We can now visualize local_outliers vs one of the QC
metrics, sum_log2, with help from the escheR
package.
Spot-level local outlier detectionlibrary(escheR)
library(ggpubr)
-# plotting using escheR
-p1 <- make_escheR(spe) |>
- add_fill(var = "sum_log2", point_size=1.25) +
- scale_fill_gradient(low ="white",high = "darkgreen")
+# library size
+p1 <- plotOutliers(spe, metric="sum_log2",
+ outliers="sum_outliers", point_size=1.1) +
+ ggtitle("Library Size")
+
+# unique genes
+p2 <- plotOutliers(spe, metric="detected_log2",
+ outliers="detected_outliers", point_size=1.1) +
+ ggtitle("Unique Genes")
+
+# mitochondrial percent
+p3 <- plotOutliers(spe, metric="subsets_Mito_percent",
+ outliers="subsets_Mito_percent_outliers", point_size=1.1) +
+ ggtitle("Mitochondrial Percent")
-p2 <- make_escheR(spe) |>
- add_fill(var = "sum_log2", point_size=1.25) |>
- add_ground(var = "local_outliers", stroke = 1) +
- scale_color_manual(
- name = "", # turn off legend name for ground_truth
- values = c(
- "TRUE" = "red",
- "FALSE" = "transparent")
- ) +
- scale_fill_gradient(low ="white",high = "darkgreen")
+# all local outliers
+p4 <- plotOutliers(spe, metric="sum_log2",
+ outliers="local_outliers", point_size=1.1, stroke=0.75) +
+ ggtitle("All Local Outliers")
-plot_list <- list(p1, p2)
+# plot
+plot_list <- list(p1, p2, p3, p4)
ggarrange(
plotlist = plot_list,
- ncol = 2, nrow = 1,
+ ncol = 2, nrow = 2,
common.legend = FALSE
)
@@ -201,9 +207,9 @@ Session information
utils::sessionInfo()
-#> R version 4.3.2 (2023-10-31)
+#> R version 4.3.3 (2024-02-29)
#> Platform: x86_64-pc-linux-gnu (64-bit)
-#> Running under: Ubuntu 22.04.3 LTS
+#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
@@ -224,70 +230,69 @@ Session information#>
#> other attached packages:
#> [1] ggpubr_0.6.0 escheR_1.2.0
-#> [3] ggplot2_3.4.4 STexampleData_1.10.0
+#> [3] ggplot2_3.5.0 STexampleData_1.10.1
#> [5] SpatialExperiment_1.12.0 SingleCellExperiment_1.24.0
#> [7] SummarizedExperiment_1.32.0 Biobase_2.62.0
-#> [9] GenomicRanges_1.54.1 GenomeInfoDb_1.38.6
+#> [9] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8
#> [11] IRanges_2.36.0 S4Vectors_0.40.2
#> [13] MatrixGenerics_1.14.0 matrixStats_1.2.0
#> [15] ExperimentHub_2.10.0 AnnotationHub_3.10.0
-#> [17] BiocFileCache_2.10.1 dbplyr_2.4.0
+#> [17] BiocFileCache_2.10.1 dbplyr_2.5.0
#> [19] BiocGenerics_0.48.1 SpotSweeper_0.99.1
#>
#> loaded via a namespace (and not attached):
-#> [1] DBI_1.2.1 bitops_1.0-7
+#> [1] DBI_1.2.2 bitops_1.0-7
#> [3] rlang_1.1.3 magrittr_2.0.3
-#> [5] compiler_4.3.2 RSQLite_2.3.5
+#> [5] compiler_4.3.3 RSQLite_2.3.5
#> [7] DelayedMatrixStats_1.24.0 png_0.1-8
-#> [9] systemfonts_1.0.5 vctrs_0.6.5
-#> [11] stringr_1.5.1 pkgconfig_2.0.3
-#> [13] crayon_1.5.2 fastmap_1.1.1
-#> [15] backports_1.4.1 magick_2.8.2
-#> [17] XVector_0.42.0 ellipsis_0.3.2
-#> [19] labeling_0.4.3 scuttle_1.12.0
-#> [21] utf8_1.2.4 promises_1.2.1
-#> [23] rmarkdown_2.25 ragg_1.2.7
-#> [25] purrr_1.0.2 bit_4.0.5
-#> [27] xfun_0.42 beachmat_2.18.0
-#> [29] zlibbioc_1.48.0 cachem_1.0.8
-#> [31] jsonlite_1.8.8 blob_1.2.4
-#> [33] highr_0.10 later_1.3.2
-#> [35] DelayedArray_0.28.0 BiocParallel_1.36.0
-#> [37] interactiveDisplayBase_1.40.0 broom_1.0.5
-#> [39] parallel_4.3.2 R6_2.5.1
-#> [41] bslib_0.6.1 stringi_1.8.3
-#> [43] car_3.1-2 jquerylib_0.1.4
-#> [45] Rcpp_1.0.12 knitr_1.45
-#> [47] httpuv_1.6.14 Matrix_1.6-1.1
-#> [49] tidyselect_1.2.0 abind_1.4-5
-#> [51] yaml_2.3.8 codetools_0.2-19
-#> [53] curl_5.2.0 lattice_0.21-9
-#> [55] tibble_3.2.1 shiny_1.8.0
-#> [57] withr_3.0.0 KEGGREST_1.42.0
-#> [59] evaluate_0.23 desc_1.4.3
-#> [61] Biostrings_2.70.2 pillar_1.9.0
-#> [63] BiocManager_1.30.22 filelock_1.0.3
-#> [65] carData_3.0-5 generics_0.1.3
-#> [67] dbscan_1.1-12 RCurl_1.98-1.14
-#> [69] BiocVersion_3.18.1 munsell_0.5.0
-#> [71] scales_1.3.0 sparseMatrixStats_1.14.0
-#> [73] xtable_1.8-4 glue_1.7.0
-#> [75] tools_4.3.2 BiocNeighbors_1.20.2
-#> [77] ggsignif_0.6.4 fs_1.6.3
-#> [79] cowplot_1.1.3 grid_4.3.2
-#> [81] tidyr_1.3.1 colorspace_2.1-0
-#> [83] AnnotationDbi_1.64.1 GenomeInfoDbData_1.2.11
-#> [85] cli_3.6.2 rappdirs_0.3.3
-#> [87] textshaping_0.3.7 fansi_1.0.6
-#> [89] viridisLite_0.4.2 S4Arrays_1.2.0
-#> [91] dplyr_1.1.4 gtable_0.3.4
-#> [93] rstatix_0.7.2 sass_0.4.8
-#> [95] digest_0.6.34 SparseArray_1.2.4
-#> [97] farver_2.1.1 rjson_0.2.21
-#> [99] memoise_2.0.1 htmltools_0.5.7
-#> [101] pkgdown_2.0.7 lifecycle_1.0.4
-#> [103] httr_1.4.7 mime_0.12
-#> [105] bit64_4.0.5 MASS_7.3-60
+#> [9] systemfonts_1.0.6 vctrs_0.6.5
+#> [11] pkgconfig_2.0.3 crayon_1.5.2
+#> [13] fastmap_1.1.1 backports_1.4.1
+#> [15] magick_2.8.3 XVector_0.42.0
+#> [17] ellipsis_0.3.2 labeling_0.4.3
+#> [19] scuttle_1.12.0 utf8_1.2.4
+#> [21] promises_1.2.1 rmarkdown_2.26
+#> [23] ragg_1.3.0 purrr_1.0.2
+#> [25] bit_4.0.5 xfun_0.42
+#> [27] beachmat_2.18.1 zlibbioc_1.48.2
+#> [29] cachem_1.0.8 jsonlite_1.8.8
+#> [31] blob_1.2.4 highr_0.10
+#> [33] later_1.3.2 DelayedArray_0.28.0
+#> [35] BiocParallel_1.36.0 interactiveDisplayBase_1.40.0
+#> [37] broom_1.0.5 parallel_4.3.3
+#> [39] R6_2.5.1 bslib_0.6.1
+#> [41] car_3.1-2 jquerylib_0.1.4
+#> [43] Rcpp_1.0.12 knitr_1.45
+#> [45] httpuv_1.6.14 Matrix_1.6-5
+#> [47] tidyselect_1.2.1 abind_1.4-5
+#> [49] yaml_2.3.8 codetools_0.2-19
+#> [51] curl_5.2.1 lattice_0.22-5
+#> [53] tibble_3.2.1 withr_3.0.0
+#> [55] KEGGREST_1.42.0 shiny_1.8.0
+#> [57] evaluate_0.23 desc_1.4.3
+#> [59] Biostrings_2.70.3 pillar_1.9.0
+#> [61] BiocManager_1.30.22 filelock_1.0.3
+#> [63] carData_3.0-5 generics_0.1.3
+#> [65] RCurl_1.98-1.14 BiocVersion_3.18.1
+#> [67] sparseMatrixStats_1.14.0 munsell_0.5.0
+#> [69] scales_1.3.0 xtable_1.8-4
+#> [71] glue_1.7.0 tools_4.3.3
+#> [73] BiocNeighbors_1.20.2 ggsignif_0.6.4
+#> [75] fs_1.6.3 cowplot_1.1.3
+#> [77] grid_4.3.3 tidyr_1.3.1
+#> [79] AnnotationDbi_1.64.1 colorspace_2.1-0
+#> [81] GenomeInfoDbData_1.2.11 cli_3.6.2
+#> [83] rappdirs_0.3.3 textshaping_0.3.7
+#> [85] fansi_1.0.6 viridisLite_0.4.2
+#> [87] S4Arrays_1.2.1 dplyr_1.1.4
+#> [89] gtable_0.3.4 rstatix_0.7.2
+#> [91] sass_0.4.9 digest_0.6.35
+#> [93] SparseArray_1.2.4 farver_2.1.1
+#> [95] rjson_0.2.21 memoise_2.0.1
+#> [97] htmltools_0.5.7 pkgdown_2.0.7
+#> [99] lifecycle_1.0.4 httr_1.4.7
+#> [101] mime_0.12 bit64_4.0.5
+#> [103] MASS_7.3-60.0.1
diff --git a/articles/getting_started_files/figure-html/local_outlier_plot-1.png b/articles/getting_started_files/figure-html/local_outlier_plot-1.png
index de37f45..9581314 100644
Binary files a/articles/getting_started_files/figure-html/local_outlier_plot-1.png and b/articles/getting_started_files/figure-html/local_outlier_plot-1.png differ
diff --git a/index.html b/index.html
index dc7ffcc..b83ed1f 100644
--- a/index.html
+++ b/index.html
@@ -5,14 +5,26 @@
-
-SpotSweeper • SpotSweeper
+
+Spatially-aware quality control for spatial transcriptomics • SpotSweeper
-
-
+
+
localOutliers Function — localOutliers • SpotSweeper localOutliers Function — localOutliers • SpotSweeper
@@ -54,78 +58,60 @@
- This function detects local outliers based on k-nearest neighbors based on either a univariate
-(z-score thresholds per QC metrics) or multivariate approach (Local Outlier Factor).
+ This function detects local outliers in spatial transcriptomics data based on standard
+quality control metrics, such as library size, unique genes, and mitochondrial ratio.
+Local outliers are defined as spots with low/high quality metrics compared to their
+surrounding neighbors, based on a modified z-score statistic.
Usage
localOutliers(
spe,
+ metric = "detected",
+ direction = "lower",
n_neighbors = 36,
- features = c("sum_umi", "sum_gene", "expr_chrM_ratio"),
- method = "multivariate",
samples = "sample_id",
- log2 = TRUE,
- cutoff = 2.58,
- scale = TRUE,
- minPts = 20,
- data_output = FALSE,
- n_cores = 1
+ log = TRUE,
+ cutoff = 3
)
Arguments
- spe
-SpatialExperiment object with the following columns in colData: sample_id, sum_umi, sum_gene
SpatialExperiment object
- n_neighbors
-Number of nearest neighbors to use for outlier detection
- metric
+colData QC metric to use for outlier detection
- features
-Vector of features to use for outlier detection
- direction
+Direction of outlier detection (higher, lower, or both)
- method
-Method to use for outlier detection (univariate or multivariate)
- n_neighbors
+Number of nearest neighbors to use for outlier detection
- samples
Column name in colData to use for sample IDs
- log2
-Logical indicating whether to log2 transform the features
- log
+Logical indicating whether to log2 transform the features (default is TRUE)
- cutoff
-Cutoff for outlier detection
- scale
-Logical indicating whether to scale the features for LOF calculation (recommended)
- minPts
-Minimum number of points (nearest neighbors) to use for LOF calculation
- data_output
-Logical indicating whether to output the z-scores for each feature
- n_cores
-Number of cores to use for parallelization in the findKNN function
Cutoff for outlier detection (default is 3)
Value
-SpatialExperiment object with updated colData
+SpatialExperiment object with updated colData containing outputs
@@ -141,11 +127,6 @@ Examples# change from gene id to gene names
rownames(spe) <- rowData(spe)$gene_name
-# show column data before SpotSweepR
-colnames(colData(spe))
-#> [1] "barcode_id" "sample_id" "in_tissue" "array_row" "array_col"
-#> [6] "ground_truth" "cell_count"
-
# drop out-of-tissue spots
spe <- spe[, spe$in_tissue == 1]
spe <- spe[, !is.na(spe$ground_truth)]
@@ -153,22 +134,12 @@ Examples# Identifying the mitochondrial transcripts in our SpatialExperiment.
is.mito <- rownames(spe)[grepl("^MT-", rownames(spe))]
-# Calculating QC metrics for each spot using scuttle
-spe<- scuttle::addPerCellQCMetrics(spe, subsets=list(Mito=is.mito))
-colnames(colData(spe))
-#> [1] "barcode_id" "sample_id" "in_tissue"
-#> [4] "array_row" "array_col" "ground_truth"
-#> [7] "cell_count" "sum" "detected"
-#> [10] "subsets_Mito_sum" "subsets_Mito_detected" "subsets_Mito_percent"
-#> [13] "total"
-
+# Calculating QC features for each spot using scuttle
+spe<- scuttle::addPerCellQC(spe, subsets=list(Mito=is.mito))
-features <- c('sum' ,'detected', "subsets_Mito_percent")
spe<- localOutliers(spe,
- features=features,
- n_neighbors=36,
- data_output=TRUE,
- method="multivariate"
+ metric="detected",
+ direction="lower"
)
diff --git a/reference/localVariance.html b/reference/localVariance.html
index 21aa13e..c255937 100644
--- a/reference/localVariance.html
+++ b/reference/localVariance.html
@@ -146,6 +146,7 @@ Examples n_neighbors=36,
name="local_mito_variance_k36"
)
+#> Error in h(simpleError(msg, call)): error in evaluating the argument 'j' in selecting a method for function '[': comparison (==) is possible only for atomic and list types
utils::sessionInfo()
-#> R version 4.3.2 (2023-10-31)
+#> R version 4.3.3 (2024-02-29)
#> Platform: x86_64-pc-linux-gnu (64-bit)
-#> Running under: Ubuntu 22.04.3 LTS
+#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
@@ -224,70 +230,69 @@ Session information#>
#> other attached packages:
#> [1] ggpubr_0.6.0 escheR_1.2.0
-#> [3] ggplot2_3.4.4 STexampleData_1.10.0
+#> [3] ggplot2_3.5.0 STexampleData_1.10.1
#> [5] SpatialExperiment_1.12.0 SingleCellExperiment_1.24.0
#> [7] SummarizedExperiment_1.32.0 Biobase_2.62.0
-#> [9] GenomicRanges_1.54.1 GenomeInfoDb_1.38.6
+#> [9] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8
#> [11] IRanges_2.36.0 S4Vectors_0.40.2
#> [13] MatrixGenerics_1.14.0 matrixStats_1.2.0
#> [15] ExperimentHub_2.10.0 AnnotationHub_3.10.0
-#> [17] BiocFileCache_2.10.1 dbplyr_2.4.0
+#> [17] BiocFileCache_2.10.1 dbplyr_2.5.0
#> [19] BiocGenerics_0.48.1 SpotSweeper_0.99.1
#>
#> loaded via a namespace (and not attached):
-#> [1] DBI_1.2.1 bitops_1.0-7
+#> [1] DBI_1.2.2 bitops_1.0-7
#> [3] rlang_1.1.3 magrittr_2.0.3
-#> [5] compiler_4.3.2 RSQLite_2.3.5
+#> [5] compiler_4.3.3 RSQLite_2.3.5
#> [7] DelayedMatrixStats_1.24.0 png_0.1-8
-#> [9] systemfonts_1.0.5 vctrs_0.6.5
-#> [11] stringr_1.5.1 pkgconfig_2.0.3
-#> [13] crayon_1.5.2 fastmap_1.1.1
-#> [15] backports_1.4.1 magick_2.8.2
-#> [17] XVector_0.42.0 ellipsis_0.3.2
-#> [19] labeling_0.4.3 scuttle_1.12.0
-#> [21] utf8_1.2.4 promises_1.2.1
-#> [23] rmarkdown_2.25 ragg_1.2.7
-#> [25] purrr_1.0.2 bit_4.0.5
-#> [27] xfun_0.42 beachmat_2.18.0
-#> [29] zlibbioc_1.48.0 cachem_1.0.8
-#> [31] jsonlite_1.8.8 blob_1.2.4
-#> [33] highr_0.10 later_1.3.2
-#> [35] DelayedArray_0.28.0 BiocParallel_1.36.0
-#> [37] interactiveDisplayBase_1.40.0 broom_1.0.5
-#> [39] parallel_4.3.2 R6_2.5.1
-#> [41] bslib_0.6.1 stringi_1.8.3
-#> [43] car_3.1-2 jquerylib_0.1.4
-#> [45] Rcpp_1.0.12 knitr_1.45
-#> [47] httpuv_1.6.14 Matrix_1.6-1.1
-#> [49] tidyselect_1.2.0 abind_1.4-5
-#> [51] yaml_2.3.8 codetools_0.2-19
-#> [53] curl_5.2.0 lattice_0.21-9
-#> [55] tibble_3.2.1 shiny_1.8.0
-#> [57] withr_3.0.0 KEGGREST_1.42.0
-#> [59] evaluate_0.23 desc_1.4.3
-#> [61] Biostrings_2.70.2 pillar_1.9.0
-#> [63] BiocManager_1.30.22 filelock_1.0.3
-#> [65] carData_3.0-5 generics_0.1.3
-#> [67] dbscan_1.1-12 RCurl_1.98-1.14
-#> [69] BiocVersion_3.18.1 munsell_0.5.0
-#> [71] scales_1.3.0 sparseMatrixStats_1.14.0
-#> [73] xtable_1.8-4 glue_1.7.0
-#> [75] tools_4.3.2 BiocNeighbors_1.20.2
-#> [77] ggsignif_0.6.4 fs_1.6.3
-#> [79] cowplot_1.1.3 grid_4.3.2
-#> [81] tidyr_1.3.1 colorspace_2.1-0
-#> [83] AnnotationDbi_1.64.1 GenomeInfoDbData_1.2.11
-#> [85] cli_3.6.2 rappdirs_0.3.3
-#> [87] textshaping_0.3.7 fansi_1.0.6
-#> [89] viridisLite_0.4.2 S4Arrays_1.2.0
-#> [91] dplyr_1.1.4 gtable_0.3.4
-#> [93] rstatix_0.7.2 sass_0.4.8
-#> [95] digest_0.6.34 SparseArray_1.2.4
-#> [97] farver_2.1.1 rjson_0.2.21
-#> [99] memoise_2.0.1 htmltools_0.5.7
-#> [101] pkgdown_2.0.7 lifecycle_1.0.4
-#> [103] httr_1.4.7 mime_0.12
-#> [105] bit64_4.0.5 MASS_7.3-60
+#> [9] systemfonts_1.0.6 vctrs_0.6.5
+#> [11] pkgconfig_2.0.3 crayon_1.5.2
+#> [13] fastmap_1.1.1 backports_1.4.1
+#> [15] magick_2.8.3 XVector_0.42.0
+#> [17] ellipsis_0.3.2 labeling_0.4.3
+#> [19] scuttle_1.12.0 utf8_1.2.4
+#> [21] promises_1.2.1 rmarkdown_2.26
+#> [23] ragg_1.3.0 purrr_1.0.2
+#> [25] bit_4.0.5 xfun_0.42
+#> [27] beachmat_2.18.1 zlibbioc_1.48.2
+#> [29] cachem_1.0.8 jsonlite_1.8.8
+#> [31] blob_1.2.4 highr_0.10
+#> [33] later_1.3.2 DelayedArray_0.28.0
+#> [35] BiocParallel_1.36.0 interactiveDisplayBase_1.40.0
+#> [37] broom_1.0.5 parallel_4.3.3
+#> [39] R6_2.5.1 bslib_0.6.1
+#> [41] car_3.1-2 jquerylib_0.1.4
+#> [43] Rcpp_1.0.12 knitr_1.45
+#> [45] httpuv_1.6.14 Matrix_1.6-5
+#> [47] tidyselect_1.2.1 abind_1.4-5
+#> [49] yaml_2.3.8 codetools_0.2-19
+#> [51] curl_5.2.1 lattice_0.22-5
+#> [53] tibble_3.2.1 withr_3.0.0
+#> [55] KEGGREST_1.42.0 shiny_1.8.0
+#> [57] evaluate_0.23 desc_1.4.3
+#> [59] Biostrings_2.70.3 pillar_1.9.0
+#> [61] BiocManager_1.30.22 filelock_1.0.3
+#> [63] carData_3.0-5 generics_0.1.3
+#> [65] RCurl_1.98-1.14 BiocVersion_3.18.1
+#> [67] sparseMatrixStats_1.14.0 munsell_0.5.0
+#> [69] scales_1.3.0 xtable_1.8-4
+#> [71] glue_1.7.0 tools_4.3.3
+#> [73] BiocNeighbors_1.20.2 ggsignif_0.6.4
+#> [75] fs_1.6.3 cowplot_1.1.3
+#> [77] grid_4.3.3 tidyr_1.3.1
+#> [79] AnnotationDbi_1.64.1 colorspace_2.1-0
+#> [81] GenomeInfoDbData_1.2.11 cli_3.6.2
+#> [83] rappdirs_0.3.3 textshaping_0.3.7
+#> [85] fansi_1.0.6 viridisLite_0.4.2
+#> [87] S4Arrays_1.2.1 dplyr_1.1.4
+#> [89] gtable_0.3.4 rstatix_0.7.2
+#> [91] sass_0.4.9 digest_0.6.35
+#> [93] SparseArray_1.2.4 farver_2.1.1
+#> [95] rjson_0.2.21 memoise_2.0.1
+#> [97] htmltools_0.5.7 pkgdown_2.0.7
+#> [99] lifecycle_1.0.4 httr_1.4.7
+#> [101] mime_0.12 bit64_4.0.5
+#> [103] MASS_7.3-60.0.1
diff --git a/articles/getting_started_files/figure-html/local_outlier_plot-1.png b/articles/getting_started_files/figure-html/local_outlier_plot-1.png
index de37f45..9581314 100644
Binary files a/articles/getting_started_files/figure-html/local_outlier_plot-1.png and b/articles/getting_started_files/figure-html/local_outlier_plot-1.png differ
diff --git a/index.html b/index.html
index dc7ffcc..b83ed1f 100644
--- a/index.html
+++ b/index.html
@@ -5,14 +5,26 @@
-
-
-
This function detects local outliers based on k-nearest neighbors based on either a univariate -(z-score thresholds per QC metrics) or multivariate approach (Local Outlier Factor).
+This function detects local outliers in spatial transcriptomics data based on standard +quality control metrics, such as library size, unique genes, and mitochondrial ratio. +Local outliers are defined as spots with low/high quality metrics compared to their +surrounding neighbors, based on a modified z-score statistic.
Usage
localOutliers(
spe,
+ metric = "detected",
+ direction = "lower",
n_neighbors = 36,
- features = c("sum_umi", "sum_gene", "expr_chrM_ratio"),
- method = "multivariate",
samples = "sample_id",
- log2 = TRUE,
- cutoff = 2.58,
- scale = TRUE,
- minPts = 20,
- data_output = FALSE,
- n_cores = 1
+ log = TRUE,
+ cutoff = 3
)Arguments
- spe -
SpatialExperiment object with the following columns in colData: sample_id, sum_umi, sum_gene
+SpatialExperiment object
-- n_neighbors -
Number of nearest neighbors to use for outlier detection
+- metric +
colData QC metric to use for outlier detection
-- features -
Vector of features to use for outlier detection
+- direction +
Direction of outlier detection (higher, lower, or both)
-- method -
Method to use for outlier detection (univariate or multivariate)
+- n_neighbors +
Number of nearest neighbors to use for outlier detection
- samples
Column name in colData to use for sample IDs
-- log2 -
Logical indicating whether to log2 transform the features
+- log +
Logical indicating whether to log2 transform the features (default is TRUE)
- cutoff -
Cutoff for outlier detection
-
-
-- scale -
Logical indicating whether to scale the features for LOF calculation (recommended)
-
-
-- minPts -
Minimum number of points (nearest neighbors) to use for LOF calculation
-
-
-- data_output -
Logical indicating whether to output the z-scores for each feature
-
-
-- n_cores -
Number of cores to use for parallelization in the findKNN function
+Cutoff for outlier detection (default is 3)
Value
-SpatialExperiment object with updated colData
+SpatialExperiment object with updated colData containing outputs
@@ -141,11 +127,6 @@ Examples# change from gene id to gene names
rownames(spe) <- rowData(spe)$gene_name
-# show column data before SpotSweepR
-colnames(colData(spe))
-#> [1] "barcode_id" "sample_id" "in_tissue" "array_row" "array_col"
-#> [6] "ground_truth" "cell_count"
-
# drop out-of-tissue spots
spe <- spe[, spe$in_tissue == 1]
spe <- spe[, !is.na(spe$ground_truth)]
@@ -153,22 +134,12 @@
diff --git a/reference/localVariance.html b/reference/localVariance.html
index 21aa13e..c255937 100644
--- a/reference/localVariance.html
+++ b/reference/localVariance.html
@@ -146,6 +146,7 @@