From f54cc84cc4da58bcdc5d87cefdda1a069b728c45 Mon Sep 17 00:00:00 2001 From: YingxinLin Date: Tue, 17 Sep 2024 06:20:30 +1000 Subject: [PATCH] update vignettes --- DESCRIPTION | 1 + docs/articles/CellSPA.html | 447 +++++++++--------- .../figure-html/unnamed-chunk-22-2.png | Bin 261138 -> 259087 bytes vignettes/CellSPA.Rmd | 17 +- 4 files changed, 218 insertions(+), 247 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index cd33760..cded381 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -36,6 +36,7 @@ Imports: shotGroups, sp, sf, + spatstat, spatstat.geom, entropy, data.table, diff --git a/docs/articles/CellSPA.html b/docs/articles/CellSPA.html index fe4da9d..db49a38 100644 --- a/docs/articles/CellSPA.html +++ b/docs/articles/CellSPA.html @@ -70,52 +70,28 @@

Yingxin -

Read BIDCell output

-
+
 data_dir <- system.file("extdata/BIDCell_csv_output", package = "CellSPA")
 data_dir
-#> [1] "/dskh/nobackup/yingxinl/tmp/Rtmp9613Vz/temp_libpathfb143446f6d00/CellSPA/extdata/BIDCell_csv_output"
-tiff_path <- system.file("extdata/BIDCell_output_subset.tif", package = "CellSPA")
-spe <- readBIDCell(data_dir,
-                   tiff_path = tiff_path,
-                   method_name = "BIDCell")
-
+#> [1] "/dskh/nobackup/yingxinl/tmp/RtmpB40XFr/temp_libpath7f47d5296819c/CellSPA/extdata/BIDCell_csv_output"
+tiff_path <- system.file("extdata/BIDCell_output_subset.tif", package = "CellSPA")
+
 spe <- processingSPE(spe,
                      qc_range = list(total_transciprts = c(20, 2000),
                                      total_genes = c(20, Inf)))
-
-# subset a set of cells for illustration
-spe <- CellSPA::subset(spe, 1:500)
-spe
-#> class: SpatialExperiment 
-#> dim: 313 500 
-#> metadata(2): CellSPA CellSegOutput
-#> assays(2): counts logcounts
-#> rownames(313): SEC11C DAPK3 ... CD1C PDCD1
-#> rowData names(2): total_cells meanExprsPct_cells
-#> colnames(500): Cell_27210 Cell_27211 ... Cell_27715 Cell_27716
-#> colData names(12): cell_id pixel_size ... total_transciprts sizeFactor
-#> reducedDimNames(2): PCA UMAP
-#> mainExpName: NULL
-#> altExpNames(0):
-#> spatialCoords names(2) : cell_centroid_x cell_centroid_y
-#> imgData names(1): sample_id
+
+#subset a set of cells for illustration
+spe <- CellSPA::subset(spe, 1:500)

Baseline metrics

-
-spe <- generatePolygon(spe)
+
+
+spe <- generatePolygon(spe)
 #>   |                                                                              |                                                                      |   0%  |                                                                              |======================================================================| 100%
 spe <- calBaselineAllMetrics(spe, verbose = TRUE)
 #> [1] "Metrics to run:  total_transciprts, total_genes, total_cells, meanExprsPct_cells"
@@ -139,7 +115,7 @@ 

Baseline metrics#> #> [1] "Calculating circularity" #> | | | 0% | |======================================================================| 100% -head(rowData(spe)) +head(rowData(spe)) #> DataFrame with 6 rows and 2 columns #> total_cells meanExprsPct_cells #> <integer> <numeric> @@ -149,16 +125,16 @@

Baseline metrics#> NKG7 16 0.032 #> RAPGEF3 69 0.138 #> PPARG 93 0.186 -head(colData(spe)) +head(colData(spe)) #> DataFrame with 6 rows and 20 columns #> cell_id pixel_size eccentricity cell_type spearman #> <numeric> <numeric> <numeric> <numeric> <numeric> -#> Cell_27210 27210 92 0.727273 7 0.578500 -#> Cell_27211 27211 56 0.433962 6 0.408182 -#> Cell_27212 27212 181 0.928000 7 0.569310 -#> Cell_27213 27213 115 0.923077 7 0.474430 -#> Cell_27214 27214 122 0.336957 7 0.538882 -#> Cell_27215 27215 153 0.839744 7 0.546259 +#> Cell_27210 27210 92 0.808957 7 0.578500 +#> Cell_27211 27211 56 -Inf 6 0.408182 +#> Cell_27212 27212 181 0.904051 7 0.569310 +#> Cell_27213 27213 115 0.724503 7 0.474430 +#> Cell_27214 27214 122 0.280000 7 0.538882 +#> Cell_27215 27215 153 0.694516 7 0.546259 #> cell_type_atlas total_reads total_genes slide sample_id #> <numeric> <numeric> <integer> <integer> <character> #> Cell_27210 5 265 79 1 sample01 @@ -177,12 +153,12 @@

Baseline metrics#> Cell_27215 259 0.979502 153 0.839744 0.358405 #> sphericity solidity convexity circularity density #> <numeric> <numeric> <numeric> <numeric> <numeric> -#> Cell_27210 0.674331 0.949367 0.971781 0.839202 2.88043 -#> Cell_27211 0.380391 0.847619 0.970686 0.579024 1.32143 -#> Cell_27212 0.626272 0.883905 0.950348 0.793084 2.22099 -#> Cell_27213 0.561809 0.840164 0.961928 0.686686 2.51304 -#> Cell_27214 0.276051 0.731293 0.907927 0.412575 1.40984 -#> Cell_27215 0.469189 0.615385 0.801868 0.557402 1.69281

+#> Cell_27210 0.583612 0.949367 0.971781 0.839202 2.88043 +#> Cell_27211 0.499965 0.847619 0.970686 0.579024 1.32143 +#> Cell_27212 0.680052 0.883905 0.950348 0.793084 2.22099 +#> Cell_27213 0.619852 0.840164 0.961928 0.686686 2.51304 +#> Cell_27214 0.336094 0.731293 0.907927 0.412575 1.40984 +#> Cell_27215 0.659214 0.615385 0.801868 0.557402 1.69281

Expression similarity @@ -190,7 +166,7 @@

Expression similarity

Processing reference

-
+
 sce_ref_full <- readRDS(system.file("extdata/sce_FFPE_full.rds", 
                             package = "CellSPA"))
 sce_ref <- processingRef(sce_ref_full, 
@@ -212,23 +188,23 @@ 

Processing reference

Calculating expression similarity

-
+
 spe <- calExpressionCorrelation(spe, 
                                 sce_ref,
                                 ref_celltype = sce_ref$celltype,
                                 method = c("pearson", "cosine"),
                                 spe_exprs_values = "logcounts",
                                 ref_exprs_values = "mean")
-head(colData(spe))
+head(colData(spe))
 #> DataFrame with 6 rows and 24 columns
 #>              cell_id pixel_size eccentricity cell_type  spearman
 #>            <numeric>  <numeric>    <numeric> <numeric> <numeric>
-#> Cell_27210     27210         92     0.727273         7  0.578500
-#> Cell_27211     27211         56     0.433962         6  0.408182
-#> Cell_27212     27212        181     0.928000         7  0.569310
-#> Cell_27213     27213        115     0.923077         7  0.474430
-#> Cell_27214     27214        122     0.336957         7  0.538882
-#> Cell_27215     27215        153     0.839744         7  0.546259
+#> Cell_27210     27210         92     0.808957         7  0.578500
+#> Cell_27211     27211         56         -Inf         6  0.408182
+#> Cell_27212     27212        181     0.904051         7  0.569310
+#> Cell_27213     27213        115     0.724503         7  0.474430
+#> Cell_27214     27214        122     0.280000         7  0.538882
+#> Cell_27215     27215        153     0.694516         7  0.546259
 #>            cell_type_atlas total_reads total_genes     slide   sample_id
 #>                  <numeric>   <numeric>   <integer> <integer> <character>
 #> Cell_27210               5         265          79         1    sample01
@@ -247,12 +223,12 @@ 

Calculating expression similarity#> Cell_27215 259 0.979502 153 0.839744 0.358405 #> sphericity solidity convexity circularity density #> <numeric> <numeric> <numeric> <numeric> <numeric> -#> Cell_27210 0.674331 0.949367 0.971781 0.839202 2.88043 -#> Cell_27211 0.380391 0.847619 0.970686 0.579024 1.32143 -#> Cell_27212 0.626272 0.883905 0.950348 0.793084 2.22099 -#> Cell_27213 0.561809 0.840164 0.961928 0.686686 2.51304 -#> Cell_27214 0.276051 0.731293 0.907927 0.412575 1.40984 -#> Cell_27215 0.469189 0.615385 0.801868 0.557402 1.69281 +#> Cell_27210 0.583612 0.949367 0.971781 0.839202 2.88043 +#> Cell_27211 0.499965 0.847619 0.970686 0.579024 1.32143 +#> Cell_27212 0.680052 0.883905 0.950348 0.793084 2.22099 +#> Cell_27213 0.619852 0.840164 0.961928 0.686686 2.51304 +#> Cell_27214 0.336094 0.731293 0.907927 0.412575 1.40984 +#> Cell_27215 0.659214 0.615385 0.801868 0.557402 1.69281 #> mean_cor_correlation mean_celltype_correlation mean_cor_cosine #> <numeric> <factor> <numeric> #> Cell_27210 0.692426 CRABP2+ Malignant 0.753021 @@ -269,23 +245,23 @@

Calculating expression similarity#> Cell_27213 KRT15+ Myoepi #> Cell_27214 CRABP2+ Malignant #> Cell_27215 ECM1+ Malignant

-
+
 spe <- calExpressionCorrelation(spe, 
                                 sce_ref,
                                 ref_celltype = sce_ref$celltype,
                                 method = c("pearson", "cosine"),
                                 spe_exprs_values = "logcounts",
                                 ref_exprs_values = "prop_detected")
-head(colData(spe))
+head(colData(spe))
 #> DataFrame with 6 rows and 28 columns
 #>              cell_id pixel_size eccentricity cell_type  spearman
 #>            <numeric>  <numeric>    <numeric> <numeric> <numeric>
-#> Cell_27210     27210         92     0.727273         7  0.578500
-#> Cell_27211     27211         56     0.433962         6  0.408182
-#> Cell_27212     27212        181     0.928000         7  0.569310
-#> Cell_27213     27213        115     0.923077         7  0.474430
-#> Cell_27214     27214        122     0.336957         7  0.538882
-#> Cell_27215     27215        153     0.839744         7  0.546259
+#> Cell_27210     27210         92     0.808957         7  0.578500
+#> Cell_27211     27211         56         -Inf         6  0.408182
+#> Cell_27212     27212        181     0.904051         7  0.569310
+#> Cell_27213     27213        115     0.724503         7  0.474430
+#> Cell_27214     27214        122     0.280000         7  0.538882
+#> Cell_27215     27215        153     0.694516         7  0.546259
 #>            cell_type_atlas total_reads total_genes     slide   sample_id
 #>                  <numeric>   <numeric>   <integer> <integer> <character>
 #> Cell_27210               5         265          79         1    sample01
@@ -304,12 +280,12 @@ 

Calculating expression similarity#> Cell_27215 259 0.979502 153 0.839744 0.358405 #> sphericity solidity convexity circularity density #> <numeric> <numeric> <numeric> <numeric> <numeric> -#> Cell_27210 0.674331 0.949367 0.971781 0.839202 2.88043 -#> Cell_27211 0.380391 0.847619 0.970686 0.579024 1.32143 -#> Cell_27212 0.626272 0.883905 0.950348 0.793084 2.22099 -#> Cell_27213 0.561809 0.840164 0.961928 0.686686 2.51304 -#> Cell_27214 0.276051 0.731293 0.907927 0.412575 1.40984 -#> Cell_27215 0.469189 0.615385 0.801868 0.557402 1.69281 +#> Cell_27210 0.583612 0.949367 0.971781 0.839202 2.88043 +#> Cell_27211 0.499965 0.847619 0.970686 0.579024 1.32143 +#> Cell_27212 0.680052 0.883905 0.950348 0.793084 2.22099 +#> Cell_27213 0.619852 0.840164 0.961928 0.686686 2.51304 +#> Cell_27214 0.336094 0.731293 0.907927 0.412575 1.40984 +#> Cell_27215 0.659214 0.615385 0.801868 0.557402 1.69281 #> mean_cor_correlation mean_celltype_correlation mean_cor_cosine #> <numeric> <factor> <numeric> #> Cell_27210 0.692426 CRABP2+ Malignant 0.753021 @@ -342,7 +318,7 @@

Calculating expression similarity#> Cell_27213 KRT15+ Myoepi #> Cell_27214 CRABP2+ Malignant #> Cell_27215 ECM1+ Malignant

-
+
 spe <- calAggExpressionCorrelation(spe,
                                    celltype = "mean_celltype_correlation",
                                    sce_ref = sce_ref,
@@ -361,7 +337,7 @@ 

Calculating expression similarity#> 0.7726590 0.0000000 0.5861591 0.5302542 #> KRT15+ Myoepi #> 0.6805255

-
+
 spe <- calAggExpressionCorrelation(spe,
                                    celltype = "mean_celltype_correlation",
                                    sce_ref = sce_ref,
@@ -384,7 +360,7 @@ 

Calculating expression similarity

Calculate marker F1 purity

-
+
 # Generate a positive marker list based on the reference data
 positive_marker_list <- generateMarkerList(sce_ref, type = "positive")
 #> [1] "For the top 10% of genes, the overlap freq between cell types"
@@ -648,21 +624,21 @@ 

Calculate marker F1 purity#> [9] "TRAC" "IL2RG" "CCR7" "EGFL7" "PTPRC" "TYROBP" "VOPP1" "ITM2C" #> [17] "SELL" "TCF4" "CD69" "PRDM1" "POSTN" "CD4" "CYTIP" "PTGDS" #> [25] "SMAP2" "FGL2" "CPA3" "LYZ" "CD163" "IL7R" "PECAM1"

-
+
 spe <- calMarkerPurity(spe,
                        celltype = "mean_celltype_correlation",
                        marker_list = positive_marker_list,
                        marker_list_name = "positive")
-head(colData(spe))
+head(colData(spe))
 #> DataFrame with 6 rows and 31 columns
 #>              cell_id pixel_size eccentricity cell_type  spearman
 #>            <numeric>  <numeric>    <numeric> <numeric> <numeric>
-#> Cell_27210     27210         92     0.727273         7  0.578500
-#> Cell_27211     27211         56     0.433962         6  0.408182
-#> Cell_27212     27212        181     0.928000         7  0.569310
-#> Cell_27213     27213        115     0.923077         7  0.474430
-#> Cell_27214     27214        122     0.336957         7  0.538882
-#> Cell_27215     27215        153     0.839744         7  0.546259
+#> Cell_27210     27210         92     0.808957         7  0.578500
+#> Cell_27211     27211         56         -Inf         6  0.408182
+#> Cell_27212     27212        181     0.904051         7  0.569310
+#> Cell_27213     27213        115     0.724503         7  0.474430
+#> Cell_27214     27214        122     0.280000         7  0.538882
+#> Cell_27215     27215        153     0.694516         7  0.546259
 #>            cell_type_atlas total_reads total_genes     slide   sample_id
 #>                  <numeric>   <numeric>   <integer> <integer> <character>
 #> Cell_27210               5         265          79         1    sample01
@@ -681,12 +657,12 @@ 

Calculate marker F1 purity#> Cell_27215 259 0.979502 153 0.839744 0.358405 #> sphericity solidity convexity circularity density #> <numeric> <numeric> <numeric> <numeric> <numeric> -#> Cell_27210 0.674331 0.949367 0.971781 0.839202 2.88043 -#> Cell_27211 0.380391 0.847619 0.970686 0.579024 1.32143 -#> Cell_27212 0.626272 0.883905 0.950348 0.793084 2.22099 -#> Cell_27213 0.561809 0.840164 0.961928 0.686686 2.51304 -#> Cell_27214 0.276051 0.731293 0.907927 0.412575 1.40984 -#> Cell_27215 0.469189 0.615385 0.801868 0.557402 1.69281 +#> Cell_27210 0.583612 0.949367 0.971781 0.839202 2.88043 +#> Cell_27211 0.499965 0.847619 0.970686 0.579024 1.32143 +#> Cell_27212 0.680052 0.883905 0.950348 0.793084 2.22099 +#> Cell_27213 0.619852 0.840164 0.961928 0.686686 2.51304 +#> Cell_27214 0.336094 0.731293 0.907927 0.412575 1.40984 +#> Cell_27215 0.659214 0.615385 0.801868 0.557402 1.69281 #> mean_cor_correlation mean_celltype_correlation mean_cor_cosine #> <numeric> <factor> <numeric> #> Cell_27210 0.692426 CRABP2+ Malignant 0.753021 @@ -727,21 +703,21 @@

Calculate marker F1 purity#> Cell_27213 0.181818 #> Cell_27214 0.428571 #> Cell_27215 0.454545

-
+
 spe <- calMarkerPurity(spe,
                        celltype = "mean_celltype_correlation",
                        marker_list = negative_marker_list,
                        marker_list_name = "negative")
-head(colData(spe))
+head(colData(spe))
 #> DataFrame with 6 rows and 34 columns
 #>              cell_id pixel_size eccentricity cell_type  spearman
 #>            <numeric>  <numeric>    <numeric> <numeric> <numeric>
-#> Cell_27210     27210         92     0.727273         7  0.578500
-#> Cell_27211     27211         56     0.433962         6  0.408182
-#> Cell_27212     27212        181     0.928000         7  0.569310
-#> Cell_27213     27213        115     0.923077         7  0.474430
-#> Cell_27214     27214        122     0.336957         7  0.538882
-#> Cell_27215     27215        153     0.839744         7  0.546259
+#> Cell_27210     27210         92     0.808957         7  0.578500
+#> Cell_27211     27211         56         -Inf         6  0.408182
+#> Cell_27212     27212        181     0.904051         7  0.569310
+#> Cell_27213     27213        115     0.724503         7  0.474430
+#> Cell_27214     27214        122     0.280000         7  0.538882
+#> Cell_27215     27215        153     0.694516         7  0.546259
 #>            cell_type_atlas total_reads total_genes     slide   sample_id
 #>                  <numeric>   <numeric>   <integer> <integer> <character>
 #> Cell_27210               5         265          79         1    sample01
@@ -760,12 +736,12 @@ 

Calculate marker F1 purity#> Cell_27215 259 0.979502 153 0.839744 0.358405 #> sphericity solidity convexity circularity density #> <numeric> <numeric> <numeric> <numeric> <numeric> -#> Cell_27210 0.674331 0.949367 0.971781 0.839202 2.88043 -#> Cell_27211 0.380391 0.847619 0.970686 0.579024 1.32143 -#> Cell_27212 0.626272 0.883905 0.950348 0.793084 2.22099 -#> Cell_27213 0.561809 0.840164 0.961928 0.686686 2.51304 -#> Cell_27214 0.276051 0.731293 0.907927 0.412575 1.40984 -#> Cell_27215 0.469189 0.615385 0.801868 0.557402 1.69281 +#> Cell_27210 0.583612 0.949367 0.971781 0.839202 2.88043 +#> Cell_27211 0.499965 0.847619 0.970686 0.579024 1.32143 +#> Cell_27212 0.680052 0.883905 0.950348 0.793084 2.22099 +#> Cell_27213 0.619852 0.840164 0.961928 0.686686 2.51304 +#> Cell_27214 0.336094 0.731293 0.907927 0.412575 1.40984 +#> Cell_27215 0.659214 0.615385 0.801868 0.557402 1.69281 #> mean_cor_correlation mean_celltype_correlation mean_cor_cosine #> <numeric> <factor> <numeric> #> Cell_27210 0.692426 CRABP2+ Malignant 0.753021 @@ -810,22 +786,22 @@

Calculate marker F1 purity

Calculate marker expressed pct

-
+
 
 spe <- calMarkerPct(spe,
                     celltype = "mean_celltype_correlation",
                     marker_list = positive_marker_list,
                     marker_list_name = "positive")
-head(colData(spe))
+head(colData(spe))
 #> DataFrame with 6 rows and 35 columns
 #>              cell_id pixel_size eccentricity cell_type  spearman
 #>            <numeric>  <numeric>    <numeric> <numeric> <numeric>
-#> Cell_27210     27210         92     0.727273         7  0.578500
-#> Cell_27211     27211         56     0.433962         6  0.408182
-#> Cell_27212     27212        181     0.928000         7  0.569310
-#> Cell_27213     27213        115     0.923077         7  0.474430
-#> Cell_27214     27214        122     0.336957         7  0.538882
-#> Cell_27215     27215        153     0.839744         7  0.546259
+#> Cell_27210     27210         92     0.808957         7  0.578500
+#> Cell_27211     27211         56         -Inf         6  0.408182
+#> Cell_27212     27212        181     0.904051         7  0.569310
+#> Cell_27213     27213        115     0.724503         7  0.474430
+#> Cell_27214     27214        122     0.280000         7  0.538882
+#> Cell_27215     27215        153     0.694516         7  0.546259
 #>            cell_type_atlas total_reads total_genes     slide   sample_id
 #>                  <numeric>   <numeric>   <integer> <integer> <character>
 #> Cell_27210               5         265          79         1    sample01
@@ -844,12 +820,12 @@ 

Calculate marker expressed pct#> Cell_27215 259 0.979502 153 0.839744 0.358405 #> sphericity solidity convexity circularity density #> <numeric> <numeric> <numeric> <numeric> <numeric> -#> Cell_27210 0.674331 0.949367 0.971781 0.839202 2.88043 -#> Cell_27211 0.380391 0.847619 0.970686 0.579024 1.32143 -#> Cell_27212 0.626272 0.883905 0.950348 0.793084 2.22099 -#> Cell_27213 0.561809 0.840164 0.961928 0.686686 2.51304 -#> Cell_27214 0.276051 0.731293 0.907927 0.412575 1.40984 -#> Cell_27215 0.469189 0.615385 0.801868 0.557402 1.69281 +#> Cell_27210 0.583612 0.949367 0.971781 0.839202 2.88043 +#> Cell_27211 0.499965 0.847619 0.970686 0.579024 1.32143 +#> Cell_27212 0.680052 0.883905 0.950348 0.793084 2.22099 +#> Cell_27213 0.619852 0.840164 0.961928 0.686686 2.51304 +#> Cell_27214 0.336094 0.731293 0.907927 0.412575 1.40984 +#> Cell_27215 0.659214 0.615385 0.801868 0.557402 1.69281 #> mean_cor_correlation mean_celltype_correlation mean_cor_cosine #> <numeric> <factor> <numeric> #> Cell_27210 0.692426 CRABP2+ Malignant 0.753021 @@ -898,22 +874,22 @@

Calculate marker expressed pct#> Cell_27213 0.681818 #> Cell_27214 1.000000 #> Cell_27215 0.772727

-
+
 
 spe <- calMarkerPct(spe,
                     celltype = "mean_celltype_correlation",
                     marker_list = negative_marker_list,
                     marker_list_name = "negative")
-head(colData(spe))
+head(colData(spe))
 #> DataFrame with 6 rows and 36 columns
 #>              cell_id pixel_size eccentricity cell_type  spearman
 #>            <numeric>  <numeric>    <numeric> <numeric> <numeric>
-#> Cell_27210     27210         92     0.727273         7  0.578500
-#> Cell_27211     27211         56     0.433962         6  0.408182
-#> Cell_27212     27212        181     0.928000         7  0.569310
-#> Cell_27213     27213        115     0.923077         7  0.474430
-#> Cell_27214     27214        122     0.336957         7  0.538882
-#> Cell_27215     27215        153     0.839744         7  0.546259
+#> Cell_27210     27210         92     0.808957         7  0.578500
+#> Cell_27211     27211         56         -Inf         6  0.408182
+#> Cell_27212     27212        181     0.904051         7  0.569310
+#> Cell_27213     27213        115     0.724503         7  0.474430
+#> Cell_27214     27214        122     0.280000         7  0.538882
+#> Cell_27215     27215        153     0.694516         7  0.546259
 #>            cell_type_atlas total_reads total_genes     slide   sample_id
 #>                  <numeric>   <numeric>   <integer> <integer> <character>
 #> Cell_27210               5         265          79         1    sample01
@@ -932,12 +908,12 @@ 

Calculate marker expressed pct#> Cell_27215 259 0.979502 153 0.839744 0.358405 #> sphericity solidity convexity circularity density #> <numeric> <numeric> <numeric> <numeric> <numeric> -#> Cell_27210 0.674331 0.949367 0.971781 0.839202 2.88043 -#> Cell_27211 0.380391 0.847619 0.970686 0.579024 1.32143 -#> Cell_27212 0.626272 0.883905 0.950348 0.793084 2.22099 -#> Cell_27213 0.561809 0.840164 0.961928 0.686686 2.51304 -#> Cell_27214 0.276051 0.731293 0.907927 0.412575 1.40984 -#> Cell_27215 0.469189 0.615385 0.801868 0.557402 1.69281 +#> Cell_27210 0.583612 0.949367 0.971781 0.839202 2.88043 +#> Cell_27211 0.499965 0.847619 0.970686 0.579024 1.32143 +#> Cell_27212 0.680052 0.883905 0.950348 0.793084 2.22099 +#> Cell_27213 0.619852 0.840164 0.961928 0.686686 2.51304 +#> Cell_27214 0.336094 0.731293 0.907927 0.412575 1.40984 +#> Cell_27215 0.659214 0.615385 0.801868 0.557402 1.69281 #> mean_cor_correlation mean_celltype_correlation mean_cor_cosine #> <numeric> <factor> <numeric> #> Cell_27210 0.692426 CRABP2+ Malignant 0.753021 @@ -991,38 +967,38 @@

Calculate marker expressed pct

Spatial Variation

-
+
 spe <- calSpatialMetricsDiversity(spe, 
                                   celltype = "mean_celltype_correlation")
-
+
 df <- spe@metadata$CellSPA$spatialMetricsDiversity$results
-ggplot(df, aes(x = x_bin, y = y_bin, fill = `cellTypeProp_ECM1+ Malignant`)) +
-    geom_tile() +
-    scale_fill_viridis_c() +
-    theme(aspect.ratio = 1, 
-          axis.text.x = element_blank(),
-          axis.text.y = element_blank(),
-          axis.ticks = element_blank()) +
-    labs(fill = "ECM1+ Malignant %") 
+ggplot(df, aes(x = x_bin, y = y_bin, fill = `cellTypeProp_ECM1+ Malignant`)) + + geom_tile() + + scale_fill_viridis_c() + + theme(aspect.ratio = 1, + axis.text.x = element_blank(), + axis.text.y = element_blank(), + axis.ticks = element_blank()) + + labs(fill = "ECM1+ Malignant %")

-
+
 
 
-ggplot(df, aes(x = entropy, y = cv_total_transciprts, 
+ggplot(df, aes(x = entropy, y = cv_total_transciprts, 
                color = `cellTypeProp_ECM1+ Malignant`)) +
-    geom_point() +
-    scale_color_viridis_c() +
-    theme(aspect.ratio = 1, 
-          axis.text.x = element_blank(),
-          axis.text.y = element_blank(),
-          axis.ticks = element_blank()) +
-    labs(color = "ECM1+ Malignant %")
+ geom_point() + + scale_color_viridis_c() + + theme(aspect.ratio = 1, + axis.text.x = element_blank(), + axis.text.y = element_blank(), + axis.ticks = element_blank()) + + labs(color = "ECM1+ Malignant %")

Neighbour purity

-
+
 nn_celltype_pair <- c("B Cells", "CD4 T|CD8 T")
 neg_markers <- list("B Cells" = c("CD3C", "CD3E", "CD8A"),
                     "CD4 T|CD8 T" = c("MS4A1", "CD79A", "CD79B"))
@@ -1054,15 +1030,15 @@ 

Neighbour purity

Read 10x output

-
+
 tenX_output_dir <- system.file("extdata/10x_output_subset", package = "CellSPA")
 tenX_output_tif <- system.file("extdata/10x_output_subset/10x_from_csv_subset.tif", package = "CellSPA")
 spe_10x <- readXenium(tenX_output_dir,
                       tiff_path = tenX_output_tif)
-
+
 
-keep_idx <- spatialCoords(spe_10x)[, 2] <= max(spatialCoords(spe)[, 1]) &
-    spatialCoords(spe_10x)[, 2] >= min(spatialCoords(spe)[, 1])
+keep_idx <- spatialCoords(spe_10x)[, 2] <= max(spatialCoords(spe)[, 1]) &
+    spatialCoords(spe_10x)[, 2] >= min(spatialCoords(spe)[, 1])
 spe_10x <- CellSPA::subset(spe_10x, which(keep_idx))
 
 spe_10x <- processingSPE(spe_10x,
@@ -1084,7 +1060,7 @@ 

Read 10x output#> spatialCoords names(2) : x_centroid y_centroid #> imgData names(1): sample_id

Run CellSPA with a wrapper function CellSPA.

-
+
 spe_10x <- CellSPA(spe_10x,
                    spe_celltype = NULL,
                    sce_ref = sce_ref,
@@ -1141,21 +1117,21 @@ 

Within method
+
 scater::plotColData(spe, "total_transciprts", 
                     x = "mean_celltype_correlation", 
                     colour_by = "mean_celltype_correlation") +
-    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
+ theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

-
+
 
 
 scater::plotColData(spe, "elongation", 
                     x = "mean_celltype_correlation", 
                     colour_by = "mean_celltype_correlation") +
-    theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
+ theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))

-
+
 spe_list <- list(BIDCell = spe,
                  `10x` = spe_10x)
@@ -1163,7 +1139,7 @@

Within method

Session Info

-
+
 sessionInfo()
 #> R version 4.4.1 (2024-06-14)
 #> Platform: x86_64-pc-linux-gnu
@@ -1187,15 +1163,15 @@ 

Session Info#> [8] base #> #> other attached packages: -#> [1] SpatialExperiment_1.14.0 scater_1.32.1 -#> [3] scuttle_1.14.0 SingleCellExperiment_1.26.0 -#> [5] SummarizedExperiment_1.34.0 Biobase_2.64.0 -#> [7] GenomicRanges_1.56.1 GenomeInfoDb_1.40.1 -#> [9] IRanges_2.38.1 S4Vectors_0.42.1 -#> [11] BiocGenerics_0.50.0 MatrixGenerics_1.16.0 -#> [13] matrixStats_1.4.1 ggthemes_5.1.0 -#> [15] ggplot2_3.5.1 CellSPA_0.1.0 -#> [17] BiocStyle_2.32.1 +#> [1] sf_1.0-17 SpatialExperiment_1.14.0 +#> [3] scater_1.32.1 scuttle_1.14.0 +#> [5] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0 +#> [7] Biobase_2.64.0 GenomicRanges_1.56.1 +#> [9] GenomeInfoDb_1.40.1 IRanges_2.38.1 +#> [11] S4Vectors_0.42.1 BiocGenerics_0.50.0 +#> [13] MatrixGenerics_1.16.0 matrixStats_1.4.1 +#> [15] ggthemes_5.1.0 ggplot2_3.5.1 +#> [17] CellSPA_0.1.0 BiocStyle_2.32.1 #> #> loaded via a namespace (and not attached): #> [1] RcppAnnoy_0.0.22 splines_4.4.1 @@ -1206,69 +1182,72 @@

Session Info#> [11] limma_3.60.4 sass_0.4.9 #> [13] rmarkdown_2.28 jquerylib_0.1.4 #> [15] yaml_2.3.10 sp_2.1-4 -#> [17] rgeos_0.6-4 multcomp_1.4-26 +#> [17] DBI_1.2.3 multcomp_1.4-26 #> [19] abind_1.4-5 zlibbioc_1.50.0 #> [21] purrr_1.0.2 R.utils_2.12.3 #> [23] Metrics_0.1.4 TH.data_1.1-2 #> [25] sandwich_3.1-0 GenomeInfoDbData_1.2.12 #> [27] ggrepel_0.9.6 irlba_2.3.5.1 -#> [29] spatstat.utils_3.1-0 spatstat.random_3.3-1 -#> [31] dqrng_0.4.1 pkgdown_2.1.0 -#> [33] DelayedMatrixStats_1.26.0 codetools_0.2-20 -#> [35] DropletUtils_1.24.0 coin_1.4-3 -#> [37] DelayedArray_0.30.1 alphahull_2.5 -#> [39] tidyselect_1.2.1 UCSC.utils_1.0.0 -#> [41] farver_2.1.2 ScaledMatrix_1.12.0 -#> [43] viridis_0.6.5 sgeostat_1.0-27 -#> [45] jsonlite_1.8.8 BiocNeighbors_1.22.0 -#> [47] survival_3.7-0 systemfonts_1.1.0 -#> [49] tools_4.4.1 ragg_1.3.3 -#> [51] Rcpp_1.0.13 glue_1.7.0 -#> [53] shotGroups_0.8.2 gridExtra_2.3 -#> [55] SparseArray_1.4.8 xfun_0.47 -#> [57] dplyr_1.1.4 HDF5Array_1.32.1 -#> [59] withr_3.0.1 BiocManager_1.30.25 -#> [61] fastmap_1.2.0 boot_1.3-31 -#> [63] rhdf5filters_1.16.0 fansi_1.0.6 -#> [65] entropy_1.3.1 digest_0.6.37 -#> [67] rsvd_1.0.5 R6_2.5.1 -#> [69] textshaping_0.4.0 colorspace_2.1-1 -#> [71] spatstat.data_3.1-2 R.methodsS3_1.8.2 -#> [73] utf8_1.2.4 generics_0.1.3 -#> [75] data.table_1.16.0 FNN_1.1.4 -#> [77] robustbase_0.99-4 httr_1.4.7 -#> [79] htmlwidgets_1.6.4 S4Arrays_1.4.1 -#> [81] uwot_0.2.2 pkgconfig_2.0.3 -#> [83] gtable_0.3.5 modeltools_0.2-23 -#> [85] XVector_0.44.0 htmltools_0.5.8.1 -#> [87] bookdown_0.40 scales_1.3.0 -#> [89] spatstat.univar_3.0-1 splancs_2.01-45 -#> [91] knitr_1.48 rstudioapi_0.16.0 -#> [93] reshape2_1.4.4 rjson_0.2.22 -#> [95] cachem_1.1.0 zoo_1.8-12 -#> [97] rhdf5_2.48.0 stringr_1.5.1 -#> [99] KernSmooth_2.23-24 parallel_4.4.1 -#> [101] vipor_0.4.7 libcoin_1.0-10 -#> [103] foreign_0.8-87 desc_1.4.3 -#> [105] pillar_1.9.0 grid_4.4.1 -#> [107] proxyC_0.4.1 vctrs_0.6.5 -#> [109] BiocSingular_1.20.0 beachmat_2.20.0 -#> [111] beeswarm_0.4.0 evaluate_0.24.0 -#> [113] maptools_1.1-8 magick_2.8.4 -#> [115] mvtnorm_1.3-1 cli_3.6.3 -#> [117] locfit_1.5-9.10 compiler_4.4.1 -#> [119] rlang_1.1.4 crayon_1.5.3 -#> [121] labeling_0.4.3 interp_1.1-6 -#> [123] plyr_1.8.9 fs_1.6.4 -#> [125] ggbeeswarm_0.7.2 stringi_1.8.4 -#> [127] viridisLite_0.4.2 deldir_2.0-4 -#> [129] BiocParallel_1.38.0 munsell_0.5.1 -#> [131] tiff_0.1-12 spatstat.geom_3.3-2 -#> [133] CompQuadForm_1.4.3 Matrix_1.7-0 -#> [135] sparseMatrixStats_1.16.0 Rhdf5lib_1.26.0 -#> [137] statmod_1.5.0 highr_0.11 -#> [139] igraph_2.0.3 RcppParallel_5.1.9 -#> [141] bslib_0.8.0 DEoptimR_1.1-3

+#> [29] spatstat.utils_3.1-0 terra_1.7-78 +#> [31] units_0.8-5 spatstat.random_3.3-1 +#> [33] dqrng_0.4.1 pkgdown_2.1.0 +#> [35] DelayedMatrixStats_1.26.0 codetools_0.2-20 +#> [37] DropletUtils_1.24.0 coin_1.4-3 +#> [39] DelayedArray_0.30.1 alphahull_2.5 +#> [41] tidyselect_1.2.1 raster_3.6-26 +#> [43] farver_2.1.2 UCSC.utils_1.0.0 +#> [45] ScaledMatrix_1.12.0 viridis_0.6.5 +#> [47] sgeostat_1.0-27 jsonlite_1.8.8 +#> [49] BiocNeighbors_1.22.0 e1071_1.7-16 +#> [51] survival_3.7-0 systemfonts_1.1.0 +#> [53] tools_4.4.1 ragg_1.3.3 +#> [55] Rcpp_1.0.13 glue_1.7.0 +#> [57] shotGroups_0.8.2 gridExtra_2.3 +#> [59] SparseArray_1.4.8 xfun_0.47 +#> [61] dplyr_1.1.4 HDF5Array_1.32.1 +#> [63] withr_3.0.1 BiocManager_1.30.25 +#> [65] fastmap_1.2.0 boot_1.3-31 +#> [67] rhdf5filters_1.16.0 fansi_1.0.6 +#> [69] entropy_1.3.1 digest_0.6.37 +#> [71] rsvd_1.0.5 R6_2.5.1 +#> [73] textshaping_0.4.0 colorspace_2.1-1 +#> [75] spatstat.data_3.1-2 R.methodsS3_1.8.2 +#> [77] utf8_1.2.4 generics_0.1.3 +#> [79] data.table_1.16.0 FNN_1.1.4 +#> [81] class_7.3-22 robustbase_0.99-4 +#> [83] httr_1.4.7 htmlwidgets_1.6.4 +#> [85] S4Arrays_1.4.1 uwot_0.2.2 +#> [87] pkgconfig_2.0.3 gtable_0.3.5 +#> [89] modeltools_0.2-23 XVector_0.44.0 +#> [91] htmltools_0.5.8.1 bookdown_0.40 +#> [93] scales_1.3.0 spatstat.univar_3.0-1 +#> [95] splancs_2.01-45 knitr_1.48 +#> [97] rstudioapi_0.16.0 reshape2_1.4.4 +#> [99] rjson_0.2.22 proxy_0.4-27 +#> [101] cachem_1.1.0 zoo_1.8-12 +#> [103] rhdf5_2.48.0 stringr_1.5.1 +#> [105] KernSmooth_2.23-24 parallel_4.4.1 +#> [107] vipor_0.4.7 libcoin_1.0-10 +#> [109] desc_1.4.3 pillar_1.9.0 +#> [111] grid_4.4.1 proxyC_0.4.1 +#> [113] vctrs_0.6.5 BiocSingular_1.20.0 +#> [115] beachmat_2.20.0 beeswarm_0.4.0 +#> [117] evaluate_0.24.0 magick_2.8.4 +#> [119] mvtnorm_1.3-1 cli_3.6.3 +#> [121] locfit_1.5-9.10 compiler_4.4.1 +#> [123] rlang_1.1.4 crayon_1.5.3 +#> [125] labeling_0.4.3 interp_1.1-6 +#> [127] classInt_0.4-10 plyr_1.8.9 +#> [129] fs_1.6.4 ggbeeswarm_0.7.2 +#> [131] stringi_1.8.4 viridisLite_0.4.2 +#> [133] deldir_2.0-4 BiocParallel_1.38.0 +#> [135] munsell_0.5.1 tiff_0.1-12 +#> [137] spatstat.geom_3.3-2 CompQuadForm_1.4.3 +#> [139] Matrix_1.7-0 sparseMatrixStats_1.16.0 +#> [141] Rhdf5lib_1.26.0 statmod_1.5.0 +#> [143] highr_0.11 igraph_2.0.3 +#> [145] RcppParallel_5.1.9 bslib_0.8.0 +#> [147] lwgeom_0.2-14 DEoptimR_1.1-3

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