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.

Overview of the Spatial Registration method

  1. Perform gene set enrichment analysis between spatial features (ex. anatomical features, histological layers) on reference spatial data set. Or access existing statistics.

  2. Perform gene set enrichment analysis between features (ex. new annotations, data-driven clusters) on new query data set.

  3. Correlate the \(t\)-statistics between the reference and query features.

  4. Annotate new spatial features with the most strongly associated reference feature.

  5. Plot correlation heat map to observe patterns between the two data sets.

Spatial Registration Overview
Spatial Registration Overview

How to run Spatial Registration with spatialLIBD tools

Introduction.

In this example we will utilize the human DLPFC 10x Genomics Visium dataset from Maynard, Collado-Torres et al. (Maynard, Collado-Torres, Weber, Uytingco, Barry, Williams, II, Tran, Besich, Tippani, Chew, Yin, Kleinman, Hyde, Rao, Hicks, Martinowich, and Jaffe, 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.

Dotplot of sample from refernce DLPFC data, colored by annotated layers
Dotplot of sample from refernce DLPFC data, colored by annotated layers

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.

Important Notes

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.

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 (2023). "Integrated single cell and
#>   unsupervised spatial transcriptomic analysis defines molecular
#>   anatomy of the human dorsolateral prefrontal cortex." _bioRxiv_.
#>   doi:10.1101/2023.02.15.528722
#>   <https://doi.org/10.1101/2023.02.15.528722>,
#>   <https://www.biorxiv.org/content/10.1101/2023.02.15.528722v1>.
#> 
#>   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." _bioRxiv_. doi:10.1101/2023.04.20.537710
#>   <https://doi.org/10.1101/2023.04.20.537710>,
#>   <https://www.biorxiv.org/content/10.1101/2023.04.20.537710v1>.
#> 
#> To see these entries in BibTeX format, use 'print(<citation>,
#> bibtex=TRUE)', 'toBibtex(.)', or set
#> 'options(citation.bibtex.max=999)'.

Setup

Install spatialLIBD

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("spatialLIBD")

## Check that you have a valid Bioconductor installation
BiocManager::valid()

Load required packages

library("spatialLIBD")
library("SingleCellExperiment")

Download Data

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")
#> 2024-02-21 18:06:22.511014 loading file /github/home/.cache/R/BiocFileCache/108e27d9587e_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   -0.03376771

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.tran

DLPFC 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 
#>        782        529        773        524        132        187        243 
#>    Inhib_A    Inhib_B    Inhib_C    Inhib_D    Inhib_E    Inhib_F Macrophage 
#>        333        454        365        413          7          8         10 
#>      Micro      Mural      Oligo        OPC      Tcell 
#>        388         18       5455        572          9

The 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
#>   donor1   371     111      75      44      22      77     102      39      98
#>   donor2   137     120     154     155      27      25      36      89     106
#>   donor6   274     298     544     325      83      85     105     205     250
#>         
#>          Inhib_C Inhib_D Inhib_E Inhib_F Macrophage Micro Mural Oligo  OPC
#>   donor1      47     119       2       0          1   152     3  2754  196
#>   donor2      56      78       2       1          3    92     2   517   91
#>   donor6     262     216       3       7          6   144    13  2184  285
#>         
#>          Tcell
#>   donor1     2
#>   donor2     2
#>   donor6     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

  1. registration_pseudobulk(): Pseudo-bulks data, filter low expressed genes, and normalize counts
  2. registration_mod(): Defines the statistical model that will be used for computing the block correlation
  3. registration_block_cor() : Computes the block correlation using the sample ID as the blocking factor, used as correlation in eBayes call
  4. registration_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"
)
#> 2024-02-21 18:06:30.024237 make pseudobulk object
#> 2024-02-21 18:06:31.696606 dropping 13 pseudo-bulked samples that are below 'min_ncells'.
#> 2024-02-21 18:06:31.755092 drop lowly expressed genes
#> 2024-02-21 18:06:31.971529 normalize expression
#> 2024-02-21 18:06:33.414303 create model matrix
#> 2024-02-21 18:06:33.438212 run duplicateCorrelation()
#> 2024-02-21 18:06:47.025659 The estimated correlation is: 0.138734774807097
#> 2024-02-21 18:06:47.028159 computing enrichment statistics
#> 2024-02-21 18:06:48.081117 extract and reformat enrichment results
#> 2024-02-21 18:06:48.133244 running the baseline pairwise model
#> 2024-02-21 18:06:48.289949 computing pairwise statistics
#> 2024-02-21 18:06:49.635123 computing F-statistics

Extract Enrichment t-statistics

## extract t-statics and rename
registration_t_stats <- sce_modeling_results$enrichment[, grep("^t_stat", colnames(sce_modeling_results$enrichment))]
colnames(registration_t_stats) <- gsub("^t_stat_", "", colnames(registration_t_stats))

## cell types x gene
dim(registration_t_stats)
#> [1] 18620    15

## check out table
registration_t_stats[1:5, 1:5]
#>                       Astro    Excit_A    Excit_B    Excit_C    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 -1.0111055

Correlate statsics with Layer Reference

cor_layer <- layer_stat_cor(
    stats = registration_t_stats,
    modeling_results = layer_modeling_results,
    model_type = "enrichment",
    top_n = 100
)

cor_layer
#>                 WM       Layer6      Layer5       Layer4      Layer3
#> Oligo    0.7536847 -0.038947167 -0.22313462 -0.216143460 -0.39418956
#> Astro    0.2902852 -0.215372148 -0.32009814 -0.320189919 -0.24920587
#> OPC      0.3309766 -0.076892980 -0.19246279 -0.254222683 -0.21417502
#> Micro    0.2548264 -0.066391136 -0.14907964 -0.132236668 -0.18353524
#> Mural    0.1652788 -0.046487411 -0.12107471 -0.190269592 -0.11924637
#> Excit_B -0.3718199 -0.119246604 -0.26805619 -0.034391012  0.57980667
#> Excit_C -0.5232250 -0.187359062  0.01974092  0.381935813  0.69540412
#> Excit_A -0.3902148  0.128043074  0.63735797  0.405168995  0.06011232
#> Excit_E -0.3651414  0.489930498  0.13044242 -0.001907714  0.15141352
#> Excit_F -0.2108888  0.443748447  0.35812706 -0.064476608 -0.09199288
#> Inhib_A -0.2497876 -0.035402374  0.08242644  0.105770587  0.12358824
#> Inhib_C -0.2100641 -0.080878595  0.05282252  0.052311336  0.09462094
#> Excit_D -0.3363905 -0.004936028  0.29430601  0.402875525  0.19153576
#> Inhib_B -0.1574240 -0.016415116  0.15172576  0.190096545  0.05317902
#> Inhib_D -0.2224211 -0.062788566  0.20260766  0.314434866  0.08963108
#>               Layer2      Layer1
#> Oligo   -0.336272668 -0.04559186
#> Astro   -0.128017818  0.66950829
#> OPC     -0.073209686  0.22845154
#> Micro   -0.118307525  0.19395081
#> Mural   -0.076057486  0.24642004
#> Excit_B  0.675251157 -0.12563319
#> Excit_C  0.326841133 -0.26729177
#> Excit_A -0.171933501 -0.38823893
#> Excit_E  0.194625039 -0.33496664
#> Excit_F -0.012550826 -0.30917103
#> Inhib_A  0.133982729  0.01021479
#> Inhib_C  0.160416496  0.06801709
#> Excit_D -0.014930006 -0.27248937
#> Inhib_B -0.004791031 -0.09307309
#> Inhib_D -0.067981519 -0.09229913

Explore Results

Now we can use these correlation values to learn about the cell types.

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, max = max(cor_layer))

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
#> 1    Oligo             good          WM
#> 2    Astro             good          L1
#> 3      OPC             good          WM
#> 4    Micro             good          WM
#> 5    Mural             poor         L1*
#> 6  Excit_B             good        L2/3
#> 7  Excit_C             good          L3
#> 8  Excit_A             good          L5
#> 9  Excit_E             good          L6
#> 10 Excit_F             good        L6/5
#> 11 Inhib_A             poor     L2/3/4*
#> 12 Inhib_C             poor         L2*
#> 13 Excit_D             good          L4
#> 14 Inhib_B             poor         L4*
#> 15 Inhib_D             good          L4

Reproducibility

The spatialLIBD package (Pardo, Spangler, Weber, Hicks, Jaffe, Martinowich, Maynard, and Collado-Torres, 2022) was made possible thanks to:

  • R (R Core Team, 2023)
  • BiocStyle (Oleś, 2023)
  • knitr (Xie, 2023)
  • RefManageR (McLean, 2017)
  • rmarkdown (Allaire, Xie, Dervieux, McPherson, Luraschi, Ushey, Atkins, Wickham, Cheng, Chang, and Iannone, 2023)
  • sessioninfo (Wickham, Chang, Flight, Müller, and Hester, 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] "2024-02-21 18:06:51 UTC"

Wallclock time spent generating the vignette.

#> Time difference of 41.633 secs

R session information.

#> ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
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#>  version  R version 4.3.2 (2023-10-31)
#>  os       Ubuntu 22.04.3 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language en
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       UTC
#>  date     2024-02-21
#>  pandoc   3.1.1 @ /usr/local/bin/ (via rmarkdown)
#> 
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This vignette was generated using BiocStyle (Oleś, 2023) with knitr (Xie, 2023) and rmarkdown (Allaire, Xie, Dervieux et al., 2023) running behind the scenes.

Citations made with RefManageR (McLean, 2017).

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