed marker gene (VIM for Layer1, PCP4 for Layer5) for slices 151507, 151669 and 151673, respectively.
Table 1.
Summary of the spatial datasets.
Fig 3.
Benchmarking Tacos with other methods on the MOB dataset.
(A) Visualization of spatial domain detection results of Tacos, STAligner, SPIRAL, SLAT, Harmony and Scanpy. (B) UMAP visualization of Tacos aligned space colored by louvain clusters and platforms. The expression levels of Atp2b4 and Fxyd6 are also shown. (C) Spatial visualization of spatial domains identified by Tacos and corresponding marker genes from Slide-seq slice (top) and Stereo-seq slice (bottom). The layers progressed from the outer to the inner layers, including the olfactory nerve layer (ONL), glomerular layer (GL), external plexiform layer (EPL), mitral cell layer (MCL), granule cell layer (GCL), rostral migratory stream (RMS), granular layer of the accessory olfactory bulb and accessory olfactory bulb.
Fig 4.
The performance of Tacos on datasets of mouse embryo from seqFISH and Stereo-seq.
(A) Spatial visualization of mouse embryo from seqFISH and Stereo-seq with the annotated label. (B) UMAP visualization of aligned embeddings obtained by Scanpy, Harmony, SLAT, STAligner, SPIRAL and Tacos. (C) Bar plots of the metric scores of these methods in aligning mouse embryo slices. (D) UMAP visualization of aligned slices. Spots with the same labels are colored by different colors, while spots with different labels are colored as grey. (E) Sankey plot of connection between seqFISH and Stereo-seq on low embedding of Tacos.
Fig 5.
The performance of Tacos on healthy hippocampus slice and Alzheimer hippocampus slice.
(A) Spatial visualization of domains detected by Scanpy, Harmony, SLAT, SPIRAL, STAligner and Tacos. (B) Spatial visualization of domains corresponding to CA1, CA2, CA3 and DG from Tacos and STAligner. (C) Spatial visualization of marker genes of spatial domain CA1, CA2, CA3 and DG. (D) Spatial visualization of disease relative genes in two slices including C1qa, C1qb, C1qc and Csf1r.
Fig 6.
The performance of Tacos on datasets of Xenium (Healthy and Alzheimer disease human brain).
(A) UMAP plot of raw data (left) and the aligned low-dimensional embedding of Tacos (right). (B) Spatially visualization of domains detected on healthy slice (left) and the Alzheimer disease slice (right). (C) The distribution of denoised CHODL by Tacos on the spatial location. (D) Violin plot of CHODL in cluster 1 across healthy and Alzheimer slices.