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Spots merging |
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%\VignetteIndexEntry{Spots merging} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
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This document demonstrates how to group and merge visium spots with low coverage using visutils. The spots are grouped into coarser mesh by seven spots preserving hexagonal layout. All spots from single groups with coverage below certain threshold are merged, their counts are summed and one spot is used as the representative: either the central one (if its coverage below the threshold) or the spot with highest coverage.
# devtools::install_github("cellgeni/visutils",force = TRUE)
library(visutils)
library(Seurat)Lets take one random skin sample from https://spatial-skin-atlas.cellgeni.sanger.ac.uk/.
sid = 'WSSKNKCLsp12887263'
tmpfile = tempfile()
download.file(paste0('https://cellgeni.cog.sanger.ac.uk/spatial-skin-atlas/download/',sid,'.h5ad'),tmpfile,quiet = TRUE)
vis = schard::h5ad2seurat_spatial(tmpfile,use.raw = TRUE,img.res = 'hires')One can use Seurat visualization functions
SpatialFeaturePlot(vis,'nCount_Spatial')
but here we will use visutils functions which offers more flexibility
par(mfrow=c(1,2),mar=c(0,0,1,5),bty='n')
plotVisium(vis,vis$nCount_Spatial,zfun=log1p)
plotVisium(vis,vis$nCount_Spatial<1000,legend.args = list(title='<1000 UMI'))
so there are quite a few spots with coverage below 1000. We will merge them instead of filtering them out.
everything can be done just in one line
umi.thr = 1000
vism = mergeSpots(vis,getCenters(vis,to.merge = vis$nCount_Spatial<umi.thr))
# lets compare merged object mobm0 with the original oneLets compare coverage in original and merged objects. The spots size it proportional to sqrt of number of spots merged.
par(mfrow=c(2,2),mar=c(0,0,1,5),bty='n')
plotVisium(vis,vis$nCount_Spatial,zfun = sqrt,main='Original')
plotVisium(vism,vism$nCount_Spatial,cex = vism$cex,zfun=sqrt,main='Merged')
plotVisium(vis,vis$nCount_Spatial < umi.thr,main='Original',legend.args = list(title='<1000 UMI'))
plotVisium(vism,vism$nCount_Spatial < umi.thr,cex=vism$cex,main='Merged',legend.args = list(title='<1000 UMI')) 
Most of spots in merged object now have coverage above threshold, so we will lose much less data on filtering
hist(vis$nCount_Spatial,500,xlim=c(0,5000))
table(vis$nCount_Spatial<umi.thr)
#>
#> FALSE TRUE
#> 1337 760The threshold can be set to 500, but lets keep at 1000 for demonstration propose.
We first define groups of spots to be merged by grouping these of them who have coverage lower than threshold into groups of 7 by spatial proximity.
groups = getCenters(vis,to.merge = vis$nCount_Spatial<umi.thr)
groups[1:5,]
#> tissue row col imagerow imagecol group group.row group.col
#> AAACACCAATAACTGC-1 1 59 19 1239 989 59_19 59 19
#> AAACAGCTTTCAGAAG-1 1 43 9 1967 727 42_10 42 10
#> AAACAGGGTCTATATT-1 1 47 13 1785 832 48_12 48 12
#> AAACATGGTGAGAGGA-1 1 62 0 1102 492 62_0 62 0
#> AAACCGGGTAGGTACC-1 1 42 28 2012 1224 43_29 43 29Lets check the groups visually
par(mfrow=c(1,2),mar=c(1,1,1,1),bty='n')
plotVisium(vis,groups$group,plot.legend = F)
# change orientation of y axis to match H7E orientation (by default y-axis is reversed)
plotVisium(vis,groups$group,plot.legend = F,type='hex',ylim = range(vis@images$WSSKNKCLsp12887263@coordinates$row))
groups of adjacent spots with same color are the ones to be merged. Spots in regions with higher coverage
Now we have groups, lets use them to merge spots
vism = mergeSpots(vis,groups)
vism@meta.data[1:5,]
#> orig.ident nCount_Spatial nFeature_Spatial barcode in_tissue array_row array_col n_genes_by_counts
#> GACCTGGTCTGGGCGT-1 SeuratProject 32510 6971 GACCTGGTCTGGGCGT-1 1 10 0 6971
#> CCAAAGCAGTTGGTTG-1 SeuratProject 5144 2381 CCAAAGCAGTTGGTTG-1 1 10 10 2381
#> CGTACCGAAAGTCTAG-1 SeuratProject 6455 2834 CGTACCGAAAGTCTAG-1 1 10 12 2834
#> TGGATAGAGTAACAGA-1 SeuratProject 6534 2885 TGGATAGAGTAACAGA-1 1 10 14 2885
#> total_counts total_counts_mt pct_counts_mt total_counts_rb pct_counts_rb barcode.1 c2l_Th c2l_NK
#> GACCTGGTCTGGGCGT-1 32510 1347 4.143341 5681 17.47462 GACCTGGTCTGGGCGT-1 0.09709959 0.06333847
#> CCAAAGCAGTTGGTTG-1 5144 227 4.412908 823 15.99922 CCAAAGCAGTTGGTTG-1 0.02819351 0.12340139
#> CGTACCGAAAGTCTAG-1 6455 255 3.950426 1093 16.93261 CGTACCGAAAGTCTAG-1 0.03691382 0.15954026
#> TGGATAGAGTAACAGA-1 6534 252 3.856750 1097 16.78910 TGGATAGAGTAACAGA-1 0.01700469 0.15689340
#> c2l_APOD+ fibroblasts c2l_CD8+ T RM c2l_T reg c2l_Macro1_2 c2l_DC1 c2l_SFRP2+ fibroblasts c2l_TAGLN+ pericytes
#> GACCTGGTCTGGGCGT-1 0.02744421 0.09471750 0.1379724 0.007156359 0.16742659 0.9079282 0.01782978
#> CCAAAGCAGTTGGTTG-1 0.03865423 0.05884737 0.1420750 0.088371895 0.10303004 2.4569583 0.04073392
#> CGTACCGAAAGTCTAG-1 0.01854635 0.10915646 0.2190897 0.096210102 0.01634783 1.4929467 0.01015466
#> TGGATAGAGTAACAGA-1 0.08937889 0.06938209 0.1908971 0.133929323 0.04581218 1.2835222 0.04463618
#> c2l_POSTN+ fibroblasts c2l_RGS5+ pericytes c2l_VEC c2l_Tc c2l_ILC_NK c2l_BC c2l_Monocytes c2l_MastC
#> GACCTGGTCTGGGCGT-1 0.08366692 0.01735309 0.04500113 0.6560913 0.02607997 0.06697251 0.118522971 0.02552956
#> CCAAAGCAGTTGGTTG-1 0.15097042 0.07027518 0.19094650 0.1053635 0.02593999 0.24641302 0.013501134 0.16374208
#> CGTACCGAAAGTCTAG-1 0.90180142 0.07687006 0.04404044 0.1275342 0.03456035 0.27354640 0.006294652 0.28011379
#> TGGATAGAGTAACAGA-1 0.26371153 0.14347357 0.44016573 0.1694106 0.01951851 0.30195144 0.006003635 0.09620604
#> c2l_Melanocytes c2l_DC2 c2l_LEC c2l_PlasmaC c2l_PTGDS+ fibroblasts c2l_MigDC c2l_Neuronal_SchwannC
#> GACCTGGTCTGGGCGT-1 0.8733758 0.07495213 0.002704027 0.0003887776 0.13166811 0.33293675 0.03202289
#> CCAAAGCAGTTGGTTG-1 0.1366515 0.03792663 0.116802314 0.0037468233 0.18061862 0.01437001 0.03153874
#> CGTACCGAAAGTCTAG-1 0.2779331 0.04742168 0.015545872 0.0008625661 0.07288710 0.03406875 0.02435081
#> TGGATAGAGTAACAGA-1 0.2777428 0.10709598 0.057556206 0.0007611657 0.03464078 0.18342535 0.04847370
#> c2l_SMC c2l_Skeletal muscle cells leiden c2l_Suprabasal keratinocytes c2l_Basal keratinocytes c2l_Chondrocytes
#> GACCTGGTCTGGGCGT-1 0.046804237 4.4858855 3 30.682146 5.7642651 0.083352360
#> CCAAAGCAGTTGGTTG-1 0.003544222 0.4107982 3 5.199662 0.4594907 0.021503296
#> CGTACCGAAAGTCTAG-1 0.001988462 0.1983928 3 5.404539 0.5138135 0.008831069
#> TGGATAGAGTAACAGA-1 0.004473562 0.5372182 3 6.869743 1.0410424 0.007542777
#> c2l_IL8+ DC1 library_id nspots merged_spots cex
#> GACCTGGTCTGGGCGT-1 0.06027652 WSSKNKCLsp12887263 1 GACCTGGTCTGGGCGT-1 0.9
#> CCAAAGCAGTTGGTTG-1 0.09316662 WSSKNKCLsp12887263 1 CCAAAGCAGTTGGTTG-1 0.9
#> CGTACCGAAAGTCTAG-1 0.06230878 WSSKNKCLsp12887263 1 CGTACCGAAAGTCTAG-1 0.9
#> TGGATAGAGTAACAGA-1 0.07489332 WSSKNKCLsp12887263 1 TGGATAGAGTAACAGA-1 0.9
#> [ reached 'max' / getOption("max.print") -- omitted 1 rows ]The resultant seurat object contains summed counts, metadata table provides information about number of spots merged (nspots) and their identity (merged_spots)
par(mar=c(1,1,1,5))
plotVisium(vism,vism$nCount_Spatial,zfun = log1p,cex=vism$cex)