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Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution

Abstract

Spatial transcriptomics approaches have substantially advanced our capacity to detect the spatial distribution of RNA transcripts in tissues, yet it remains challenging to characterize whole-transcriptome-level data for single cells in space. Addressing this need, researchers have developed integration methods to combine spatial transcriptomic data with single-cell RNA-seq data to predict the spatial distribution of undetected transcripts and/or perform cell type deconvolution of spots in histological sections. However, to date, no independent studies have comparatively analyzed these integration methods to benchmark their performance. Here we present benchmarking of 16 integration methods using 45 paired datasets (comprising both spatial transcriptomics and scRNA-seq data) and 32 simulated datasets. We found that Tangram, gimVI, and SpaGE outperformed other integration methods for predicting the spatial distribution of RNA transcripts, whereas Cell2location, SpatialDWLS, and RCTD are the top-performing methods for the cell type deconvolution of spots. We provide a benchmark pipeline to help researchers select optimal integration methods to process their datasets.

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Fig. 1: Benchmarking workflow and summary characteristics of the examined paired datasets.
Fig. 2: Comparing the accuracy of eight integration methods capable of predicting the spatial distribution of RNA transcripts.
Fig. 3: Comparing the accuracy of the eight integration methods for sparse spatial expression matrices down-sampled from the original datasets using Splatter.
Fig. 4: Comparing the performance of the 12 integration methods capable of deconvoluting cell types of each histological spot.

Data availability

A summary of the individual accession numbers is given in Supplementary Table 1. The raw data are available from following study:

Dataset 1 (mouse gastrulation): seqFISH, https://content.cruk.cam.ac.uk/jmlab/SpatialMouseAtlas2020/; 10X Chromium, ‘Sample 21’ in MouseGastrulationData within the R/Bioconductor data packageMouseGastrulationData.

Dataset 2 (mouse embryonic stem cell): seqFISH, https://zenodo.org/record/3735329#.YY69HZMza3J; Microwell-Seq, ‘EmbryonicStemCells’ in ‘MCA_BatchRemoved_Merge_dge.h5ad’ file in https://figshare.com/articles/dataset/MCA_DGE_Data/5435866.

Dataset 3 (mouse hippocampus): seqFISH, https://ars.els-cdn.com/content/image/1-s2.0-S0896627316307024-mmc6.xlsx; 10X Chromium, ‘HIPP_sc_Rep1_10X sample’ in GSE158450 in the GEO database.

Dataset 4 (mouse cortex): seqFISH+, https://github.com/CaiGroup/seqFISH-PLUS, and the spatial coordinate of each spot was generated using ‘stitchFieldCoordinates’ function in Giotto; Smart-seq, mouse primary visual cortex (VISp) in the dataset in https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-v1-and-alm-smart-seq.

Dataset 5 (mouse olfactory bulb): seqFISH+, https://github.com/CaiGroup/seqFISH-PLUS; Drop-seq, GSE148360 in the GEO database.

Dataset 6 (mouse hypothalamic preoptic region): MERFISH, the eighteenth female parent mouse (animal ID = 18) in https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248; 10X Chromium, GSE113576 in the GEO database.

Dataset 7 (human osteosarcoma): MERFISH, the ‘B1_cell’ used in https://www.pnas.org/doi/suppl/10.1073/pnas.1912459116/suppl_file/pnas.1912459116.sd12.csv; 10X Chromium, BC22 in GSE152048 in the GEO database.

Dataset 8 (mouse primary motor cortex): MERFISH, ‘mouse1_slice162’ in https://caltech.box.com/shared/static/dzqt6ryytmjbgyai356s1z0phtnsbaol.gz; 10X Chromium, https://data.nemoarchive.org/biccn/lab/zeng/transcriptome/scell/10x_v3/mouse/processed/analysis/10X_cells_v3_AIBS/.

Dataset 9 (mouse VISP): MERFISH, https://github.com/spacetx-spacejam/data/; Smart-seq, mouse primary visual cortex (VISp) in https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-v1-and-alm-smart-seq.

Dataset 10 (mouse visual cortex): STARmap, ‘20180505_BY3_1kgenes’ in https://www.starmapresources.com/data; Smart-seq, mouse primary visual cortex (VISp) in https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-v1-and-alm-smart-seq.

Dataset 11 (mouse prefronatal cortex): STARmap, ‘20180419_BZ9_control’ in https://www.starmapresources.com/data; 10X Chromium, ‘PFC_sc_Rep2_10X’ in GSE158450 in the GEO database.

Dataset 12 (human middle temporal gyrus): ISS, https://github.com/spacetx-spacejam/data; Smart-seq, https://portal.brain-map.org/atlases-and-data/rnaseq/human-mtg-smart-seq.

Dataset 13 (mouse VISP): ISS, https://github.com/spacetx-spacejam/data; Smart-seq, mouse primary visual cortex (VISp) in the dataset in https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-v1-and-alm-smart-seq.

Dataset 14 (Drosophila embryo): FISH, https://github.com/rajewsky-lab/distmap; Drop-seq, GSE95025 in the Gene Expression Omnibus (GEO) database.

Dataset 15 (mouse somatosensory cortex): osmFISH, cortical regions in http://linnarssonlab.org/osmFISH/; Smart-seq, mouse somatosensory cortex (SSp) in https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-smart-seq.

Dataset 16 (mouse VISP): BaristaSeq, https://github.com/spacetx-spacejam/data; Smart-seq, mouse primary visual cortex (VISp) in https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-v1-and-alm-smart-seq.

Dataset 17 (mouse VISP): ExSeq, https://github.com/spacetx-spacejam/data; Smart-seq, mouse primary visual cortex (VISp) in https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-v1-and-alm-smart-seq.

Dataset 18 (mouse hindlimb muscle): 10X Visium, Vis5A in GSE161318 in the GEO database; 10X Chromium, D2_Ev3 in GSE159500 in the GEO database.

Dataset 19 (mouse hindlimb muscle): 10X Visium, Vis9A in GSE161318 in the GEO database; 10X Chromium, D7_Ev3 in GSE159500 in the GEO database.

Dataset 20 (human breast cancer): 10X Visium, ‘CID3586’ in https://zenodo.org/record/4739739#.YY6N_pMzaWC; 10X Chromium, ‘CID3586’ in GSE176078 in the GEO database.

Dataset 21 (human breast cancer): 10X Visium, ‘1160920F’ in https://zenodo.org/record/4739739#.YY6N_pMzaWC; 10X Chromium, ‘CID3586’ in GSE176078 in the GEO database.

Dataset 22 (human breast cancer): 10X Visium, ‘CID4290’ in https://zenodo.org/record/4739739#.YY6N_pMzaWC; 10X Chromium, ‘CID3586’ in GSE176078 in the GEO database.

Dataset 23 (human breast cancer): 10X Visium, ‘CID4465’ in https://zenodo.org/record/4739739#.YY6N_pMzaWC; 10X Chromium, ‘CID3586’ in GSE176078 in the GEO database.

Dataset 24 (human breast cancer): 10X Visium, ‘CID44971’ https://zenodo.org/record/4739739#.YY6N_pMzaWC; 10X Chromium, ‘CID3586’ in GSE176078 in the GEO database.

Dataset 25 (human breast cancer): 10X Visium, ‘CID4535’ in https://zenodo.org/record/4739739#.YY6N_pMzaWC; 10X Chromium, ‘CID3586’ in GSE176078 in the GEO database.

Dataset 26 (zebrafish melanoma): 10X Visium, ‘Visium-A’ in GSE159709 in the GEO database; 10X Chromium, ‘SingleCell-E’ in GSE159709 in the GEO database.

Dataset 27 (mouse embryo): 10X Visium, ‘Visium-A1’ in GSE160137 in the GEO database; 10X Chromium, ‘Pax2-GFP_SC-2’ in GSE143806 in the GEO database.

Dataset 28 (human prostate): 10X Visium, ‘D25’ in GSE159697 in the GEO database; 10X Chromium, ‘V8’ in GSE142489 in the GEO database.

Dataset 29 (mouse kidney): 10X Visium, Sham Model in GSE171406 in the GEO database; 10X Chromium, wild-type sham mouse in GSE171639 in the GEO database.

Dataset 30 (mouse kidney): 10X Visium, ischemia reperfusion injury model in GSE171406 in the GEO database; 10X Chromium, wild-type ischemic acute kidney injury mouse in GSE171639 in the GEO database.

Dataset 31 (mouse brain): 10X Visium, ‘section1’ in GSE153424 in the GEO database; 10X Chromium, ‘brain1_cx’ in GSE153424 in the GEO database.

Dataset 32 (mouse prefrontal cortex): 10X Visium, ‘Visium_10X’ in GSE158450 in the GEO database; 10X Chromium, ‘PFC_sc_Rep1_10X’ in GSE158450 in the GEO database.

Dataset 33 (mouse hippocampus): 10X Visium, ‘Visium_10X’ in GSE158450 in the GEO database; 10X Chromium, ‘HIPP_sc_Rep1_10X’ in GSE158450 in the GEO database.

Dataset 34 (mouse kidney): 10X Visium, GSE154107 in the GEO database; 10X Chromium, sample ‘(LPS36hr) scRNA-seq’ in GSE151658 in the GEO database.

Dataset 35 (human prostate): 10X Visium, ‘ETOH’ in GSE159697 in the GEO database; 10X Chromium, ‘V8’ in GSE142489 in the GEO database.

Dataset 36 (mouse lymph node): 10X Visium, ‘PBS’ samples of Tissue 1 in https://github.com/romain-lopez/DestVI-reproducibility; 10X Chromium, ‘PBS’ samples in https://github.com/romain-lopez/DestVI-reproducibility.

Dataset 37 (mouse MCA205 tumor): 10X Visium, Tumor A1 of Tissue 1 in https://github.com/romain-lopez/DestVI-reproducibility; 10X Chromium, https://github.com/romain-lopez/DestVI-reproducibility.

Dataset 38 (mouse primary motor cortex): 10X Visium, https://storage.googleapis.com/tommaso-brain-data/tangram_demo/Allen-Visium_Allen1_cell_count.h5ad; 10X Chromium, ‘batch 9’ in ‘mop_sn_tutorial.h5ad’ file from https://console.cloud.google.com/storage/browser/tommaso-brain-data.

Dataset 39 (mouse primary motor cortex): Slide-seq, https://storage.googleapis.com/tommaso-brain-data/tangram_demo/slideseq_MOp_1217.h5ad.gz; 10X Chromium, ‘batch 9’ in ‘mop_sn_tutorial.h5ad’ file from https://console.cloud.google.com/storage/browser/tommaso-brain-data.

Dataset 40 (mouse cerebellum): Slide-seqV2, SCP948 in https://singlecell.broadinstitute.org/single_cell/; 10X Chromium, sample M003 of study SCP795 in https://singlecell.broadinstitute.org/single_cell/.

Dataset 41 (mouse hippocampus): Slide-seqV2, ‘Puck_200115_08’ in https://singlecell.broadinstitute.org/single_cell/study/SCP815/highly-sensitive-spatial-transcriptomics-at-near-cellular-resolution-with-slide-seqv2#study-download; Drop-seq, we randomly sampled 10,000 cells from ‘GSE116470_F_GRCm38.81.P60Hippocampus.raw.dge.txt.gz’ file in GSE116470 in the GEO database.

Dataset 42 (human squamous carcinoma): ST, GSM4284322 in the GEO database; 10X Chromium, ‘GSE144236_cSCC_counts.txt.gz’ in GSE144236 in the GEO database.

Dataset 43 (mouse hippocampus): ST, wild-type replicate 1 in https://data.mendeley.com/datasets/6s959w2zyr/1; 10X Chromium, GSE116470 in the GEO database.

Dataset 44 (mouse olfactory bulb): HDST, replicate1 in GSE130682 in the GEO database; 10X Chromium, WT1 samples used from GSE121891 in the GEO database.

Dataset 45 (mouse liver): Seq-scope, https://deepblue.lib.umich.edu/data/downloads/gx41mj14n; Smart-seq2, liver sample in GSE109774 in the GEO database.

We also provide an open source website for users to download all the above datasets: https://drive.google.com/drive/folders/1pHmE9cg_tMcouV1LFJFtbyBJNp7oQo9J?usp=sharing.

Source data for figures and Extended Data Figures are provided with this paper. Source data are provided with this paper.

Code availability

We uploaded the code and scripts used for the comparative analysis and figure plotting to GitHub: https://github.com/QuKunLab/SpatialBenchmarking. The package can also be used to analyze user’s own datasets.

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Acknowledgements

This work was supported by the National Key R&D Program of China (2020YFA0112200 to K.Q.), the National Natural Science Foundation of China grants (T2125012, 91940306, 31970858, and 31771428 to K.Q.; 32170668 to B.L.; 81871479 to J.L.), CAS Project for Young Scientists in Basic Research YSBR-005 (to K.Q.), Anhui Province Science and Technology Key Program (202003a07020021 to K.Q.) and the Fundamental Research Funds for the Central Universities (YD2070002019, WK9110000141, and WK2070000158 to K.Q.; WK9100000001 to J.L). We thank the USTC supercomputing center and the School of Life Science Bioinformatics Center for providing computing resources for this project.

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Contributions

K.Q. and B.L. conceived the project. B.L., W.Z. and C.G. designed the framework and performed data analysis with help from H.X., L.L., M.F, Y.H., X.Y., X.Z., F.C. and T.X., X.Z., M.T., K.L., J.L. and L.C. contributed to revision of the manuscript. B.L., K.Q., C.G., and W.Z. wrote the manuscript with inputs from all authors. K.Q. supervised the entire project. All authors read and approved the final manuscript.

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Correspondence to Kun Qu.

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Nature Methods thanks Ahmed Mahfouz and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary Handling editor: Lin Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Comparing the accuracy of eight integration methods in predicting the spatial distribution of RNA transcripts.

a, The spatial distribution of COL17A1 in dataset 42 (ST; 10X Chromium; human squamous carcinoma), including the ground truth and prediction results from the integration methods. PCC: Pearson Correlation Coefficient between the expression vector of a transcript in the ground truth and that of the predicted result. b, Bar plots of PCC, SSIM, RMSE, and JS of each integration method in predicting the spatial distribution of transcripts of dataset 42. SSIM: Structural Similarity Index; RMSE: Root Mean Square Error; JS: Jensen-Shannon divergence. Data are presented as mean values ± 95% confidence intervals; n = 948 predicted genes. c, The violin plot of AS (accuracy score, aggregated from PCC, SSIM, RMSE, and JS; see Methods) of the eight integration methods for transcripts in dataset 42. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; n = 4 benchmark metrics.

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Extended Data Fig. 2 The boxplots of PCC, SSIM, RMSE, JS values of each integration method in predicting the spatial distribution of RNA transcripts of 45 paired spatial transcriptomics and scRNA-seq datasets.

The boxplots of PCC, SSIM, RMSE, JS values of each integration method in predicting the spatial distribution of RNA transcripts of 45 paired spatial transcriptomics and scRNA-seq datasets. Center line, median; box limits, upper and lower quartiles; whiskers, 0.5× interquartile range, the number of genes for each dataset is shown at the top of each panel.

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Extended Data Fig. 3 PCC, SSIM, RMSE, JS and AS of spatial distribution of RNA transcripts predicted by each integration method for the 45 paired spatial transcriptomics and scRNA-seq datasets.

a-g, Boxplots of AS (accuracy score, aggregated from PCC, SSIM, RMSE, and JS; see Methods) of the integration methods for transcripts in the 17 image-based datasets (a), 28 seq-based datasets (b), 32 simulated datasets (c), 21 10X visium datasets (d), 5 seqFISH datasets (e), 4 MERFISH datasets (f), 3 Slide-seq datasets (g). Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range. Grey dots indicate the prediction result is not available, as the tool made an error when predictions.

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Extended Data Fig. 4 The PCC values of each integration method when processing the raw expression matrices and the normalized expression matrices.

The PCC values of each integration method when processing the raw expression matrices and the normalized expression matrices. R-R: raw expression matrix of spatial data and raw expression matrix of scRNA-seq data; N-R: normalized expression matrix of spatial data and raw expression matrix of scRNA-seq data; R-N: raw expression matrix of spatial data and normalized expression matrix of scRNA-seq data; N-N: normalized expression matrix of spatial data and normalized expression matrix of scRNA-seq data; n = 43 independent datasets. Dataset6 and Dataset8 are excluded, as the normalized expression matrix of spatial data has been normalized.

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Extended Data Fig. 5 Impact of normalization on the accuracy of eight integration methods that can predict the spatial distribution of RNA transcripts.

a, b, Boxplots of the PCC values of the eight integration methods for 28 seq-based datasets (a) or 15 image-based datasets (b) when using the four schemes of input expression matrices (that is R-R, R-N, N-R, and N-N, see their definition in the legend of Extended Data Fig. 4). For the genes predicted by each method, we removed outliers using 10%-90% confidence interval. Statistical significance was analyzed with two-sided paired t-test, *P < 0.05, **P < 0.01, ***P < 0.001 and ****P < 0.0001. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range. c-f, Boxplots of the AS values of the eight integration methods for all the 45 paired datasets when using the four schemes of input expression matrices. For the genes predicted by each method, we removed outliers using 10%-90% confidence interval. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; n = 43 independent datasets.

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Extended Data Fig. 6 Correlation between the four metrics (PCC, SSIM, RMSE, and JS) and the sparsity of each examined spatial expression matrix.

Correlation between the four metrics (PCC, SSIM, RMSE, and JS) and the sparsity of each examined spatial expression matrix. For all the eight integration methods that can predict the spatial distribution of transcripts, the JS values are linearly positively correlated with the sparsity of expression matrices of the spatial transcriptomics data (R2 ≥ 0.50).

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Extended Data Fig. 7 Comparing the accuracy of the eight integration methods for sparse expression matrices down-sampled from the original datasets using Scuttle.

a, Spatial distribution of Cplx1 expression in dataset 4 (seqFISH+; Smart-seq; mouse cortex), predicted from the original data and down-sampled data (down-sampling rate = 0.8). b, PCC of the spatial distribution of transcripts predicted from the original data and down-sampled data from dataset 4. The PCC values of the red-colored transcripts are greater than 0.5 for both the original and the down-sampled data. The proportion of the red-colored transcripts in all transcripts was defined as the ‘robustness score’ (RS). c, RS values of the eight integration methods when processing sparse expression matrices down-sampled from dataset 4 at different down-sampling rates. d, RS values of the eight integration methods when processing the sparse expression matrices of the down-sampled datasets. The original datasets (used to generate the down-sampled datasets) capture >1000 genes from >100 spots, and the sparsity of the expression matrices is <0.7. Data are presented as mean values ± 95% confidence intervals; n = 19 independent datasets.

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Extended Data Fig. 8 Comparing the performance of the twelve integration methods in cell type deconvolution.

a, PCC, SSIM, RMSE, and JS values for the cell type composition of the spots simulated from dataset 10, generated by twelve integration methods. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; n = 12 predicted cell types. b, A seqFISH+ slide of dataset 4 (seqFISH+; Smart-seq; mouse cortex) with cells annotated by cell type. Each grid represents a simulated spot containing 1~18 cells. c, The proportion of L5&6 excitatory neurons in the spots simulated from dataset 4, including the ground truth and the predicted results of twelve integration methods. d, PCC, SSIM, RMSE, and JS values for the cell type composition of the spots simulated from dataset 4, generated by twelve integration methods. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5× interquartile range; n = 8 predicted cell types. e, PCC, SSIM, RMSE, and JS values for the cell type composition of the spots in all the simulated datasets (n = 32), generated by ten integration methods. SpaOTsc and novoSpaRc are excluded, as they require spatial location information for each spot, which is not available in the simulated datasets. Data are presented as mean values ± 95% confidence intervals; n = 32 independent datasets.

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Extended Data Fig. 9 Computer resources consumed by each integration method.

a-c, The impact of the number of cells in scRNA-seq data (a), the number of spots in spatial data (b), and the number of genes used for training (c), on computational resources consumed by the integration methods that can predict the spatial distribution of undetected transcripts. d, The computer time and memory spent by the integration methods that can deconvolute cell types of histological spots, when processing a simulated dataset which contains 20000 spots in its spatial transcriptomics data and 10000 cells in its scRNA-seq data. e-g, The impacts of the number of cells in scRNA-seq data (e), the number of spots in spatial data (f), and the number of the cell types (g) on computational resources consumed by the integration methods that can deconvolute cell types of histological spots.

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Li, B., Zhang, W., Guo, C. et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat Methods 19, 662–670 (2022). https://doi.org/10.1038/s41592-022-01480-9

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