Spatial Simulators
in developmentAssessing the quality of spatial transcriptomics simulators
Computational methods for spatially resolved transcriptomics (SRT) are frequently developed and assessed through data simulation. The effectiveness of these evaluations relies on the simulation methods' ability to accurately reflect experimental data. However, a systematic evaluation framework for spatial simulators is lacking. Here, we present SpatialSimBench, a comprehensive evaluation framework that assesses 13 simulation methods using 10 distinct STR datasets.
The research goal of this benchmark is to systematically evaluate and compare the performance of various simulation methods for spatial transcriptomics (ST) data. It aims to address the lack of a comprehensive evaluation framework for spatial simulators and explore the feasibility of leveraging existing single-cell simulators for ST data. The experimental setup involves collecting public spatial transcriptomics datasets and corresponding scRNA-seq datasets. The spatial and scRNA-seq datasets can originate from different study but should consist of similar cell types from similar tissues.
Contributors
Leaderboard
Methods ranked by scaled overall mean. Each cell encodes a score from 0 to 1 by size and intensity.
QC: Normalisation Visualisation 29 plots
Per metric: points placed by control-anchored scaled score (x); dashed lines mark scaled 0 and 1 (worst/best control); the lower axis shows the raw score. Points beyond [-0.2, 1.2] are clamped to the edge as triangles. Hover a dot or line to highlight it and read details.
- Celltype interactionlower better
- Centralized scorelower better
- clustering_arihigher better
- clustering_nmihigher better
- crosscor_cosinehigher better
- crosscor_mantelhigher better
- ctdeconcolute_jsdlower better
- ctdeconvolute_rmselower better
- Effective library sizelower better
- Effective library sizelower better
- Fraction of zeros per celllower better
- Fraction of zeros per genelower better
- Gene Pearson correlationlower better
- L statisticslower better
- Library sizelower better
- Library sizelower better
- Library size vs fraction zerolower better
- Mean vs fraction zerolower better
- Mean vs variancelower better
- Neighborhood enrichmentlower better
- Sample Pearson correlationlower better
- Scaled mean cellslower better
- Scaled mean geneslower better
- Scaled variance celllower better
- Scaled variance geneslower better
- svg_precisionhigher better
- svg_recallhigher better
- TMMlower better
- Transition matrixlower better
QC: Indicator table 64 errors8 warnings
Automated checks on the benchmark run and its results: missing values, score scaling, metric ranges and similar. Errors are high-severity issues that usually need a maintainer's attention; warnings are lower-severity signals. Findings that are expected for this task are listed separately as silenced.
64 high-severity issues need review. 670 of 742 checks passed.
- error Raw results Metric 'crosscor_cosine' % missing
Percentage of missing results should be less than 10% Task: spatial_simulators Metric: crosscor_cosine Number of results: 54 Expected number of results: 90 Percentage missing: 40%
- error Raw results Metric 'svg_recall' number of control methods
Number of metric scores for control methods should be equal to #datasets × #control_methods Task: spatial_simulators Metric: svg_recall Control method scores: 16 Expected control method scores: 20 Percentage succeeded: 80%
- error Raw results Metric 'svg_precision' number of control methods
Number of metric scores for control methods should be equal to #datasets × #control_methods Task: spatial_simulators Metric: svg_precision Control method scores: 9 Expected control method scores: 20 Percentage succeeded: 45%
- error Raw results Metric 'ks_statistic_L_stats' number of control methods
Number of metric scores for control methods should be equal to #datasets × #control_methods Task: spatial_simulators Metric: ks_statistic_L_stats Control method scores: 18 Expected control method scores: 20 Percentage succeeded: 90%
- error Raw results Metric 'ks_statistic_celltype_interaction' number of control methods
Number of metric scores for control methods should be equal to #datasets × #control_methods Task: spatial_simulators Metric: ks_statistic_celltype_interaction Control method scores: 18 Expected control method scores: 20 Percentage succeeded: 90%
- error Raw results Metric 'ks_statistic_nn_correlation' number of control methods
Number of metric scores for control methods should be equal to #datasets × #control_methods Task: spatial_simulators Metric: ks_statistic_nn_correlation Control method scores: 18 Expected control method scores: 20 Percentage succeeded: 90%
- error Raw results Metric 'ks_statistic_morans_I' number of control methods
Number of metric scores for control methods should be equal to #datasets × #control_methods Task: spatial_simulators Metric: ks_statistic_morans_I Control method scores: 18 Expected control method scores: 20 Percentage succeeded: 90%
- error Scaling Metric 'svg_precision' % outside range
Percentage of scaled scores outside control range should be less than 10% Task: spatial_simulators Metric: svg_precision Inside range: NA Scaled scores: 64 Percentage outside: NA%
- error Scaling Worst 'svg_precision' score for 'negative'
Method 'negative' performs much worse than controls for metric' svg_precision' Task: spatial_simulators Method: negative Metric: svg_precision Worst score: NA Percentage outside range: 0%
- error Scaling Best 'svg_precision' score for 'negative'
Method 'negative' performs much better than controls for metric 'svg_precision' Task: spatial_simulators Method: negative Metric: svg_precision Best score: NA Percentage outside range: 0%
- error Scaling Worst 'svg_precision' score for 'positive'
Method 'positive' performs much worse than controls for metric' svg_precision' Task: spatial_simulators Method: positive Metric: svg_precision Worst score: NA Percentage outside range: 0%
- error Scaling Best 'svg_precision' score for 'positive'
Method 'positive' performs much better than controls for metric 'svg_precision' Task: spatial_simulators Method: positive Metric: svg_precision Best score: NA Percentage outside range: 0%
- error Scaling Worst 'svg_precision' score for 'scdesign2'
Method 'scdesign2' performs much worse than controls for metric' svg_precision' Task: spatial_simulators Method: scdesign2 Metric: svg_precision Worst score: NA Percentage outside range: 0%
- error Scaling Best 'svg_precision' score for 'scdesign2'
Method 'scdesign2' performs much better than controls for metric 'svg_precision' Task: spatial_simulators Method: scdesign2 Metric: svg_precision Best score: NA Percentage outside range: 0%
- error Scaling Worst 'svg_precision' score for 'scdesign3'
Method 'scdesign3' performs much worse than controls for metric' svg_precision' Task: spatial_simulators Method: scdesign3 Metric: svg_precision Worst score: NA Percentage outside range: 0%
- error Scaling Best 'svg_precision' score for 'scdesign3'
Method 'scdesign3' performs much better than controls for metric 'svg_precision' Task: spatial_simulators Method: scdesign3 Metric: svg_precision Best score: NA Percentage outside range: 0%
- error Scaling Worst 'svg_precision' score for 'sparsim'
Method 'sparsim' performs much worse than controls for metric' svg_precision' Task: spatial_simulators Method: sparsim Metric: svg_precision Worst score: NA Percentage outside range: 0%
- error Scaling Best 'svg_precision' score for 'sparsim'
Method 'sparsim' performs much better than controls for metric 'svg_precision' Task: spatial_simulators Method: sparsim Metric: svg_precision Best score: NA Percentage outside range: 0%
- error Scaling Worst 'svg_precision' score for 'splatter'
Method 'splatter' performs much worse than controls for metric' svg_precision' Task: spatial_simulators Method: splatter Metric: svg_precision Worst score: NA Percentage outside range: 0%
- error Scaling Best 'svg_precision' score for 'splatter'
Method 'splatter' performs much better than controls for metric 'svg_precision' Task: spatial_simulators Method: splatter Metric: svg_precision Best score: NA Percentage outside range: 0%
- error Scaling Worst 'svg_precision' score for 'srtsim'
Method 'srtsim' performs much worse than controls for metric' svg_precision' Task: spatial_simulators Method: srtsim Metric: svg_precision Worst score: NA Percentage outside range: 0%
- error Scaling Best 'svg_precision' score for 'srtsim'
Method 'srtsim' performs much better than controls for metric 'svg_precision' Task: spatial_simulators Method: srtsim Metric: svg_precision Best score: NA Percentage outside range: 0%
- error Scaling Worst 'svg_precision' score for 'symsim'
Method 'symsim' performs much worse than controls for metric' svg_precision' Task: spatial_simulators Method: symsim Metric: svg_precision Worst score: NA Percentage outside range: 0%
- error Scaling Best 'svg_precision' score for 'symsim'
Method 'symsim' performs much better than controls for metric 'svg_precision' Task: spatial_simulators Method: symsim Metric: svg_precision Best score: NA Percentage outside range: 0%
- error Scaling Worst 'svg_precision' score for 'zinbwave'
Method 'zinbwave' performs much worse than controls for metric' svg_precision' Task: spatial_simulators Method: zinbwave Metric: svg_precision Worst score: NA Percentage outside range: 0%
- error Scaling Best 'svg_precision' score for 'zinbwave'
Method 'zinbwave' performs much better than controls for metric 'svg_precision' Task: spatial_simulators Method: zinbwave Metric: svg_precision Best score: NA Percentage outside range: 0%
- error Scaling Metric 'ks_statistic_celltype_interaction' % outside range
Percentage of scaled scores outside control range should be less than 10% Task: spatial_simulators Metric: ks_statistic_celltype_interaction Inside range: NA Scaled scores: 78 Percentage outside: NA%
- error Scaling Worst 'ks_statistic_celltype_interaction' score for 'negative'
Method 'negative' performs much worse than controls for metric' ks_statistic_celltype_interaction' Task: spatial_simulators Method: negative Metric: ks_statistic_celltype_interaction Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_celltype_interaction' score for 'negative'
Method 'negative' performs much better than controls for metric 'ks_statistic_celltype_interaction' Task: spatial_simulators Method: negative Metric: ks_statistic_celltype_interaction Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_celltype_interaction' score for 'positive'
Method 'positive' performs much worse than controls for metric' ks_statistic_celltype_interaction' Task: spatial_simulators Method: positive Metric: ks_statistic_celltype_interaction Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_celltype_interaction' score for 'positive'
Method 'positive' performs much better than controls for metric 'ks_statistic_celltype_interaction' Task: spatial_simulators Method: positive Metric: ks_statistic_celltype_interaction Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_celltype_interaction' score for 'scdesign2'
Method 'scdesign2' performs much worse than controls for metric' ks_statistic_celltype_interaction' Task: spatial_simulators Method: scdesign2 Metric: ks_statistic_celltype_interaction Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_celltype_interaction' score for 'scdesign2'
Method 'scdesign2' performs much better than controls for metric 'ks_statistic_celltype_interaction' Task: spatial_simulators Method: scdesign2 Metric: ks_statistic_celltype_interaction Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_celltype_interaction' score for 'scdesign3'
Method 'scdesign3' performs much worse than controls for metric' ks_statistic_celltype_interaction' Task: spatial_simulators Method: scdesign3 Metric: ks_statistic_celltype_interaction Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_celltype_interaction' score for 'scdesign3'
Method 'scdesign3' performs much better than controls for metric 'ks_statistic_celltype_interaction' Task: spatial_simulators Method: scdesign3 Metric: ks_statistic_celltype_interaction Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_celltype_interaction' score for 'sparsim'
Method 'sparsim' performs much worse than controls for metric' ks_statistic_celltype_interaction' Task: spatial_simulators Method: sparsim Metric: ks_statistic_celltype_interaction Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_celltype_interaction' score for 'sparsim'
Method 'sparsim' performs much better than controls for metric 'ks_statistic_celltype_interaction' Task: spatial_simulators Method: sparsim Metric: ks_statistic_celltype_interaction Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_celltype_interaction' score for 'splatter'
Method 'splatter' performs much worse than controls for metric' ks_statistic_celltype_interaction' Task: spatial_simulators Method: splatter Metric: ks_statistic_celltype_interaction Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_celltype_interaction' score for 'splatter'
Method 'splatter' performs much better than controls for metric 'ks_statistic_celltype_interaction' Task: spatial_simulators Method: splatter Metric: ks_statistic_celltype_interaction Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_celltype_interaction' score for 'srtsim'
Method 'srtsim' performs much worse than controls for metric' ks_statistic_celltype_interaction' Task: spatial_simulators Method: srtsim Metric: ks_statistic_celltype_interaction Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_celltype_interaction' score for 'srtsim'
Method 'srtsim' performs much better than controls for metric 'ks_statistic_celltype_interaction' Task: spatial_simulators Method: srtsim Metric: ks_statistic_celltype_interaction Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_celltype_interaction' score for 'symsim'
Method 'symsim' performs much worse than controls for metric' ks_statistic_celltype_interaction' Task: spatial_simulators Method: symsim Metric: ks_statistic_celltype_interaction Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_celltype_interaction' score for 'symsim'
Method 'symsim' performs much better than controls for metric 'ks_statistic_celltype_interaction' Task: spatial_simulators Method: symsim Metric: ks_statistic_celltype_interaction Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_celltype_interaction' score for 'zinbwave'
Method 'zinbwave' performs much worse than controls for metric' ks_statistic_celltype_interaction' Task: spatial_simulators Method: zinbwave Metric: ks_statistic_celltype_interaction Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_celltype_interaction' score for 'zinbwave'
Method 'zinbwave' performs much better than controls for metric 'ks_statistic_celltype_interaction' Task: spatial_simulators Method: zinbwave Metric: ks_statistic_celltype_interaction Best score: NaN Percentage outside range: 0%
- error Scaling Metric 'ks_statistic_enrichment' % outside range
Percentage of scaled scores outside control range should be less than 10% Task: spatial_simulators Metric: ks_statistic_enrichment Inside range: NA Scaled scores: 90 Percentage outside: NA%
- error Scaling Worst 'ks_statistic_enrichment' score for 'negative'
Method 'negative' performs much worse than controls for metric' ks_statistic_enrichment' Task: spatial_simulators Method: negative Metric: ks_statistic_enrichment Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_enrichment' score for 'negative'
Method 'negative' performs much better than controls for metric 'ks_statistic_enrichment' Task: spatial_simulators Method: negative Metric: ks_statistic_enrichment Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_enrichment' score for 'positive'
Method 'positive' performs much worse than controls for metric' ks_statistic_enrichment' Task: spatial_simulators Method: positive Metric: ks_statistic_enrichment Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_enrichment' score for 'positive'
Method 'positive' performs much better than controls for metric 'ks_statistic_enrichment' Task: spatial_simulators Method: positive Metric: ks_statistic_enrichment Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_enrichment' score for 'scdesign2'
Method 'scdesign2' performs much worse than controls for metric' ks_statistic_enrichment' Task: spatial_simulators Method: scdesign2 Metric: ks_statistic_enrichment Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_enrichment' score for 'scdesign2'
Method 'scdesign2' performs much better than controls for metric 'ks_statistic_enrichment' Task: spatial_simulators Method: scdesign2 Metric: ks_statistic_enrichment Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_enrichment' score for 'scdesign3'
Method 'scdesign3' performs much worse than controls for metric' ks_statistic_enrichment' Task: spatial_simulators Method: scdesign3 Metric: ks_statistic_enrichment Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_enrichment' score for 'scdesign3'
Method 'scdesign3' performs much better than controls for metric 'ks_statistic_enrichment' Task: spatial_simulators Method: scdesign3 Metric: ks_statistic_enrichment Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_enrichment' score for 'sparsim'
Method 'sparsim' performs much worse than controls for metric' ks_statistic_enrichment' Task: spatial_simulators Method: sparsim Metric: ks_statistic_enrichment Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_enrichment' score for 'sparsim'
Method 'sparsim' performs much better than controls for metric 'ks_statistic_enrichment' Task: spatial_simulators Method: sparsim Metric: ks_statistic_enrichment Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_enrichment' score for 'splatter'
Method 'splatter' performs much worse than controls for metric' ks_statistic_enrichment' Task: spatial_simulators Method: splatter Metric: ks_statistic_enrichment Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_enrichment' score for 'splatter'
Method 'splatter' performs much better than controls for metric 'ks_statistic_enrichment' Task: spatial_simulators Method: splatter Metric: ks_statistic_enrichment Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_enrichment' score for 'srtsim'
Method 'srtsim' performs much worse than controls for metric' ks_statistic_enrichment' Task: spatial_simulators Method: srtsim Metric: ks_statistic_enrichment Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_enrichment' score for 'srtsim'
Method 'srtsim' performs much better than controls for metric 'ks_statistic_enrichment' Task: spatial_simulators Method: srtsim Metric: ks_statistic_enrichment Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_enrichment' score for 'symsim'
Method 'symsim' performs much worse than controls for metric' ks_statistic_enrichment' Task: spatial_simulators Method: symsim Metric: ks_statistic_enrichment Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_enrichment' score for 'symsim'
Method 'symsim' performs much better than controls for metric 'ks_statistic_enrichment' Task: spatial_simulators Method: symsim Metric: ks_statistic_enrichment Best score: NaN Percentage outside range: 0%
- error Scaling Worst 'ks_statistic_enrichment' score for 'zinbwave'
Method 'zinbwave' performs much worse than controls for metric' ks_statistic_enrichment' Task: spatial_simulators Method: zinbwave Metric: ks_statistic_enrichment Worst score: NaN Percentage outside range: 0%
- error Scaling Best 'ks_statistic_enrichment' score for 'zinbwave'
Method 'zinbwave' performs much better than controls for metric 'ks_statistic_enrichment' Task: spatial_simulators Method: zinbwave Metric: ks_statistic_enrichment Best score: NaN Percentage outside range: 0%
Show 8 warnings
- warning Raw results Dataset 'brain' % missing
Percentage of missing results should be less than 10% Task: spatial_simulators Dataset: brain Number of results: 205 Expected number of results: 261 Percentage missing: 21%
- warning Raw results Metric 'svg_recall' % missing
Percentage of missing results should be less than 10% Task: spatial_simulators Metric: svg_recall Number of results: 71 Expected number of results: 90 Percentage missing: 21%
- warning Raw results Metric 'svg_precision' % missing
Percentage of missing results should be less than 10% Task: spatial_simulators Metric: svg_precision Number of results: 64 Expected number of results: 90 Percentage missing: 29%
- warning Raw results Metric 'ks_statistic_L_stats' % missing
Percentage of missing results should be less than 10% Task: spatial_simulators Metric: ks_statistic_L_stats Number of results: 78 Expected number of results: 90 Percentage missing: 13%
- warning Raw results Metric 'ks_statistic_celltype_interaction' % missing
Percentage of missing results should be less than 10% Task: spatial_simulators Metric: ks_statistic_celltype_interaction Number of results: 78 Expected number of results: 90 Percentage missing: 13%
- warning Raw results Metric 'ks_statistic_nn_correlation' % missing
Percentage of missing results should be less than 10% Task: spatial_simulators Metric: ks_statistic_nn_correlation Number of results: 78 Expected number of results: 90 Percentage missing: 13%
- warning Raw results Metric 'ks_statistic_morans_I' % missing
Percentage of missing results should be less than 10% Task: spatial_simulators Metric: ks_statistic_morans_I Number of results: 78 Expected number of results: 90 Percentage missing: 13%
- warning Raw results Metric component 'ks_statistic_sc_features' % failed
Percentage of failed processes should be less than 10% Task: spatial_simulators Metric component: ks_statistic_sc_features Succeeded processes: 78 Attempted processes: 90 Percentage failed: 13%
Method info 7
A transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
scDesign2 is a transparent simulator that achieves all three goals (preserving genes, capturing gene correlations, and generating any number of cells with varying sequencing depths) and generates high-fidelity synthetic data for multiple single-cell gene expression count-based technologies.
A probabilistic model that unifies the generation and inference for single-cell and spatial omics data
scDesign3 offers a probabilistic model that unifies the generation and inference for single-cell and spatial omics data. The model's interpretable parameters and likelihood enable scDesign3 to generate customized in silico data and unsupervisedly assess the goodness-of-fit of inferred cell latent structures (for example, clusters, trajectories and spatial locations).
SPARSim single cell is a count data simulator for scRNA-seq data.
SPARSim is a scRNA-seq count data simulator based on a Gamma-Multivariate Hypergeometric model. It allows to generate count data that resembles real data in terms of count intensity, variability and sparsity.
A single cell RNA-seq data simulator based on a gamma-Poisson distribution.
The Splat model is a gamma-Poisson distribution used to generate a gene by cell matrix of counts. Mean expression levels for each gene are simulated from a gamma distribution and the Biological Coefficient of Variation is used to enforce a mean-variance trend before counts are simulated from a Poisson distribution.
An SRT-specific simulator for scalable, reproducible, and realistic SRT simulations.
A key benefit of srtsim is its ability to maintain location-wise and gene-wise SRT count properties and preserve spatial expression patterns, enabling evaluation of SRT method performance using synthetic data.
Simulating multiple faceted variability in single cell RNA sequencing
SymSim is a simulator for modeling single-cell RNA-Seq data, accounting for three primary sources of variation: intrinsic transcription noise, extrinsic variation from different cell states, and technical variation from measurement noise and bias.
A general and flexible method for signal extraction from single-cell RNA-seq data
ZINB-WaVE is a general and flexible zero-inflated negative binomial model, which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data.
Control method info 2
A negative control method.
A positive control method.
Metric info 29
KS statistic of the celltype interaction
The Kolmogorov-Smirnov statistic comparing the Celltype interaction in the real datasets versus the Celltype interaction in the simulated datasets.
Ks Statistic of the centralized score matrix.
The Kolmogorov-Smirnov statistic comparing the centralized score matrix of the real dataset versus the simulated dataset. The centralized score matrix is a vector of the group degree centrality (inter-cluster connectivity), average clustering coefficient (propensity for a spot within a spatial cluster to be connected to spots in another cluster), and the group closeness centrality (relative proximity or accessibility of one cluster to all spots in another).
ARI
Adjusted Rand Index used in spatial clustering to measure the similarity between two data clusterings, adjusted for chance.
NMI
Normalized Mutual Information used in spatial clustering to measure the agreement between two different clusterings, scaled to [0, 1].
Cosine Similarity
Cosine similarity used in spatial cross-correlation to measure the cosine of the angle between two non-zero vectors.
Mantel Statistic
Mantel statistic used in spatial cross-correlation to test the correlation between two distance matrices.
JSD
Jensen-Shannon Divergence used in cell type deconvolution to measure the similarity between two probability distributions.
RMSE
Root Mean Squared Error used in cell type deconvolution to measure the difference between observed and predicted values.
KS statistic of the effective library size.
The Kolmogorov-Smirnov statistic comparing the effective library size of the real datasets versus the effective library size of the simulated datasets.
KS statistic of the effective library size.
The Kolmogorov-Smirnov statistic comparing the morans I of the real datasets versus the morans I of the simulated datasets.
KS statistic of the fraction of zeros per spot (cell).
The Kolmogorov-Smirnov statistic comparing the fraction of zeros per spot (cell) in the real datasets versus the fraction of zeros per spot (cell) in the simulated datasets.
KS statistic of the fraction of zeros per gene.
The Kolmogorov-Smirnov statistic comparing the fraction of zeros per gene in the real datasets versus the fraction of zeros per gene in the simulated datasets.
KS statistic of the gene Pearson correlation.
The Kolmogorov-Smirnov statistic comparing the gene Pearson correlation of the real datasets versus the gene Pearson correlation of the simulated datasets.
KS statistic of the L statistics
The Kolmogorov-Smirnov statistic comparing the L statistics in the real datasets versus the L statistics in the simulated datasets.
KS statistic of the library size.
The Kolmogorov-Smirnov statistic comparing the total sum of UMI counts across all genes in the real datasets versus the total sum of UMI counts across all genes in the simmulated datasets.
KS statistic of the library size.
The Kolmogorov-Smirnov statistic comparing the nn correlation in the real datasets versus the nn correlation in the simmulated datasets.
KS statistic of the relationship between library size and the proportion of zeros per spot (cell).
The Kolmogorov-Smirnov statistic comparing the relationship between library size and the proportion of zeros per spot (cell) in the real datasets versus the simulated datasets.
KS statistic of the relationship between mean expression and the proportion of zero per gene.
The Kolmogorov-Smirnov statistic comparing the relationship between mean expression and the proportion of zero per gene in the real datasets versus the simulated datasets.
KS statistic of the relationship between mean expression and variance expression.
The Kolmogorov-Smirnov statistic comparing the relationship between mean expression and variance expression in the real datasets versus the simulated datasets.
Ks Statistic of the neighborhood enrichment.
The Kolmogorov-Smirnov statistic comparing the neighborhood enrichment matrices of the real dataset versus the simulated dataset. The neighborhood enrichment matrix quantifies the enrichment observed between each pair of spatial clusters. It serves to systematically assess the interaction between different clusters within a spatial context, providing insights into the relative connectivity between various spatial clusters.
KS statistic of the sample Pearson correlation.
The Kolmogorov-Smirnov statistic comparing the sample Pearson correlation of the real datasets versus the sample Pearson correlation of the simulated datasets.
KS statistic of the spot- (or cell-) level scaled mean of the expression matrix.
The Kolmogorov-Smirnov statistic comparing the z-score standardization of the mean of expression matrix in terms of log2(CPM) in the real datasets versus the simulated datasets.
KS statistic of the gene-level scaled mean of the expression matrix.
The Kolmogorov-Smirnov statistic comparing the gene-level z-score standardization of the mean of expression matrix in terms of log2(CPM) in the real datasets versus the simulated datasets.
KS statistic of the spot- (or cell-) level scaled variance of the expression matrix.
The Kolmogorov-Smirnov statistic comparing the spot-level z-score standardization of the variance of expression matrix in terms of log2(CPM) in the real datasets versus the simulated datasets.
KS statistic of the gene-level scaled variance of the expression matrix.
The Kolmogorov-Smirnov statistic comparing the gene-level z-score standardization of the variance of expression matrix in terms of log2(CPM) in the real datasets versus the simulated datasets.
Precision (Spatial Variable Gene)
Precision used in identifying spatial variable genes, measuring the accuracy of positive predictions.
Recall
Recall used in identifying spatial variable genes, measuring the true positive rate.
KS statistic of the weight trimmed mean of M-values normalization factor (TMM).
The Kolmogorov-Smirnov statistic comparing the weight trimmed mean of M-values normalization factor for the real datasets versus the weight trimmed mean of M-values normalization factor for the simulated datasets.
KS Statistic of the transition matrix.
The Kolmogorov-Smirnov statistic comparing the transition matrix of the real dataset versus the simulated dataset. The transition matrix elucidates the interrelationships among spatial clusters in each space. Each element in the matrix signifies the transition probability from one spatial cluster to another, thereby mapping the dynamic interplay of spatial clusters.
Dataset info 10
10X Visium spatial RNA-seq from adult mouse brain sections paired to single-nucleus RNA-seq
This datasets were generated matched single nucleus (sn, this submission) and Visium spatial RNA-seq (10X Genomics) profiles of adjacent mouse brain sections that contain multiple regions from the telencephalon and diencephalon.
A spatially resolved atlas of human breast cancers
This study presents a spatially resolved transcriptomics analysis of human breast cancers.
Scripts and source data for image processing, barcode calling, and cell type annotations in a seqFISH+ experiment.
The dataset includes processed image data, cell type annotations with Louvain clusters, gene IDs for transcript locations, and mRNA point locations, with additional data available on Zenodo.
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of Tumor A1 of Tissue 1
Spatial transcriptomics of Tumor A1 of Tissue 1.
single-cell and spatial transcriptomic molecular map of mouse gastrulation
Single-Cell omics Data across Mouse Gastrulation and Highly multiplexed spatially resolved gene expression profiling of Early Organogenesis.
Spatial RNA sequencing of regenerating mouse hindlimb muscle
The spatial transcriptomics datasets regenerates mouse muscle tissue generated with the 10x Genomics Visium platform.
Single-cell and spatial transcriptomic of mouse olfactory bulb
Spatial profiling of human osteosarcoma cells.
Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression.
Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas
We developed a multimodal intersection analysis method combining scRNA-seq with spatial transcriptomics to map and characterize the spatial organization and interactions of distinct cell subpopulations in complex tissues, such as primary pancreatic tumors..
Spatially resolved gene expression of human protate tissue slices treated with steroid hormones for 8 hours
Spatially resolved gene expression was prepard by dissociated hman prostate tissue to single cells, and collected & prepped for RNA-seq using the Visium Spatial Gene Expression kit.
References
- Journal of Machine Learning Technologies. (n.d.). 10.9735/2229-3981 ↗
- Baruzzo, G., Patuzzi, I., & Di Camillo, B. (2019). SPARSim single cell: a count data simulator for scRNA-seq data. 10.1093/bioinformatics/btz752 ↗
- Chacón, J. E., & Duong, T. (2018). Multivariate Kernel Smoothing and its Applications. 10.1201/9780429485572 ↗
- Drost, H.-G. (2018). Philentropy: Information Theory and Distance Quantification with R. 10.21105/joss.00765 ↗
- Eng, C.-H. L., Lawson, M., Zhu, Q., Dries, R., Koulena, N., Takei, Y., Yun, J., Cronin, C., Karp, C., Yuan, G.-C., & Cai, L. (2019). Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+. 10.1038/s41586-019-1049-y ↗
- Hodson, T. O. (2022). Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not. 10.5194/gmd-15-5481-2022 ↗
- Kleshchevnikov, V., Shmatko, A., Dann, E., Aivazidis, A., King, H. W., Li, T., Elmentaite, R., Lomakin, A., Kedlian, V., Gayoso, A., Jain, M. S., Park, J. S., Ramona, L., Tuck, E., Arutyunyan, A., Vento-Tormo, R., Gerstung, M., James, L., Stegle, O., & Bayraktar, O. A. (2022). Cell2location maps fine-grained cell types in spatial transcriptomics. Nature Biotechnology, 40(5), 661–671. 10.1038/s41587-021-01139-4 ↗
- Legendre, P., Fortin, M., & Borcard, D. (2015). Should the Mantel test be used in spatial analysis? 10.1111/2041-210x.12425 ↗
- Leydesdorff, L. (2005). Similarity measures, author cocitation analysis, and information theory. 10.1002/asi.20130 ↗
- Liang, X., Cao, Y., & Hwa Yang, J. Y. (2024). Multi-task benchmarking of spatially resolved gene expression simulation models. 10.1101/2024.05.29.596418 ↗
- Lohoff, T., Ghazanfar, S., Missarova, A., Koulena, N., Pierson, N., Griffiths, J. A., Bardot, E. S., Eng, C.-H. L., Tyser, R. C. V., Argelaguet, R., Guibentif, C., Srinivas, S., Briscoe, J., Simons, B. D., Hadjantonakis, A.-K., Göttgens, B., Reik, W., Nichols, J., Cai, L., & Marioni, J. C. (2021). Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. 10.1038/s41587-021-01006-2 ↗
- Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., Ergen, C., Addadi, Y., Golani, O., Ronchese, F., Jordan, M. I., Amit, I., & Yosef, N. (2022). DestVI identifies continuums of cell types in spatial transcriptomics data. Nature Biotechnology, 40(9), 1360–1369. 10.1038/s41587-022-01272-8 ↗
- McCray, T., Pacheco, J. V., Loitz, C. C., Garcia, J., Baumann, B., Schlicht, M. J., Valyi-Nagy, K., Abern, M. R., & Nonn, L. (2021). Vitamin D sufficiency enhances differentiation of patient-derived prostate epithelial organoids. 10.1016/j.isci.2021.102640 ↗
- McKellar, D. W., Walter, L. D., Song, L. T., Mantri, M., Wang, M. F. Z., De Vlaminck, I., & Cosgrove, B. D. (2020). Strength in numbers: Large-scale integration of single-cell transcriptomic data reveals rare, transient muscle progenitor cell states in muscle regeneration. 10.1101/2020.12.01.407460 ↗
- Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S., & Vert, J.-P. (2018). A general and flexible method for signal extraction from single-cell RNA-seq data. 10.1038/s41467-017-02554-5 ↗
- Song, D., Wang, Q., Yan, G., Liu, T., Sun, T., & Li, J. J. (2023). scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. 10.1038/s41587-023-01772-1 ↗
- Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., Mollbrink, A., Linnarsson, S., Codeluppi, S., Borg, Å., Pontén, F., Costea, P. I., Sahlén, P., Mulder, J., Bergmann, O., … Frisén, J. (2016). Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. 10.1126/science.aaf2403 ↗
- Sun, T., Song, D., Li, W. V., & Li, J. J. (2021). scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured. 10.1186/s13059-021-02367-2 ↗
- Vinh, N. X., Epps, J., & Bailey, J. (2009). Information theoretic measures for clusterings comparison. 10.1145/1553374.1553511 ↗
- Wu, S. Z., Al-Eryani, G., Roden, D. L., Junankar, S., Harvey, K., Andersson, A., Thennavan, A., Wang, C., Torpy, J. R., Bartonicek, N., Wang, T., Larsson, L., Kaczorowski, D., Weisenfeld, N. I., Uytingco, C. R., Chew, J. G., Bent, Z. W., Chan, C.-L., Gnanasambandapillai, V., … Swarbrick, A. (2021). A single-cell and spatially resolved atlas of human breast cancers. Nature Genetics, 53(9), 1334–1347. 10.1038/s41588-021-00911-1 ↗
- Xia, C., Fan, J., Emanuel, G., Hao, J., & Zhuang, X. (2019). Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression. 10.1073/pnas.1912459116 ↗
- Zappia, L., Phipson, B., & Oshlack, A. (2017). Splatter: simulation of single-cell RNA sequencing data. 10.1186/s13059-017-1305-0 ↗
- Zhang, X., Xu, C., & Yosef, N. (2019). Simulating multiple faceted variability in single cell RNA sequencing. 10.1038/s41467-019-10500-w ↗
- Zhu, J., Shang, L., & Zhou, X. (2023). SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics. 10.1186/s13059-023-02879-z ↗