Remove ambient RNA from C133_Neeland data

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Last updated: 2024-03-14

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Knit directory: paed-inflammation-CITEseq/

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---

suppressPackageStartupMessages({
  library(here)
  library(BiocStyle)
  library(ggplot2)
  library(cowplot)
  library(patchwork)
  library(tidyverse)
  library(SingleCellExperiment)
  library(DropletUtils)
  library(scater)
  library(decontX)
  library(celda)
  library(dsb)
})

Load data

files <- list.files(here("data",
                         paste0("C133_Neeland_batch", 0:6),
                         "data",
                         "SCEs"),
                    pattern = "doublets_filtered",
                    full.names = TRUE)
               
sceLst <- sapply(files, function(fn){
  readRDS(file = fn)
})

sceLst

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch0/data/SCEs/C133_Neeland_batch0.doublets_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 33538 25635 
metadata(0):
assays(1): counts
rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
  ENSG00000268674
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(25635): 1_AAACCCAAGCTAGTTC-1 1_AAACCCACAGTCGCTG-1 ...
  4_TTTGTTGTCTAGTACG-1 4_TTTGTTGTCTCGAACA-1
colData names(43): Barcode Capture ... Fungi_type sample.id
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch1/data/SCEs/C133_Neeland_batch1.doublets_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 19522 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(19522): 1_AAACCCACACTTCCTG-1 1_AAACCCACAGACAAAT-1 ...
  2_TTTGTTGTCATTGGTG-1 2_TTTGTTGTCGATGGAG-1
colData names(56): Barcode Capture ... Fungi_type sample.id
reducedDimNames(0):
mainExpName: NULL
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch2/data/SCEs/C133_Neeland_batch2.doublets_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 22386 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(22386): 1_AAACCCAAGACTGTTC-1 1_AAACCCAAGATGATTG-1 ...
  2_TTTGTTGTCCAAGGGA-1 2_TTTGTTGTCCTTCTAA-1
colData names(56): Barcode Capture ... Fungi_type sample.id
reducedDimNames(0):
mainExpName: NULL
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch3/data/SCEs/C133_Neeland_batch3.doublets_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 46052 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(46052): 1_AAACCCAAGCAGCACA-1 1_AAACCCAAGCATCTTG-1 ...
  2_TTTGTTGTCTAGGCCG-1 2_TTTGTTGTCTCGGCTT-1
colData names(56): Barcode Capture ... Fungi_type sample.id
reducedDimNames(0):
mainExpName: NULL
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch4/data/SCEs/C133_Neeland_batch4.doublets_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 18858 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(18858): 1_AAACCCAAGGATTTGA-1 1_AAACCCAAGTCTCTGA-1 ...
  2_TTTGTTGCATGTGGCC-1 2_TTTGTTGGTCAACATC-1
colData names(56): Barcode Capture ... Fungi_type sample.id
reducedDimNames(0):
mainExpName: NULL
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch5/data/SCEs/C133_Neeland_batch5.doublets_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 32959 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(32959): 1_AAACCCAAGAAGATCT-1 1_AAACCCAAGGAGAGGC-1 ...
  2_TTTGTTGTCGGATTAC-1 2_TTTGTTGTCTGAGAGG-1
colData names(56): Barcode Capture ... Fungi_type sample.id
reducedDimNames(0):
mainExpName: NULL
altExpNames(2): HTO ADT

$`/Users/maksimovicjovana/Work/Projects/MCRI/melanie.neeland/paed-inflammation-CITEseq/data/C133_Neeland_batch6/data/SCEs/C133_Neeland_batch6.doublets_filtered.SCE.rds`
class: SingleCellExperiment 
dim: 36601 31275 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(31275): 1_AAACCCAAGAAGCGCT-1 1_AAACCCAAGACTCATC-1 ...
  2_TTTGTTGTCCCGAGTG-1 2_TTTGTTGTCGAGAATA-1
colData names(56): Barcode Capture ... Fungi_type sample.id
reducedDimNames(0):
mainExpName: NULL
altExpNames(2): HTO ADT

Remove ambient RNA contamination

Run decontX

During this step we will also denoise the ADT data for each batch using DSB as outlined in this workflow.

# identify isotype controls for DSB ADT normalisation
read_csv(file = here("data",
                     "C133_Neeland_batch1",
                     "data",
                     "sample_sheets",
                     "ADT_features.csv")) %>%
  dplyr::filter(grepl("[Ii]sotype", id)) %>%
  pull(name) -> isotype_controls

sceLst <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  
  sce_raw <- readRDS(str_replace(names(sceLst)[i], 
                     "doublets_filtered",
                     "CellRanger"))
  
  if(length(levels(sce$Capture)) < 4){
    sce_decont <- decontX(sce, background = sce_raw)
    
    # get isotype controls
    rowData(altExp(sce, "ADT")) %>%
      data.frame %>%
      dplyr::filter(grepl("[Ii]sotype", Symbol)) %>%
      pull(ID) -> isotype_controls

    # get ADT counts for "cells"
    adt <- counts(altExp(sce, "ADT"))
    # get ADT counts for background
    adt_background <- counts(altExp(sce_raw, "Antibody Capture"))
    # exclude all "cells" from background matrix
    adt_background <- adt_background[!str_detect(rownames(adt_background), "HTO"),]
    adt_background <- adt_background[,!colnames(adt_background) %in% colnames(adt)]
    # exclude droplets with >500 RNA counts
    adt_background <- adt_background[, colSums(adt_background) < 500]
    
    # normalize and denoise with dsb 
    adt_dsb <- DSBNormalizeProtein(
      cell_protein_matrix = adt, 
      empty_drop_matrix = adt_background, 
      denoise.counts = TRUE, 
      use.isotype.control = TRUE, 
      isotype.control.name.vec = isotype_controls)
    
    # add normalised dsb ADT assay
    tmp <- SingleCellExperiment(list(counts = adt_dsb),
                                     rowData = rowData(altExp(sce_decont, "ADT")))
    altExp(sce_decont, "ADT.dsb") <- tmp
      
  } else {
    sce_decont <- decontX(sce, background = sce_raw,
                          batch = sce$Capture,
                          bgBatch = sce_raw$Sample)
    
  }
  sce_decont
  
})

[1] "correcting ambient protein background noise"
[1] "some proteins with low background variance detected check raw and normalized distributions.  protein stats can be returned with return.stats = TRUE"
  [1] "A0006" "A0007" "A0020" "A0023" "A0024" "A0026" "A0029" "A0031" "A0032"
 [10] "A0033" "A0034" "A0046" "A0047" "A0050" "A0052" "A0053" "A0058" "A0063"
 [19] "A0064" "A0066" "A0070" "A0071" "A0072" "A0073" "A0081" "A0083" "A0085"
 [28] "A0087" "A0089" "A0090" "A0091" "A0092" "A0095" "A0100" "A0101" "A0124"
 [37] "A0127" "A0134" "A0138" "A0140" "A0141" "A0142" "A0143" "A0144" "A0145"
 [46] "A0146" "A0147" "A0149" "A0151" "A0152" "A0153" "A0154" "A0155" "A0156"
 [55] "A0159" "A0160" "A0161" "A0162" "A0165" "A0167" "A0168" "A0170" "A0171"
 [64] "A0172" "A0174" "A0176" "A0180" "A0181" "A0185" "A0187" "A0189" "A0206"
 [73] "A0214" "A0215" "A0216" "A0217" "A0218" "A0219" "A0224" "A0236" "A0237"
 [82] "A0238" "A0240" "A0241" "A0242" "A0246" "A0247" "A0352" "A0353" "A0355"
 [91] "A0357" "A0358" "A0359" "A0364" "A0367" "A0368" "A0369" "A0370" "A0371"
[100] "A0372" "A0383" "A0384" "A0385" "A0386" "A0389" "A0390" "A0391" "A0393"
[109] "A0396" "A0398" "A0404" "A0406" "A0407" "A0408" "A0419" "A0420" "A0446"
[118] "A0575" "A0576" "A0577" "A0579" "A0581" "A0582" "A0586" "A0590" "A0591"
[127] "A0599" "A0817" "A0822" "A0830" "A0845" "A0853" "A0861" "A0864" "A0866"
[136] "A0867" "A0868" "A0871" "A0872" "A0896" "A0902" "A0912" "A0920" "A0923"
[145] "A0931" "A0940" "A0941" "A0944" "A1018" "A1046"
[1] "fitting models to each cell for dsb technical component and removing cell to cell technical noise"
[1] "correcting ambient protein background noise"
[1] "some proteins with low background variance detected check raw and normalized distributions.  protein stats can be returned with return.stats = TRUE"
  [1] "A0006" "A0007" "A0020" "A0023" "A0024" "A0026" "A0029" "A0031" "A0032"
 [10] "A0033" "A0034" "A0046" "A0047" "A0050" "A0052" "A0053" "A0058" "A0063"
 [19] "A0064" "A0066" "A0070" "A0071" "A0072" "A0073" "A0081" "A0083" "A0085"
 [28] "A0087" "A0088" "A0089" "A0090" "A0091" "A0092" "A0095" "A0100" "A0101"
 [37] "A0124" "A0127" "A0134" "A0138" "A0140" "A0141" "A0142" "A0143" "A0144"
 [46] "A0145" "A0146" "A0147" "A0149" "A0151" "A0152" "A0153" "A0154" "A0155"
 [55] "A0156" "A0158" "A0159" "A0160" "A0161" "A0162" "A0163" "A0165" "A0167"
 [64] "A0168" "A0170" "A0171" "A0172" "A0174" "A0176" "A0179" "A0180" "A0181"
 [73] "A0185" "A0187" "A0189" "A0206" "A0214" "A0215" "A0216" "A0217" "A0218"
 [82] "A0219" "A0224" "A0236" "A0237" "A0238" "A0240" "A0241" "A0242" "A0246"
 [91] "A0247" "A0352" "A0353" "A0355" "A0357" "A0358" "A0359" "A0364" "A0367"
[100] "A0368" "A0369" "A0370" "A0371" "A0372" "A0373" "A0383" "A0384" "A0385"
[109] "A0386" "A0389" "A0390" "A0391" "A0393" "A0396" "A0398" "A0404" "A0406"
[118] "A0407" "A0408" "A0419" "A0420" "A0446" "A0447" "A0575" "A0576" "A0577"
[127] "A0579" "A0581" "A0582" "A0586" "A0590" "A0591" "A0599" "A0817" "A0822"
[136] "A0830" "A0845" "A0853" "A0861" "A0864" "A0866" "A0867" "A0868" "A0870"
[145] "A0871" "A0872" "A0894" "A0896" "A0897" "A0898" "A0902" "A0912" "A0920"
[154] "A0923" "A0931" "A0935" "A0940" "A0941" "A0944" "A1018" "A1046"
[1] "fitting models to each cell for dsb technical component and removing cell to cell technical noise"
[1] "correcting ambient protein background noise"
[1] "some proteins with low background variance detected check raw and normalized distributions.  protein stats can be returned with return.stats = TRUE"
  [1] "A0006" "A0007" "A0020" "A0023" "A0024" "A0026" "A0029" "A0031" "A0032"
 [10] "A0033" "A0034" "A0046" "A0047" "A0050" "A0052" "A0053" "A0058" "A0063"
 [19] "A0064" "A0066" "A0070" "A0071" "A0072" "A0073" "A0081" "A0083" "A0085"
 [28] "A0087" "A0088" "A0089" "A0090" "A0091" "A0092" "A0095" "A0100" "A0101"
 [37] "A0124" "A0127" "A0134" "A0136" "A0138" "A0140" "A0141" "A0142" "A0143"
 [46] "A0144" "A0145" "A0146" "A0147" "A0149" "A0151" "A0152" "A0153" "A0154"
 [55] "A0155" "A0156" "A0158" "A0159" "A0160" "A0161" "A0162" "A0163" "A0165"
 [64] "A0167" "A0168" "A0170" "A0171" "A0172" "A0174" "A0176" "A0179" "A0180"
 [73] "A0181" "A0185" "A0187" "A0189" "A0206" "A0214" "A0215" "A0216" "A0217"
 [82] "A0218" "A0219" "A0224" "A0236" "A0237" "A0238" "A0240" "A0241" "A0242"
 [91] "A0246" "A0247" "A0352" "A0353" "A0355" "A0357" "A0358" "A0359" "A0364"
[100] "A0367" "A0368" "A0369" "A0370" "A0371" "A0372" "A0373" "A0383" "A0384"
[109] "A0385" "A0386" "A0389" "A0390" "A0391" "A0393" "A0394" "A0396" "A0398"
[118] "A0404" "A0406" "A0407" "A0408" "A0419" "A0420" "A0446" "A0447" "A0575"
[127] "A0576" "A0577" "A0579" "A0581" "A0582" "A0586" "A0590" "A0591" "A0599"
[136] "A0817" "A0822" "A0830" "A0845" "A0853" "A0861" "A0864" "A0866" "A0867"
[145] "A0868" "A0870" "A0871" "A0872" "A0894" "A0896" "A0897" "A0898" "A0902"
[154] "A0912" "A0920" "A0923" "A0931" "A0935" "A0940" "A0941" "A0944" "A1018"
[163] "A1046"
[1] "fitting models to each cell for dsb technical component and removing cell to cell technical noise"
[1] "correcting ambient protein background noise"
[1] "some proteins with low background variance detected check raw and normalized distributions.  protein stats can be returned with return.stats = TRUE"
  [1] "A0006" "A0007" "A0020" "A0023" "A0024" "A0026" "A0029" "A0031" "A0032"
 [10] "A0033" "A0034" "A0046" "A0047" "A0050" "A0052" "A0053" "A0058" "A0063"
 [19] "A0064" "A0066" "A0070" "A0071" "A0072" "A0073" "A0081" "A0083" "A0085"
 [28] "A0087" "A0088" "A0089" "A0090" "A0091" "A0092" "A0095" "A0100" "A0101"
 [37] "A0124" "A0127" "A0134" "A0136" "A0138" "A0140" "A0141" "A0142" "A0143"
 [46] "A0144" "A0145" "A0146" "A0147" "A0149" "A0151" "A0152" "A0153" "A0154"
 [55] "A0155" "A0156" "A0158" "A0159" "A0160" "A0161" "A0162" "A0163" "A0165"
 [64] "A0167" "A0168" "A0170" "A0171" "A0172" "A0174" "A0176" "A0179" "A0180"
 [73] "A0181" "A0185" "A0187" "A0189" "A0206" "A0214" "A0215" "A0216" "A0217"
 [82] "A0218" "A0219" "A0224" "A0236" "A0237" "A0238" "A0240" "A0241" "A0242"
 [91] "A0246" "A0247" "A0352" "A0353" "A0355" "A0357" "A0358" "A0359" "A0364"
[100] "A0367" "A0368" "A0369" "A0370" "A0371" "A0372" "A0373" "A0383" "A0384"
[109] "A0385" "A0386" "A0389" "A0390" "A0391" "A0393" "A0394" "A0396" "A0398"
[118] "A0404" "A0406" "A0407" "A0408" "A0419" "A0420" "A0446" "A0447" "A0575"
[127] "A0576" "A0577" "A0579" "A0581" "A0582" "A0586" "A0590" "A0591" "A0599"
[136] "A0817" "A0822" "A0830" "A0845" "A0853" "A0861" "A0864" "A0866" "A0867"
[145] "A0868" "A0870" "A0871" "A0872" "A0894" "A0896" "A0897" "A0898" "A0902"
[154] "A0912" "A0920" "A0923" "A0931" "A0935" "A0940" "A0941" "A0944" "A1018"
[163] "A1046"
[1] "fitting models to each cell for dsb technical component and removing cell to cell technical noise"
[1] "correcting ambient protein background noise"
[1] "some proteins with low background variance detected check raw and normalized distributions.  protein stats can be returned with return.stats = TRUE"
  [1] "A0006" "A0007" "A0020" "A0023" "A0024" "A0026" "A0029" "A0031" "A0032"
 [10] "A0033" "A0034" "A0046" "A0047" "A0050" "A0052" "A0053" "A0058" "A0063"
 [19] "A0064" "A0066" "A0070" "A0071" "A0072" "A0073" "A0081" "A0083" "A0085"
 [28] "A0087" "A0088" "A0089" "A0090" "A0091" "A0092" "A0095" "A0100" "A0101"
 [37] "A0124" "A0127" "A0134" "A0136" "A0138" "A0140" "A0141" "A0142" "A0143"
 [46] "A0144" "A0145" "A0146" "A0147" "A0149" "A0151" "A0152" "A0153" "A0154"
 [55] "A0155" "A0156" "A0158" "A0159" "A0160" "A0161" "A0162" "A0163" "A0165"
 [64] "A0167" "A0168" "A0170" "A0171" "A0172" "A0174" "A0176" "A0179" "A0180"
 [73] "A0181" "A0185" "A0187" "A0189" "A0206" "A0214" "A0215" "A0216" "A0217"
 [82] "A0218" "A0219" "A0224" "A0236" "A0237" "A0238" "A0240" "A0241" "A0242"
 [91] "A0246" "A0247" "A0352" "A0353" "A0355" "A0357" "A0358" "A0359" "A0364"
[100] "A0367" "A0368" "A0369" "A0370" "A0371" "A0372" "A0373" "A0383" "A0384"
[109] "A0385" "A0386" "A0389" "A0390" "A0391" "A0393" "A0394" "A0396" "A0398"
[118] "A0404" "A0406" "A0407" "A0408" "A0419" "A0420" "A0446" "A0447" "A0575"
[127] "A0576" "A0577" "A0579" "A0581" "A0582" "A0586" "A0590" "A0591" "A0599"
[136] "A0817" "A0822" "A0830" "A0845" "A0853" "A0861" "A0864" "A0866" "A0867"
[145] "A0868" "A0870" "A0871" "A0872" "A0894" "A0896" "A0897" "A0898" "A0902"
[154] "A0912" "A0920" "A0923" "A0931" "A0935" "A0940" "A0941" "A0944" "A1018"
[163] "A1046"
[1] "fitting models to each cell for dsb technical component and removing cell to cell technical noise"
[1] "correcting ambient protein background noise"
[1] "some proteins with low background variance detected check raw and normalized distributions.  protein stats can be returned with return.stats = TRUE"
  [1] "A0006" "A0007" "A0020" "A0023" "A0024" "A0026" "A0029" "A0031" "A0032"
 [10] "A0033" "A0034" "A0046" "A0047" "A0050" "A0052" "A0053" "A0058" "A0063"
 [19] "A0064" "A0066" "A0070" "A0071" "A0072" "A0073" "A0081" "A0083" "A0085"
 [28] "A0087" "A0088" "A0089" "A0090" "A0091" "A0092" "A0095" "A0100" "A0101"
 [37] "A0124" "A0127" "A0134" "A0136" "A0138" "A0140" "A0141" "A0142" "A0143"
 [46] "A0144" "A0145" "A0146" "A0147" "A0149" "A0151" "A0152" "A0153" "A0154"
 [55] "A0155" "A0156" "A0158" "A0159" "A0160" "A0161" "A0162" "A0163" "A0165"
 [64] "A0167" "A0168" "A0170" "A0171" "A0172" "A0174" "A0176" "A0179" "A0180"
 [73] "A0181" "A0185" "A0187" "A0189" "A0206" "A0214" "A0215" "A0216" "A0217"
 [82] "A0218" "A0219" "A0224" "A0236" "A0237" "A0238" "A0240" "A0241" "A0242"
 [91] "A0246" "A0247" "A0352" "A0353" "A0355" "A0357" "A0358" "A0359" "A0364"
[100] "A0367" "A0368" "A0369" "A0370" "A0371" "A0372" "A0373" "A0383" "A0384"
[109] "A0385" "A0386" "A0389" "A0390" "A0391" "A0393" "A0394" "A0396" "A0398"
[118] "A0404" "A0406" "A0407" "A0408" "A0419" "A0420" "A0446" "A0447" "A0575"
[127] "A0576" "A0577" "A0579" "A0581" "A0582" "A0586" "A0590" "A0591" "A0599"
[136] "A0817" "A0822" "A0830" "A0845" "A0853" "A0861" "A0864" "A0866" "A0867"
[145] "A0868" "A0870" "A0871" "A0872" "A0894" "A0896" "A0897" "A0898" "A0902"
[154] "A0912" "A0920" "A0923" "A0931" "A0935" "A0940" "A0941" "A0944" "A1018"
[163] "A1046"
[1] "fitting models to each cell for dsb technical component and removing cell to cell technical noise"

DecontX clusters

p <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  
  if(length(levels(sce$Capture)) < 4){
    umap <- reducedDim(sce, glue::glue("decontX_UMAP"))
    plotDimReduceCluster(x = sce$decontX_clusters,
                         dim1 = umap[, 1], dim2 = umap[, 2])
    
  } else {
    capture_names <- levels(sce$Capture)
    
    p <- lapply(capture_names, function(cn){
      umap <- reducedDim(sce, glue::glue("decontX_{cn}_UMAP"))
      plotDimReduceCluster(x = sce$decontX_clusters,
                           dim1 = umap[, 1], dim2 = umap[, 2])
    })
    wrap_plots(p, ncol = 2)
    
  }
  
})

p

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

DecontX contamination

p <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  
  if(length(levels(sce$Capture)) < 4){  
    plotDecontXContamination(sce)
    
  } else {
    capture_names <- levels(sce$Capture)
    
    p <- lapply(capture_names, function(cn){
      plotDecontXContamination(sce, batch = cn)
    })
    
    wrap_plots(p, ncol = 2, guides = "collect") &
      theme(legend.position = "bottom",
            axis.title = element_text(size = 10),
            axis.text = element_text(size = 8))
    
  }
})

p

[[1]]

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

[[2]]

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

[[7]]

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

Main cell type markers (before decontX)

p <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  
  if(length(levels(sce$Capture)) < 4){
    sce_decont <- logNormCounts(sce)
    rownames(sce_decont) <- rowData(sce_decont)$Symbol
    
    umap <- reducedDim(sce_decont, glue::glue("decontX_UMAP"))
    plotDimReduceFeature(as.matrix(logcounts(sce_decont)),
                         dim1 = umap[, 1],
                         dim2 = umap[, 2],
                         features = c("CD3D", "CD3E", # T-cells
                                      "ITGAM", "CD14", # Macs
                                      "CD79A", "MS4A1", # B-cells
                                      "EPCAM", "CDH1"), # Epithelial
                         exactMatch = TRUE,
                         ncol = 2)
    
  } else {
    sce_decont <- logNormCounts(sce)
    rownames(sce_decont) <- rowData(sce_decont)$Symbol
    capture_names <- levels(sce$Capture)
    
    p <- lapply(capture_names, function(cn){
      umap <- reducedDim(sce_decont, glue::glue("decontX_{cn}_UMAP"))
      plotDimReduceFeature(as.matrix(logcounts(sce_decont)),
                           dim1 = umap[, 1],
                           dim2 = umap[, 2],
                           features = c("CD3D", "CD3E", # T-cells
                                        "ITGAM", "CD14", # Macs
                                        "CD79A", "MS4A1", # B-cells
                                        "EPCAM", "CDH1"), # Epithelial
                           exactMatch = TRUE,
                           ncol = 2)
    })
    
    wrap_plots(p, ncol = 2, guides = "collect") &
      theme(legend.position = "bottom",
            axis.title = element_text(size = 10),
            axis.text = element_text(size = 8))
    
  }
})

p

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

[[7]]

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

Main cell type markers (after decontX)

p <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  
  if(length(levels(sce$Capture)) < 4){
    sce_decont <- logNormCounts(sce, assay.type = "decontXcounts")
    rownames(sce_decont) <- rowData(sce_decont)$Symbol
    
    umap <- reducedDim(sce_decont, glue::glue("decontX_UMAP"))
    plotDimReduceFeature(as.matrix(logcounts(sce_decont)),
                         dim1 = umap[, 1],
                         dim2 = umap[, 2],
                         features = c("CD3D", "CD3E", # T-cells
                                      "ITGAM", "CD14", # Macs
                                      "CD79A", "MS4A1", # B-cells
                                      "EPCAM", "CDH1"), # Epithelial
                         exactMatch = TRUE,
                         ncol = 2)
    
  } else {
    sce_decont <- logNormCounts(sce, assay.type = "decontXcounts")
    rownames(sce_decont) <- rowData(sce_decont)$Symbol
    capture_names <- levels(sce_decont$Capture)
    
    p <- lapply(capture_names, function(cn){
      umap <- reducedDim(sce_decont, glue::glue("decontX_{cn}_UMAP"))
      plotDimReduceFeature(as.matrix(logcounts(sce_decont)),
                           dim1 = umap[, 1],
                           dim2 = umap[, 2],
                           features = c("CD3D", "CD3E", # T-cells
                                        "ITGAM", "CD14", # Macs
                                        "CD79A", "MS4A1", # B-cells
                                        "EPCAM", "CDH1"), # Epithelial
                           exactMatch = TRUE,
                           ncol = 2)
    })
    
    wrap_plots(p, ncol = 2, guides = "collect") &
      theme(legend.position = "bottom",
            axis.title = element_text(size = 10),
            axis.text = element_text(size = 8))
    
  }
})

p

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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8abff7b	Jovana Maksimovic	2024-02-28

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Version	Author	Date
8abff7b	Jovana Maksimovic	2024-02-28

Save data

batches <- str_extract(files, "batch[0-6]")

sapply(1:length(sceLst), function(i){
  out <- here("data",
              paste0("C133_Neeland_", batches[i]),
              "data", 
              "SCEs", 
              glue::glue("C133_Neeland_{batches[i]}.ambient_removed.SCE.rds"))
  if(!file.exists(out)) saveRDS(sceLst[[i]], out)
  fs::file_chmod(out, "664")
  if(any(str_detect(fs::group_ids()$group_name, 
                    "oshlack_lab"))) fs::file_chown(out, 
                                                    group_id = "oshlack_lab")
})

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Session Info

Session information

sessionInfo()

R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: Australia/Melbourne
tzcode source: internal

attached base packages:
[1] stats4    stats     graphics  grDevices datasets  utils     methods  
[8] base     

other attached packages:
 [1] dsb_1.0.3                   celda_1.18.1               
 [3] Matrix_1.6-5                decontX_1.0.0              
 [5] scater_1.30.1               scuttle_1.12.0             
 [7] DropletUtils_1.22.0         SingleCellExperiment_1.24.0
 [9] SummarizedExperiment_1.32.0 Biobase_2.62.0             
[11] GenomicRanges_1.54.1        GenomeInfoDb_1.38.6        
[13] IRanges_2.36.0              S4Vectors_0.40.2           
[15] BiocGenerics_0.48.1         MatrixGenerics_1.14.0      
[17] matrixStats_1.2.0           lubridate_1.9.3            
[19] forcats_1.0.0               stringr_1.5.1              
[21] dplyr_1.1.4                 purrr_1.0.2                
[23] readr_2.1.5                 tidyr_1.3.1                
[25] tibble_3.2.1                tidyverse_2.0.0            
[27] patchwork_1.2.0             cowplot_1.1.3              
[29] ggplot2_3.5.0               BiocStyle_2.30.0           
[31] here_1.0.1                  workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.3                  spatstat.sparse_3.0-3    
  [3] bitops_1.0-7              httr_1.4.7               
  [5] RColorBrewer_1.1-3        doParallel_1.0.17        
  [7] tools_4.3.2               sctransform_0.4.1        
  [9] utf8_1.2.4                R6_2.5.1                 
 [11] HDF5Array_1.30.0          lazyeval_0.2.2           
 [13] uwot_0.1.16               rhdf5filters_1.14.1      
 [15] withr_3.0.0               sp_2.1-3                 
 [17] gridExtra_2.3             progressr_0.14.0         
 [19] cli_3.6.2                 spatstat.explore_3.2-6   
 [21] enrichR_3.2               labeling_0.4.3           
 [23] sass_0.4.8                Seurat_4.4.0             
 [25] spatstat.data_3.0-4       ggridges_0.5.6           
 [27] pbapply_1.7-2             QuickJSR_1.1.3           
 [29] StanHeaders_2.32.5        dbscan_1.1-12            
 [31] R.utils_2.12.3            parallelly_1.37.0        
 [33] WriteXLS_6.5.0            limma_3.58.1             
 [35] rstudioapi_0.15.0         FNN_1.1.4                
 [37] generics_0.1.3            combinat_0.0-8           
 [39] vroom_1.6.5               ica_1.0-3                
 [41] spatstat.random_3.2-2     inline_0.3.19            
 [43] loo_2.6.0                 ggbeeswarm_0.7.2         
 [45] fansi_1.0.6               abind_1.4-5              
 [47] R.methodsS3_1.8.2         lifecycle_1.0.4          
 [49] whisker_0.4.1             yaml_2.3.8               
 [51] edgeR_4.0.15              rhdf5_2.46.1             
 [53] SparseArray_1.2.4         Rtsne_0.17               
 [55] grid_4.3.2                promises_1.2.1           
 [57] dqrng_0.3.2               crayon_1.5.2             
 [59] miniUI_0.1.1.1            lattice_0.22-5           
 [61] beachmat_2.18.1           pillar_1.9.0             
 [63] knitr_1.45                rjson_0.2.21             
 [65] future.apply_1.11.1       codetools_0.2-19         
 [67] leiden_0.4.3.1            glue_1.7.0               
 [69] getPass_0.2-4             data.table_1.15.0        
 [71] vctrs_0.6.5               png_0.1-8                
 [73] gtable_0.3.4              cachem_1.0.8             
 [75] xfun_0.42                 S4Arrays_1.2.0           
 [77] mime_0.12                 RcppEigen_0.3.3.9.4      
 [79] survival_3.5-8            iterators_1.0.14         
 [81] statmod_1.5.0             ellipsis_0.3.2           
 [83] fitdistrplus_1.1-11       ROCR_1.0-11              
 [85] nlme_3.1-164              bit64_4.0.5              
 [87] RcppAnnoy_0.0.22          rstan_2.32.5             
 [89] rprojroot_2.0.4           bslib_0.6.1              
 [91] irlba_2.3.5.1             vipor_0.4.7              
 [93] KernSmooth_2.23-22        colorspace_2.1-0         
 [95] tidyselect_1.2.0          processx_3.8.3           
 [97] bit_4.0.5                 curl_5.2.0               
 [99] compiler_4.3.2            git2r_0.33.0             
[101] BiocNeighbors_1.20.2      DelayedArray_0.28.0      
[103] plotly_4.10.4             scales_1.3.0             
[105] lmtest_0.9-40             callr_3.7.3              
[107] digest_0.6.34             goftest_1.2-3            
[109] spatstat.utils_3.0-4      rmarkdown_2.25           
[111] XVector_0.42.0            htmltools_0.5.7          
[113] pkgconfig_2.0.3           sparseMatrixStats_1.14.0 
[115] highr_0.10                fastmap_1.1.1            
[117] rlang_1.1.3               htmlwidgets_1.6.4        
[119] shiny_1.8.0               DelayedMatrixStats_1.24.0
[121] farver_2.1.1              jquerylib_0.1.4          
[123] zoo_1.8-12                jsonlite_1.8.8           
[125] mclust_6.1                BiocParallel_1.36.0      
[127] R.oo_1.26.0               BiocSingular_1.18.0      
[129] RCurl_1.98-1.14           magrittr_2.0.3           
[131] GenomeInfoDbData_1.2.11   Rhdf5lib_1.24.2          
[133] munsell_0.5.0             Rcpp_1.0.12              
[135] viridis_0.6.5             reticulate_1.35.0        
[137] stringi_1.8.3             MCMCprecision_0.4.0      
[139] zlibbioc_1.48.0           MASS_7.3-60.0.1          
[141] plyr_1.8.9                pkgbuild_1.4.3           
[143] parallel_4.3.2            listenv_0.9.1            
[145] ggrepel_0.9.5             deldir_2.0-2             
[147] splines_4.3.2             tensor_1.5               
[149] hms_1.1.3                 locfit_1.5-9.8           
[151] ps_1.7.6                  igraph_2.0.1.1           
[153] spatstat.geom_3.2-8       reshape2_1.4.4           
[155] ScaledMatrix_1.10.0       rstantools_2.4.0         
[157] evaluate_0.23             SeuratObject_4.1.4       
[159] RcppParallel_5.1.7        renv_1.0.3               
[161] BiocManager_1.30.22       tzdb_0.4.0               
[163] foreach_1.5.2             httpuv_1.6.14            
[165] RANN_2.6.1                polyclip_1.10-6          
[167] future_1.33.1             scattermore_1.2          
[169] rsvd_1.0.5                xtable_1.8-4             
[171] later_1.3.2               viridisLite_0.4.2        
[173] beeswarm_0.4.0            cluster_2.1.6            
[175] timechange_0.3.0          globals_0.16.2