Guide to Spatial Registration
17 December 2024
Package
spatialLIBD 1.19.6
1 What is Spatial Registration?
Spatial Registration is an analysis that compares the gene expression of groups in a query RNA-seq data set (typically spatially resolved RNA-seq or single cell RNA-seq) to groups in a reference spatially resolved RNA-seq data set (such annotated anatomical features).
For spatial data, this can be helpful to compare manual annotations, or annotating clusters. For scRNA-seq data it can check if a cell type might be more concentrated in one area or anatomical feature of the tissue.
The spatial annotation process correlates the \(t\)-statistics from the gene enrichment analysis between spatial features from the reference data set, with the \(t\)-statistics from the gene enrichment of features in the query data set. Pairs with high positive correlation show where similar patterns of gene expression are occurring and what anatomical feature the new spatial feature or cell population may map to.
1.1 Overview of the Spatial Registration method
Perform gene set enrichment analysis between spatial features (ex. anatomical features, histological layers) on reference spatial data set. Or access existing statistics.
Perform gene set enrichment analysis between features (ex. new annotations, data-driven clusters) on new query data set.
Correlate the \(t\)-statistics between the reference and query features.
Annotate new spatial features with the most strongly associated reference feature.
Plot correlation heat map to observe patterns between the two data sets.
2 How to run Spatial Registration with spatialLIBD tools
2.1 Introduction.
In this example we will utilize the human DLPFC 10x Genomics Visium dataset from Maynard, Collado-Torres et al. (Maynard, Collado-Torres, Weber et al., 2021) as the reference. This data contains manually annotated features: the six cortical layers + white matter present in the DLPFC. We will use the pre-calculated enrichment statistics for the layers, which are available from spatialLIBD.
The query dataset will be the DLPFC single nucleus RNA-seq (snRNA-seq) data from (Tran, Maynard, Spangler, Huuki, Montgomery, Sadashivaiah, Tippani, Barry, Hancock, Hicks, Kleinman, Hyde, Collado-Torres, Jaffe, and Martinowich, 2021).
We will compare the gene expression in the cell type populations of the query dataset to the annotated layers in the reference.
2.2 Important Notes
2.2.1 Required knowledge
It may be helpful to review Introduction to spatialLIBD vignette available through GitHub or Bioconductor for more information about this data set and R package.
2.2.2 Citing spatialLIBD
We hope that spatialLIBD will be useful for your research. Please use the following information to cite the package and the overall approach. Thank you!
## Citation info
citation("spatialLIBD")
#> To cite package 'spatialLIBD' in publications use:
#>
#> Pardo B, Spangler A, Weber LM, Hicks SC, Jaffe AE, Martinowich K, Maynard KR, Collado-Torres L (2022).
#> "spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data." _BMC
#> Genomics_. doi:10.1186/s12864-022-08601-w <https://doi.org/10.1186/s12864-022-08601-w>,
#> <https://doi.org/10.1186/s12864-022-08601-w>.
#>
#> Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK, Williams SR, II JLC, Tran MN, Besich Z,
#> Tippani M, Chew J, Yin Y, Kleinman JE, Hyde TM, Rao N, Hicks SC, Martinowich K, Jaffe AE (2021).
#> "Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex." _Nature
#> Neuroscience_. doi:10.1038/s41593-020-00787-0 <https://doi.org/10.1038/s41593-020-00787-0>,
#> <https://www.nature.com/articles/s41593-020-00787-0>.
#>
#> Huuki-Myers LA, Spangler A, Eagles NJ, Montgomergy KD, Kwon SH, Guo B, Grant-Peters M, Divecha HR,
#> Tippani M, Sriworarat C, Nguyen AB, Ravichandran P, Tran MN, Seyedian A, Consortium P, Hyde TM, Kleinman
#> JE, Battle A, Page SC, Ryten M, Hicks SC, Martinowich K, Collado-Torres L, Maynard KR (2024). "A
#> data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex." _Science_.
#> doi:10.1126/science.adh1938 <https://doi.org/10.1126/science.adh1938>,
#> <https://doi.org/10.1126/science.adh1938>.
#>
#> Kwon SH, Parthiban S, Tippani M, Divecha HR, Eagles NJ, Lobana JS, Williams SR, Mark M, Bharadwaj RA,
#> Kleinman JE, Hyde TM, Page SC, Hicks SC, Martinowich K, Maynard KR, Collado-Torres L (2023). "Influence
#> of Alzheimer’s disease related neuropathology on local microenvironment gene expression in the human
#> inferior temporal cortex." _GEN Biotechnology_. doi:10.1089/genbio.2023.0019
#> <https://doi.org/10.1089/genbio.2023.0019>, <https://doi.org/10.1089/genbio.2023.0019>.
#>
#> To see these entries in BibTeX format, use 'print(<citation>, bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.2.3 Setup
2.3.1 Install spatialLIBD
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("spatialLIBD")
## Check that you have a valid Bioconductor installation
BiocManager::valid()2.3.2 Load required packages
library("spatialLIBD")
library("SingleCellExperiment")2.4 Download Data
2.4.1 Spatial Reference
The reference data is easily accessed through spatialLIBD. The modeling results
for the annotated layers is already calculated and can be accessed with the fetch_data() function.
This data contains the results form three models (anova, enrichment, and pairwise), we will use the enrichment results for spatial registration. The tables contain the \(t\)-statistics, p-values, and gene ensembl ID and symbol.
## get reference layer enrichment statistics
layer_modeling_results <- fetch_data(type = "modeling_results")
#> adding rname 'https://www.dropbox.com/s/se6rrgb9yhm5gfh/Human_DLPFC_Visium_modeling_results.Rdata?dl=1'
#> 2024-12-17 11:41:32.158692 loading file /home/biocbuild/.cache/R/BiocFileCache/35f6b519a55991_Human_DLPFC_Visium_modeling_results.Rdata%3Fdl%3D1
layer_modeling_results$enrichment[1:5, 1:5]
#> t_stat_WM t_stat_Layer1 t_stat_Layer2 t_stat_Layer3 t_stat_Layer4
#> 1 -0.6344143 -1.0321320 0.1781501 -0.72835965 1.56703859
#> 2 -2.4758891 1.2232062 -0.8733745 1.93793650 1.33150141
#> 3 -3.0079360 -0.8564572 2.1335852 0.48741121 0.35212807
#> 4 -1.2916584 -0.9494234 -0.9485440 0.56378302 -0.11206713
#> 5 2.3175897 0.6156900 0.1127478 -0.09907566 -0.033767712.4.2 Query Data: snRNA-seq
For the query data set, we will use the public single nucleus RNA-seq (snRNA-seq) data from (Tran, Maynard, Spangler et al., 2021) can be accessed on github.
This data is also from postmortem human brain DLPFC, and contains gene expression data for 11k nuclei and 19 cell types.
We will use BiocFileCache() to cache this data. It is stored as a SingleCellExperiment
object named sce.dlpfc.tran, and takes 1.01 GB of RAM memory to load.
# Download and save a local cache of the data available at:
# https://github.com/LieberInstitute/10xPilot_snRNAseq-human#processed-data
bfc <- BiocFileCache::BiocFileCache()
url <- paste0(
"https://libd-snrnaseq-pilot.s3.us-east-2.amazonaws.com/",
"SCE_DLPFC-n3_tran-etal.rda"
)
local_data <- BiocFileCache::bfcrpath(url, x = bfc)
load(local_data, verbose = TRUE)
#> Loading objects:
#> sce.dlpfc.tranDLPFC tissue consists of many cell types, some are quite rare and will not have enough data to complete the analysis
table(sce.dlpfc.tran$cellType)
#>
#> Astro Excit_A Excit_B Excit_C Excit_D Excit_E Excit_F Inhib_A Inhib_B Inhib_C
#> 782 529 773 524 132 187 243 333 454 365
#> Inhib_D Inhib_E Inhib_F Macrophage Micro Mural Oligo OPC Tcell
#> 413 7 8 10 388 18 5455 572 9The data will be pseudo-bulked over donor x cellType, it is recommended to drop
groups with < 10 nuclei (this is done automatically in the pseudobulk step).
table(sce.dlpfc.tran$donor, sce.dlpfc.tran$cellType)
#>
#> Astro Excit_A Excit_B Excit_C Excit_D Excit_E Excit_F Inhib_A Inhib_B Inhib_C Inhib_D Inhib_E Inhib_F
#> donor1 371 111 75 44 22 77 102 39 98 47 119 2 0
#> donor2 137 120 154 155 27 25 36 89 106 56 78 2 1
#> donor6 274 298 544 325 83 85 105 205 250 262 216 3 7
#>
#> Macrophage Micro Mural Oligo OPC Tcell
#> donor1 1 152 3 2754 196 2
#> donor2 3 92 2 517 91 2
#> donor6 6 144 13 2184 285 52.5 Get Enrichment statistics for snRNA-seq data
spatialLIBD contains many functions to compute modeling_results for the query sc/snRNA-seq or spatial data.
The process includes the following steps
registration_pseudobulk(): Pseudo-bulks data, filter low expressed genes, and normalize countsregistration_mod(): Defines the statistical model that will be used for computing the block correlationregistration_block_cor(): Computes the block correlation using the sample ID as the blocking factor, used as correlation in eBayes callregistration_stats_enrichment(): Computes the gene enrichment \(t\)-statistics (one group vs. All other groups)
The function registration_wrapper() makes life easy by wrapping these functions together in to one step!
## Perform the spatial registration
sce_modeling_results <- registration_wrapper(
sce = sce.dlpfc.tran,
var_registration = "cellType",
var_sample_id = "donor",
gene_ensembl = "gene_id",
gene_name = "gene_name"
)
#> 2024-12-17 11:41:40.065088 make pseudobulk object
#> 2024-12-17 11:41:42.317204 dropping 13 pseudo-bulked samples that are below 'min_ncells'.
#> 2024-12-17 11:41:42.363777 drop lowly expressed genes
#> 2024-12-17 11:41:42.523332 normalize expression
#> 2024-12-17 11:41:43.428346 create model matrix
#> 2024-12-17 11:41:43.466228 run duplicateCorrelation()
#> 2024-12-17 11:42:05.686213 The estimated correlation is: 0.138734774807097
#> 2024-12-17 11:42:05.690091 computing enrichment statistics
#> 2024-12-17 11:42:07.663257 extract and reformat enrichment results
#> 2024-12-17 11:42:07.781408 running the baseline pairwise model
#> 2024-12-17 11:42:08.128307 computing pairwise statistics
#> 2024-12-17 11:42:10.532644 computing F-statistics2.6 Extract Enrichment t-statistics
## check out table on enrichment t-statistics
sce_modeling_results$enrichment[1:5, 1:5]
#> t_stat_Astro t_stat_Excit_A t_stat_Excit_B t_stat_Excit_C t_stat_Excit_D
#> ENSG00000238009 -0.71009456 0.7957792 0.0497619 0.6825793 0.5526941
#> ENSG00000237491 -4.24672326 1.7724150 1.6819367 0.9047336 2.8222782
#> ENSG00000225880 0.06152726 0.6941825 0.9819037 -0.1958094 -0.7766439
#> ENSG00000223764 7.69037575 -0.4106009 -0.4107015 -0.4106542 -0.4107933
#> ENSG00000187634 10.14422194 -0.4721603 0.2733466 -0.1397438 -1.01110552.7 Correlate statsics with Layer Reference
cor_layer <- layer_stat_cor(
stats = sce_modeling_results$enrichment,
modeling_results = layer_modeling_results,
model_type = "enrichment",
top_n = 100
)
cor_layer
#> WM Layer6 Layer5 Layer4 Layer3 Layer2 Layer1
#> Oligo 0.7536847 -0.038947167 -0.22313462 -0.216143460 -0.39418956 -0.336272668 -0.04559186
#> Astro 0.2902852 -0.215372148 -0.32009814 -0.320189919 -0.24920587 -0.128017818 0.66950829
#> OPC 0.3309766 -0.076892980 -0.19246279 -0.254222683 -0.21417502 -0.073209686 0.22845154
#> Micro 0.2548264 -0.066391136 -0.14907964 -0.132236668 -0.18353524 -0.118307525 0.19395081
#> Mural 0.1652788 -0.046487411 -0.12107471 -0.190269592 -0.11924637 -0.076057486 0.24642004
#> Excit_B -0.3718199 -0.119246604 -0.26805619 -0.034391012 0.57980667 0.675251157 -0.12563319
#> Excit_C -0.5232250 -0.187359062 0.01974092 0.381935813 0.69540412 0.326841133 -0.26729177
#> Excit_A -0.3902148 0.128043074 0.63735797 0.405168995 0.06011232 -0.171933501 -0.38823893
#> Excit_E -0.3651414 0.489930498 0.13044242 -0.001907714 0.15141352 0.194625039 -0.33496664
#> Excit_F -0.2108888 0.443748447 0.35812706 -0.064476608 -0.09199288 -0.012550826 -0.30917103
#> Inhib_A -0.2497876 -0.035402374 0.08242644 0.105770587 0.12358824 0.133982729 0.01021479
#> Inhib_C -0.2100641 -0.080878595 0.05282252 0.052311336 0.09462094 0.160416496 0.06801709
#> Excit_D -0.3363905 -0.004936028 0.29430601 0.402875525 0.19153576 -0.014930006 -0.27248937
#> Inhib_B -0.1574240 -0.016415116 0.15172576 0.190096545 0.05317902 -0.004791031 -0.09307309
#> Inhib_D -0.2224211 -0.062788566 0.20260766 0.314434866 0.08963108 -0.067981519 -0.092299133 Explore Results
Now we can use these correlation values to learn about the cell types.
3.1 Create Heatmap of Correlations
We can see from this heatmap what layers the different cell types are associated with.
Oligo with WM
Astro with Layer 1
Excitatory neurons to different layers of the cortex
Weak associate with Inhibitory Neurons
layer_stat_cor_plot(cor_layer)
3.2 Annotate Cell Types by Top Correlation
We can use annotate_registered_clusters to create annotation labels for the
cell types based on the correlation values.
anno <- annotate_registered_clusters(
cor_stats_layer = cor_layer,
confidence_threshold = 0.25,
cutoff_merge_ratio = 0.25
)
anno
#> cluster layer_confidence layer_label layer_label_simple
#> 1 Oligo good WM WM
#> 2 Astro good Layer1 L1
#> 3 OPC good WM WM
#> 4 Micro good WM WM
#> 5 Mural poor Layer1* L1*
#> 6 Excit_B good Layer2/Layer3 L2/3
#> 7 Excit_C good Layer3 L3
#> 8 Excit_A good Layer5 L5
#> 9 Excit_E good Layer6 L6
#> 10 Excit_F good Layer6/Layer5 L6/5
#> 11 Inhib_A poor Layer2/Layer3/Layer4* L2/3/4*
#> 12 Inhib_C poor Layer2* L2*
#> 13 Excit_D good Layer4 L4
#> 14 Inhib_B poor Layer4* L4*
#> 15 Inhib_D good Layer4 L43.3 Plot Annotated Cell Types
Finally, we can update our heatmap with colors and annotations based on cluster registration for the snRNA-seq clusters.
layer_stat_cor_plot(
cor_layer,
query_colors = get_colors(clusters = rownames(cor_layer)),
reference_colors = libd_layer_colors,
annotation = anno,
cluster_rows = FALSE,
cluster_columns = FALSE
)
4 Reproducibility
The spatialLIBD package (Pardo, Spangler, Weber et al., 2022) was made possible thanks to:
- R (R Core Team, 2024)
- BiocStyle (Oleś, 2024)
- knitr (Xie, 2024)
- RefManageR (McLean, 2017)
- rmarkdown (Allaire, Xie, Dervieux et al., 2024)
- sessioninfo (Wickham, Chang, Flight et al., 2021)
- testthat (Wickham, 2011)
This package was developed using biocthis.
Code for creating the vignette
## Create the vignette
library("rmarkdown")
system.time(render("guide_to_spatial_registration.Rmd", "BiocStyle::html_document"))
## Extract the R code
library("knitr")
knit("guide_to_spatial_registration.Rmd", tangle = TRUE)Date the vignette was generated.
#> [1] "2024-12-17 11:42:15 EST"Wallclock time spent generating the vignette.
#> Time difference of 53.22 secsR session information.
#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
#> setting value
#> version R Under development (unstable) (2024-10-21 r87258)
#> os Ubuntu 24.04.1 LTS
#> system x86_64, linux-gnu
#> ui X11
#> language (EN)
#> collate C
#> ctype en_US.UTF-8
#> tz America/New_York
#> date 2024-12-17
#> pandoc 3.1.3 @ /usr/bin/ (via rmarkdown)
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#> ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────5 Bibliography
This vignette was generated using BiocStyle (Oleś, 2024) with knitr (Xie, 2024) and rmarkdown (Allaire, Xie, Dervieux et al., 2024) running behind the scenes.
Citations made with RefManageR (McLean, 2017).
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