Visualize detected doublets

Usage

VizSeuratDoublets(
  object,
  plot_type = c("dim", "pie", "pk", "pK"),
  palette = "Set2",
  ...
)

Arguments

object

A Seurat object with detected doublets

plot_type

Type of plot to generate One of 'dim', 'pie', 'pk', 'pK'. dim/pie show the distribution of doublets by droplet type. pk/pK show the relationship between BC metric and pK when using DoubletFinder.

palette

Color palette to use

...

Additional arguments to pass to the plot function

For 'dim', additional arguments to pass to scplotter::CellDimPlot
For 'pie', additional arguments to pass to plotthis::PieChart
For 'pk' or 'pK', additional arguments to pass to plotthis::LinePlot

Value

The plot

Examples

# \donttest{
datadir <- system.file("extdata", "scrna", package = "biopipen.utils")
meta <- data.frame(
    Sample = c("Sample1", "Sample2"),
    RNAData = c(
        file.path(datadir, "Sample1"),
        file.path(datadir, "Sample2")
    )
)

obj <- LoadSeuratAndPerformQC(meta, cache = FALSE, gene_qc = list(min_cells = 3),
    cell_qc = "nFeature_RNA > 500")
#> INFO    [2025-04-23 18:07:12] Loading each sample ...
#> INFO    [2025-04-23 18:07:12] - Loading Sample1 and performing QC ...
#> INFO    [2025-04-23 18:07:12] - Loading Sample2 and performing QC ...
#> INFO    [2025-04-23 18:07:12] Merging samples ...
VizSeuratCellQC(obj)

VizSeuratCellQC(obj, plot_type = "scatter")

VizSeuratCellQC(obj, plot_type = "ridge")
#> Picking joint bandwidth of 177
#> Picking joint bandwidth of 61.9
#> Picking joint bandwidth of 0.341
#> Picking joint bandwidth of 0.341
#> Picking joint bandwidth of 0.341
#> Picking joint bandwidth of 0.341
#> Picking joint bandwidth of 177
#> Picking joint bandwidth of 61.9
#> Picking joint bandwidth of 0.341
#> Picking joint bandwidth of 0.341
#> Picking joint bandwidth of 0.341
#> Picking joint bandwidth of 0.341

VizSeuratGeneQC(obj)
#> Using `bins = 30`. Pick better value with `binwidth`.


obj <- RunSeuratTransformation(obj)
#> INFO    [2025-04-23 18:07:16] Performing data transformation and scaling ...
#> INFO    [2025-04-23 18:07:16] - Running NormalizeData ...
#> Normalizing layer: counts.Sample1
#> Normalizing layer: counts.Sample2
#> INFO    [2025-04-23 18:07:18] - Running FindVariableFeatures ...
#> Finding variable features for layer counts.Sample1
#> Finding variable features for layer counts.Sample2
#> INFO    [2025-04-23 18:07:19] - Running ScaleData ...
#> Centering and scaling data matrix
#> INFO    [2025-04-23 18:07:20] - Running RunPCA ...
#> PC_ 1 
#> Positive:  ENSG00000091513, ENSG00000171766, ENSG00000123560, ENSG00000230885, ENSG00000172005, ENSG00000173786, ENSG00000013297, ENSG00000174607, ENSG00000168314, ENSG00000105695 
#> 	   ENSG00000167641, ENSG00000136541, ENSG00000103089, ENSG00000184221, ENSG00000197971, ENSG00000197430, ENSG00000139433, ENSG00000115290, ENSG00000159176, ENSG00000120913 
#> 	   ENSG00000188643, ENSG00000134198, ENSG00000130429, ENSG00000205116, ENSG00000104267, ENSG00000102934, ENSG00000148180, ENSG00000127920, ENSG00000169562, ENSG00000189058 
#> Negative:  ENSG00000128683, ENSG00000074317, ENSG00000102804, ENSG00000143153, ENSG00000197457, ENSG00000132639, ENSG00000133169, ENSG00000213190, ENSG00000139970, ENSG00000213760 
#> 	   ENSG00000101489, ENSG00000125869, ENSG00000122952, ENSG00000151778, ENSG00000152954, ENSG00000182698, ENSG00000105649, ENSG00000162188, ENSG00000170004, ENSG00000104490 
#> 	   ENSG00000168081, ENSG00000115738, ENSG00000128739, ENSG00000170091, ENSG00000220205, ENSG00000250479, ENSG00000100092, ENSG00000273079, ENSG00000157152, ENSG00000152932 
#> PC_ 2 
#> Positive:  ENSG00000109846, ENSG00000105290, ENSG00000104833, ENSG00000088986, ENSG00000168314, ENSG00000134198, ENSG00000172005, ENSG00000132718, ENSG00000088832, ENSG00000013297 
#> 	   ENSG00000115290, ENSG00000230885, ENSG00000167641, ENSG00000136541, ENSG00000184221, ENSG00000103089, ENSG00000174607, ENSG00000149557, ENSG00000197430, ENSG00000151552 
#> 	   ENSG00000205116, ENSG00000108387, ENSG00000104267, ENSG00000173786, ENSG00000015592, ENSG00000123560, ENSG00000198892, ENSG00000102934, ENSG00000188643, ENSG00000159176 
#> Negative:  ENSG00000163131, ENSG00000159189, ENSG00000049860, ENSG00000169313, ENSG00000173372, ENSG00000173369, ENSG00000110876, ENSG00000158869, ENSG00000101439, ENSG00000171659 
#> 	   ENSG00000011600, ENSG00000183160, ENSG00000095970, ENSG00000162511, ENSG00000182578, ENSG00000168329, ENSG00000051523, ENSG00000206428, ENSG00000143226, ENSG00000100600 
#> 	   ENSG00000273686, ENSG00000113140, ENSG00000110057, ENSG00000166428, ENSG00000116774, ENSG00000258818, ENSG00000117984, ENSG00000112799, ENSG00000213316, ENSG00000226248 
#> PC_ 3 
#> Positive:  ENSG00000134419, ENSG00000138326, ENSG00000145592, ENSG00000142534, ENSG00000177600, ENSG00000196262, ENSG00000110700, ENSG00000158869, ENSG00000173369, ENSG00000163131 
#> 	   ENSG00000173372, ENSG00000110876, ENSG00000159189, ENSG00000197958, ENSG00000011600, ENSG00000128245, ENSG00000142541, ENSG00000183160, ENSG00000113140, ENSG00000171659 
#> 	   ENSG00000182578, ENSG00000169313, ENSG00000114391, ENSG00000049860, ENSG00000162511, ENSG00000095970, ENSG00000168329, ENSG00000273686, ENSG00000051523, ENSG00000171858 
#> Negative:  ENSG00000139182, ENSG00000132718, ENSG00000092096, ENSG00000197106, ENSG00000111897, ENSG00000172247, ENSG00000181790, ENSG00000174437, ENSG00000101438, ENSG00000101180 
#> 	   ENSG00000143153, ENSG00000187957, ENSG00000185818, ENSG00000105409, ENSG00000135472, ENSG00000163536, ENSG00000071553, ENSG00000180155, ENSG00000163399, ENSG00000157087 
#> 	   ENSG00000063015, ENSG00000179218, ENSG00000152583, ENSG00000069667, ENSG00000171951, ENSG00000179222, ENSG00000176884, ENSG00000168081, ENSG00000164742, ENSG00000154096 
#> PC_ 4 
#> Positive:  ENSG00000172809, ENSG00000087258, ENSG00000161016, ENSG00000233927, ENSG00000145592, ENSG00000161970, ENSG00000142541, ENSG00000182899, ENSG00000170027, ENSG00000173376 
#> 	   ENSG00000198918, ENSG00000138326, ENSG00000189056, ENSG00000106244, ENSG00000171858, ENSG00000118432, ENSG00000146676, ENSG00000132535, ENSG00000048740, ENSG00000176788 
#> 	   ENSG00000175352, ENSG00000114391, ENSG00000134419, ENSG00000122026, ENSG00000143195, ENSG00000166441, ENSG00000110076, ENSG00000174444, ENSG00000138650, ENSG00000233954 
#> Negative:  ENSG00000166922, ENSG00000099797, ENSG00000117410, ENSG00000136156, ENSG00000110651, ENSG00000182636, ENSG00000182698, ENSG00000111716, ENSG00000128463, ENSG00000143198 
#> 	   ENSG00000102471, ENSG00000184277, ENSG00000116459, ENSG00000014641, ENSG00000062716, ENSG00000134333, ENSG00000152583, ENSG00000120885, ENSG00000144746, ENSG00000115310 
#> 	   ENSG00000080824, ENSG00000277791, ENSG00000005022, ENSG00000123395, ENSG00000151366, ENSG00000075415, ENSG00000137267, ENSG00000166736, ENSG00000140740, ENSG00000163479 
#> PC_ 5 
#> Positive:  ENSG00000117984, ENSG00000179218, ENSG00000158869, ENSG00000100600, ENSG00000136156, ENSG00000030582, ENSG00000011600, ENSG00000173372, ENSG00000182578, ENSG00000168329 
#> 	   ENSG00000173369, ENSG00000101160, ENSG00000163131, ENSG00000109861, ENSG00000113140, ENSG00000027697, ENSG00000185896, ENSG00000226248, ENSG00000051523, ENSG00000183160 
#> 	   ENSG00000116774, ENSG00000224156, ENSG00000135919, ENSG00000089157, ENSG00000064601, ENSG00000278032, ENSG00000110876, ENSG00000128564, ENSG00000118705, ENSG00000159189 
#> Negative:  ENSG00000182698, ENSG00000178531, ENSG00000213190, ENSG00000100442, ENSG00000109270, ENSG00000188730, ENSG00000128739, ENSG00000175854, ENSG00000173915, ENSG00000102981 
#> 	   ENSG00000127540, ENSG00000102804, ENSG00000261857, ENSG00000113387, ENSG00000160213, ENSG00000157470, ENSG00000172809, ENSG00000174871, ENSG00000240972, ENSG00000112304 
#> 	   ENSG00000109846, ENSG00000090263, ENSG00000214736, ENSG00000145335, ENSG00000160194, ENSG00000152954, ENSG00000204856, ENSG00000161970, ENSG00000272835, ENSG00000169249 
obj <- RunSeuratIntegration(obj)
#> INFO    [2025-04-23 18:07:21] Performing data integration ...
#> INFO    [2025-04-23 18:07:21] - Running IntegrateLayers (method = rpca) ...
#> Computing within dataset neighborhoods
#> Finding all pairwise anchors
#> Projecting new data onto SVD
#> Projecting new data onto SVD
#> Finding neighborhoods
#> Finding anchors
#> 	Found 261 anchors
#> Merging dataset 2 into 1
#> Extracting anchors for merged samples
#> Finding integration vectors
#> Finding integration vector weights
#> Integrating data
#> INFO    [2025-04-23 18:07:25] - Joining layers ...
obj <- RunSeuratDoubletDetection(obj, tool = "scDblFinder", filter = FALSE)
#> INFO    [2025-04-23 18:07:26] Running doublet detection using scDblFinder ...
#> Assuming the input to be a matrix of counts or expected counts.
#> Creating ~1500 artificial doublets...
#> Dimensional reduction
#> Evaluating kNN...
#> Training model...
#> iter=0, 24 cells excluded from training.
#> iter=1, 22 cells excluded from training.
#> iter=2, 27 cells excluded from training.
#> Threshold found:0.57
#> 14 (3.1%) doublets called
VizSeuratDoublets(obj)

VizSeuratDoublets(obj, plot_type = "pie")

# }