vignettes/introduction.md
In Czh3/iSpatial: infer transcriptome-wide spatial expression via integration of scRNA and spRNA
title: "iSpatial Tutorial"
output:
pdf_document: default
html_document: default
vignette: |
%\VignetteIndexEntry{introduction} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
iSpatail Tutorial
Load package
Install iSpatial package from Github. iSpatial is based on Seurat, you need install Seurat first .
# install iSpatial package
# devtools::install_github("Czh3/iSpatial")
# load
library(iSpatial)
library(Seurat)
library(ggplot2)
Load data
Load scRNA-seq and merFISH of Mouse Nucleus Accumbens. The data are down-sampled from Renchao et al.
# scRNA-seq data
data(NA_scRNA)
# merFISH data
data(NA_merFISH)
Normalize data
The data need be log normalized.
# scRNA-seq data
NA_scRNA = NormalizeData(NA_scRNA, verbose = FALSE)
# merFISH data
NA_merFISH = NormalizeData(NA_merFISH, verbose = FALSE)
infer transcriptome-wide spatial expression
# infer spatial transcriptome
NA_merFISH_iSpatial = iSpatial::infer(NA_merFISH, NA_scRNA)
#> Stablize spatial transcriptome.
#> 1st level integration
#> Merging dataset 2 into 1
#> Extracting anchors for merged samples
#> Finding integration vectors
#> Finding integration vector weights
#> Integrating data
#> normalization
#> 2nd level integration
#> Computing nearest neighbors
#> Only one graph name supplied, storing nearest-neighbor graph only
#> infer expression.
Example of spatial expression
Gene "Acbd7" is not in the merFISH targeted genes
"Acbd7" %in% rownames(NA_merFISH)
#> [1] FALSE
After inferring, we can get the spatial expression of Acbd7
SpatialFeaturePlot(NA_merFISH_iSpatial, features = "Acbd7", stroke=0.1, slot="data") +
theme(legend.position = "right") +
scale_fill_gradientn(colours = c(rainbow(10)[5:10], "red"))
#> Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Enhance the spatial expression of Tac2
#raw expression
p1 = SpatialFeaturePlot(NA_merFISH, features = "Tac2", stroke=0.1, slot="data") +
theme(legend.position = "right") + ggtitle("before") +
scale_fill_gradientn(colours = c(rainbow(10)[5:10], "red"))
#> Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
# inferred
p2 = SpatialFeaturePlot(NA_merFISH_iSpatial, features = "Tac2", stroke=0.1, slot="data") +
theme(legend.position = "right") + ggtitle("after") +
scale_fill_gradientn(colours = c(rainbow(10)[5:10], "red"))
#> Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
p1 + p2
Find spatial variable genes
# run umap of scRNAseq data
NA_scRNA = FindVariableFeatures(NA_scRNA, verbose = FALSE)
NA_scRNA = ScaleData(NA_scRNA, verbose = FALSE)
NA_scRNA = RunPCA(NA_scRNA, verbose = FALSE)
NA_scRNA = RunUMAP(NA_scRNA, dims = 1:30, verbose = FALSE)
# Find spatial variable genes
SVGs_infer = iSpatial::spatial_variable_genes(spRNA.obj = NA_merFISH_iSpatial,
spRNA.assay = "enhanced",
scRNA.obj = NA_scRNA,
scRNA.assay = "RNA",
n.core = 10)
SVGs_infer_sig = SVGs_infer[SVGs_infer$p.value < .01 & SVGs_infer$spRNA.p.value < .01, ]
ggplot2::ggplot(SVGs_infer, aes(-log10(scRNA.p.value), -log10(spRNA.p.value),
gene = gene, color = -log10(p.value))) +
geom_point()+
theme_classic()
Group the SVGs into clusters based on spatial expression pattern
gene_cluster = iSpatial::cluster_spatial_expr_pattern(NA_merFISH_iSpatial, "enhanced",
feature = SVGs_infer_sig$gene,
n.cluster = 12,
ncol.plot = 4,
bin = 20,
n.core = 10)
#> Number of genes in each cluster:
#> SVG_cluster
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> 48 177 189 28 37 17 41 62 40 41 64 23
# genes in cluster 7
names(gene_cluster[gene_cluster==7])
#> [1] "2010001M06Rik" "2310075C17Rik" "8030453O22Rik" "Abi3bp" "Actn2" "Acvr1c" "Adcy1" "Arg2"
#> [9] "BB031773" "Cacng1" "Ccbp2" "Cited4" "Clec12a" "Cnr1" "Coch" "Col19a1"
#> [17] "Cyp2a5" "Dgat2l6" "E230025N22Rik" "E530001K10Rik" "Epor" "Fabp4" "Fgf3" "Fibcd1"
#> [25] "Galnt14" "Gbx1" "Gm16075" "Gpx6" "Igfbp6" "Il20ra" "Mas1" "Mme"
#> [33] "Onecut2" "Rab3b" "Reln" "Slc16a3" "Snai3" "Sprr1a" "Syt2" "Tmprss6"
#> [41] "Wt1"
# plot
SpatialFeaturePlot(NA_merFISH_iSpatial, features = "Coch", stroke=0.1, slot="data") +
theme(legend.position = "right") +
scale_fill_gradientn(colours = c(rainbow(10)[5:10], "red"))
#> Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
sessionInfo()
#> R version 4.0.5 (2021-03-31)
#> Platform: x86_64-conda-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.3 LTS
#>
#> Matrix products: default
#> BLAS/LAPACK: /netscr/chaozhang/miniconda3/lib/libopenblasp-r0.3.18.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
#> [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] webshot_0.5.2 plotly_4.10.0 rmarkdown_2.11 knitr_1.36 ggplot2_3.3.5 SeuratObject_4.0.2 Seurat_4.0.5
#> [8] iSpatial_1.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] plyr_1.8.6 igraph_1.2.7 lazyeval_0.2.2 splines_4.0.5 crosstalk_1.2.0 listenv_0.8.0
#> [7] scattermore_0.7 usethis_2.1.3 digest_0.6.28 foreach_1.5.1 htmltools_0.5.2 fansi_0.5.0
#> [13] magrittr_2.0.1 memoise_2.0.0 tensor_1.5 cluster_2.1.2 doParallel_1.0.16 ROCR_1.0-11
#> [19] remotes_2.4.1 globals_0.14.0 matrixStats_0.61.0 spatstat.sparse_2.0-0 prettyunits_1.1.1 colorspace_2.0-2
#> [25] ggrepel_0.9.1 xfun_0.28 dplyr_1.0.7 callr_3.7.0 crayon_1.4.2 jsonlite_1.7.2
#> [31] spatstat.data_2.1-0 survival_3.2-13 zoo_1.8-9 iterators_1.0.13 glue_1.4.2 polyclip_1.10-0
#> [37] gtable_0.3.0 leiden_0.3.9 pkgbuild_1.2.0 future.apply_1.8.1 abind_1.4-5 scales_1.1.1
#> [43] pheatmap_1.0.12 DBI_1.1.1 miniUI_0.1.1.1 Rcpp_1.0.7 viridisLite_0.4.0 xtable_1.8-4
#> [49] riverplot_0.10 reticulate_1.22 spatstat.core_2.3-1 bit_4.0.4 mclust_5.4.9 htmlwidgets_1.5.4
#> [55] httr_1.4.2 FNN_1.1.3 RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2 farver_2.1.0
#> [61] pkgconfig_2.0.3 uwot_0.1.10 deldir_1.0-6 utf8_1.2.2 rliger_1.0.0 labeling_0.4.2
#> [67] tidyselect_1.1.1 rlang_0.4.12 reshape2_1.4.4 later_1.2.0 munsell_0.5.0 tools_4.0.5
#> [73] cachem_1.0.6 cli_3.1.0 generics_0.1.1 devtools_2.4.2 ggridges_0.5.3 evaluate_0.14
#> [79] stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1 goftest_1.2-3 processx_3.5.2 bit64_4.0.5
#> [85] fs_1.5.0 fitdistrplus_1.1-6 purrr_0.3.4 RANN_2.6.1 pbapply_1.5-0 future_1.23.0
#> [91] nlme_3.1-153 mime_0.12 hdf5r_1.3.3 compiler_4.0.5 rstudioapi_0.13 png_0.1-7
#> [97] testthat_3.1.0 spatstat.utils_2.2-0 tibble_3.1.5 stringi_1.7.5 highr_0.9 ps_1.6.0
#> [103] RSpectra_0.16-0 desc_1.4.0 lattice_0.20-45 Matrix_1.3-4 markdown_1.1 SeuratDisk_0.0.0.9019
#> [109] ggsci_2.9 vctrs_0.3.8 pillar_1.6.4 lifecycle_1.0.1 spatstat.geom_2.3-0 lmtest_0.9-38
#> [115] RcppAnnoy_0.0.19 data.table_1.14.2 cowplot_1.1.1 irlba_2.3.3 httpuv_1.6.3 patchwork_1.1.1
#> [121] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.28.1 sessioninfo_1.2.1
#> [127] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1 pkgload_1.2.3 rprojroot_2.0.2 withr_2.4.2
#> [133] sctransform_0.3.2 harmony_0.1.0 mgcv_1.8-38 parallel_4.0.5 grid_4.0.5 rpart_4.1-15
#> [139] tidyr_1.1.4 Rtsne_0.15 pROC_1.18.0 shiny_1.7.1
Czh3/iSpatial documentation built on Sept. 15, 2022, 10:32 p.m.
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title: "iSpatial Tutorial" output: pdf_document: default html_document: default vignette: | %\VignetteIndexEntry{introduction} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
iSpatail Tutorial
Load package
Install iSpatial package from Github. iSpatial is based on Seurat, you need install Seurat first .
# install iSpatial package
# devtools::install_github("Czh3/iSpatial")
# load
library(iSpatial)
library(Seurat)
library(ggplot2)
Load data
Load scRNA-seq and merFISH of Mouse Nucleus Accumbens. The data are down-sampled from Renchao et al.
# scRNA-seq data
data(NA_scRNA)
# merFISH data
data(NA_merFISH)
Normalize data
The data need be log normalized.
# scRNA-seq data
NA_scRNA = NormalizeData(NA_scRNA, verbose = FALSE)
# merFISH data
NA_merFISH = NormalizeData(NA_merFISH, verbose = FALSE)
infer transcriptome-wide spatial expression
# infer spatial transcriptome
NA_merFISH_iSpatial = iSpatial::infer(NA_merFISH, NA_scRNA)
#> Stablize spatial transcriptome.
#> 1st level integration
#> Merging dataset 2 into 1
#> Extracting anchors for merged samples
#> Finding integration vectors
#> Finding integration vector weights
#> Integrating data
#> normalization
#> 2nd level integration
#> Computing nearest neighbors
#> Only one graph name supplied, storing nearest-neighbor graph only
#> infer expression.
Example of spatial expression
Gene "Acbd7" is not in the merFISH targeted genes
"Acbd7" %in% rownames(NA_merFISH)
#> [1] FALSE
After inferring, we can get the spatial expression of Acbd7
SpatialFeaturePlot(NA_merFISH_iSpatial, features = "Acbd7", stroke=0.1, slot="data") +
theme(legend.position = "right") +
scale_fill_gradientn(colours = c(rainbow(10)[5:10], "red"))
#> Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Enhance the spatial expression of Tac2
#raw expression
p1 = SpatialFeaturePlot(NA_merFISH, features = "Tac2", stroke=0.1, slot="data") +
theme(legend.position = "right") + ggtitle("before") +
scale_fill_gradientn(colours = c(rainbow(10)[5:10], "red"))
#> Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
# inferred
p2 = SpatialFeaturePlot(NA_merFISH_iSpatial, features = "Tac2", stroke=0.1, slot="data") +
theme(legend.position = "right") + ggtitle("after") +
scale_fill_gradientn(colours = c(rainbow(10)[5:10], "red"))
#> Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
p1 + p2
Find spatial variable genes
# run umap of scRNAseq data
NA_scRNA = FindVariableFeatures(NA_scRNA, verbose = FALSE)
NA_scRNA = ScaleData(NA_scRNA, verbose = FALSE)
NA_scRNA = RunPCA(NA_scRNA, verbose = FALSE)
NA_scRNA = RunUMAP(NA_scRNA, dims = 1:30, verbose = FALSE)
# Find spatial variable genes
SVGs_infer = iSpatial::spatial_variable_genes(spRNA.obj = NA_merFISH_iSpatial,
spRNA.assay = "enhanced",
scRNA.obj = NA_scRNA,
scRNA.assay = "RNA",
n.core = 10)
SVGs_infer_sig = SVGs_infer[SVGs_infer$p.value < .01 & SVGs_infer$spRNA.p.value < .01, ]
ggplot2::ggplot(SVGs_infer, aes(-log10(scRNA.p.value), -log10(spRNA.p.value),
gene = gene, color = -log10(p.value))) +
geom_point()+
theme_classic()
Group the SVGs into clusters based on spatial expression pattern
gene_cluster = iSpatial::cluster_spatial_expr_pattern(NA_merFISH_iSpatial, "enhanced",
feature = SVGs_infer_sig$gene,
n.cluster = 12,
ncol.plot = 4,
bin = 20,
n.core = 10)
#> Number of genes in each cluster:
#> SVG_cluster
#> 1 2 3 4 5 6 7 8 9 10 11 12
#> 48 177 189 28 37 17 41 62 40 41 64 23
# genes in cluster 7
names(gene_cluster[gene_cluster==7])
#> [1] "2010001M06Rik" "2310075C17Rik" "8030453O22Rik" "Abi3bp" "Actn2" "Acvr1c" "Adcy1" "Arg2"
#> [9] "BB031773" "Cacng1" "Ccbp2" "Cited4" "Clec12a" "Cnr1" "Coch" "Col19a1"
#> [17] "Cyp2a5" "Dgat2l6" "E230025N22Rik" "E530001K10Rik" "Epor" "Fabp4" "Fgf3" "Fibcd1"
#> [25] "Galnt14" "Gbx1" "Gm16075" "Gpx6" "Igfbp6" "Il20ra" "Mas1" "Mme"
#> [33] "Onecut2" "Rab3b" "Reln" "Slc16a3" "Snai3" "Sprr1a" "Syt2" "Tmprss6"
#> [41] "Wt1"
# plot
SpatialFeaturePlot(NA_merFISH_iSpatial, features = "Coch", stroke=0.1, slot="data") +
theme(legend.position = "right") +
scale_fill_gradientn(colours = c(rainbow(10)[5:10], "red"))
#> Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
sessionInfo()
#> R version 4.0.5 (2021-03-31)
#> Platform: x86_64-conda-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.3 LTS
#>
#> Matrix products: default
#> BLAS/LAPACK: /netscr/chaozhang/miniconda3/lib/libopenblasp-r0.3.18.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
#> [6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] webshot_0.5.2 plotly_4.10.0 rmarkdown_2.11 knitr_1.36 ggplot2_3.3.5 SeuratObject_4.0.2 Seurat_4.0.5
#> [8] iSpatial_1.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] plyr_1.8.6 igraph_1.2.7 lazyeval_0.2.2 splines_4.0.5 crosstalk_1.2.0 listenv_0.8.0
#> [7] scattermore_0.7 usethis_2.1.3 digest_0.6.28 foreach_1.5.1 htmltools_0.5.2 fansi_0.5.0
#> [13] magrittr_2.0.1 memoise_2.0.0 tensor_1.5 cluster_2.1.2 doParallel_1.0.16 ROCR_1.0-11
#> [19] remotes_2.4.1 globals_0.14.0 matrixStats_0.61.0 spatstat.sparse_2.0-0 prettyunits_1.1.1 colorspace_2.0-2
#> [25] ggrepel_0.9.1 xfun_0.28 dplyr_1.0.7 callr_3.7.0 crayon_1.4.2 jsonlite_1.7.2
#> [31] spatstat.data_2.1-0 survival_3.2-13 zoo_1.8-9 iterators_1.0.13 glue_1.4.2 polyclip_1.10-0
#> [37] gtable_0.3.0 leiden_0.3.9 pkgbuild_1.2.0 future.apply_1.8.1 abind_1.4-5 scales_1.1.1
#> [43] pheatmap_1.0.12 DBI_1.1.1 miniUI_0.1.1.1 Rcpp_1.0.7 viridisLite_0.4.0 xtable_1.8-4
#> [49] riverplot_0.10 reticulate_1.22 spatstat.core_2.3-1 bit_4.0.4 mclust_5.4.9 htmlwidgets_1.5.4
#> [55] httr_1.4.2 FNN_1.1.3 RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2 farver_2.1.0
#> [61] pkgconfig_2.0.3 uwot_0.1.10 deldir_1.0-6 utf8_1.2.2 rliger_1.0.0 labeling_0.4.2
#> [67] tidyselect_1.1.1 rlang_0.4.12 reshape2_1.4.4 later_1.2.0 munsell_0.5.0 tools_4.0.5
#> [73] cachem_1.0.6 cli_3.1.0 generics_0.1.1 devtools_2.4.2 ggridges_0.5.3 evaluate_0.14
#> [79] stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1 goftest_1.2-3 processx_3.5.2 bit64_4.0.5
#> [85] fs_1.5.0 fitdistrplus_1.1-6 purrr_0.3.4 RANN_2.6.1 pbapply_1.5-0 future_1.23.0
#> [91] nlme_3.1-153 mime_0.12 hdf5r_1.3.3 compiler_4.0.5 rstudioapi_0.13 png_0.1-7
#> [97] testthat_3.1.0 spatstat.utils_2.2-0 tibble_3.1.5 stringi_1.7.5 highr_0.9 ps_1.6.0
#> [103] RSpectra_0.16-0 desc_1.4.0 lattice_0.20-45 Matrix_1.3-4 markdown_1.1 SeuratDisk_0.0.0.9019
#> [109] ggsci_2.9 vctrs_0.3.8 pillar_1.6.4 lifecycle_1.0.1 spatstat.geom_2.3-0 lmtest_0.9-38
#> [115] RcppAnnoy_0.0.19 data.table_1.14.2 cowplot_1.1.1 irlba_2.3.3 httpuv_1.6.3 patchwork_1.1.1
#> [121] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-20 gridExtra_2.3 parallelly_1.28.1 sessioninfo_1.2.1
#> [127] codetools_0.2-18 MASS_7.3-54 assertthat_0.2.1 pkgload_1.2.3 rprojroot_2.0.2 withr_2.4.2
#> [133] sctransform_0.3.2 harmony_0.1.0 mgcv_1.8-38 parallel_4.0.5 grid_4.0.5 rpart_4.1-15
#> [139] tidyr_1.1.4 Rtsne_0.15 pROC_1.18.0 shiny_1.7.1
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