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Load libraries

suppressPackageStartupMessages({
  library(BiocStyle)
  library(tidyverse)
  library(here)
  library(glue)
  library(patchwork)
  library(paletteer)
  library(readxl)
  library(ggupset)
  library(stringr)
})

Load Data

files <- list.files(here("data",
                         paste0("C133_Neeland_batch", 0:6),
                         "data", 
                         "SCEs"), 
                      pattern = "doublets_called",
                      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_called.SCE.rds`
class: SingleCellExperiment 
dim: 33538 26939 
metadata(0):
assays(1): counts
rownames(33538): ENSG00000243485 ENSG00000237613 ... ENSG00000277475
  ENSG00000268674
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(26939): 1_AAACCCAAGCTAGTTC-1 1_AAACCCACAGTCGCTG-1 ...
  4_TTTGTTGTCTAGTACG-1 4_TTTGTTGTCTCGAACA-1
colData names(25): Barcode Capture ... scDblFinder.cxds_score batch
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_called.SCE.rds`
class: SingleCellExperiment 
dim: 36601 21970 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(21970): 1_AAACCCACACTTCCTG-1 1_AAACCCACAGACAAAT-1 ...
  2_TTTGTTGTCATTGGTG-1 2_TTTGTTGTCGATGGAG-1
colData names(32): Barcode Capture ... scDblFinder.cxds_score batch
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_called.SCE.rds`
class: SingleCellExperiment 
dim: 36601 45711 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(45711): 1_AAACCCAAGACCTGGA-1 1_AAACCCAAGACTGTTC-1 ...
  2_TTTGTTGTCTCATGGA-1 2_TTTGTTGTCTCCAAGA-1
colData names(32): Barcode Capture ... scDblFinder.cxds_score batch
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_called.SCE.rds`
class: SingleCellExperiment 
dim: 36601 58038 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(58038): 1_AAACCCAAGCAGCACA-1 1_AAACCCAAGCATCTTG-1 ...
  2_TTTGTTGTCTAGGCCG-1 2_TTTGTTGTCTCGGCTT-1
colData names(32): Barcode Capture ... scDblFinder.cxds_score batch
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_called.SCE.rds`
class: SingleCellExperiment 
dim: 36601 45760 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(45760): 1_AAACCCAAGCGTTAGG-1 1_AAACCCAAGGATTTGA-1 ...
  2_TTTGTTGTCGACGATT-1 2_TTTGTTGTCTAGGCCG-1
colData names(32): Barcode Capture ... scDblFinder.cxds_score batch
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_called.SCE.rds`
class: SingleCellExperiment 
dim: 36601 44715 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(44715): 1_AAACCCAAGAAGATCT-1 1_AAACCCAAGATGCAGC-1 ...
  2_TTTGTTGTCGGATTAC-1 2_TTTGTTGTCTGAGAGG-1
colData names(32): Barcode Capture ... scDblFinder.cxds_score batch
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_called.SCE.rds`
class: SingleCellExperiment 
dim: 36601 46485 
metadata(0):
assays(1): counts
rownames(36601): ENSG00000243485 ENSG00000237613 ... ENSG00000278817
  ENSG00000277196
rowData names(20): ID Symbol ... is_mito is_pseudogene
colnames(46485): 1_AAACCCAAGAAGCGCT-1 1_AAACCCAAGACTCATC-1 ...
  2_TTTGTTGTCGAGAATA-1 2_TTTGTTGTCTACTGAG-1
colData names(32): Barcode Capture ... scDblFinder.cxds_score batch
reducedDimNames(0):
mainExpName: NULL
altExpNames(2): HTO ADT

Load sample information

ext_metadata_df <- read_excel(
  here("data/CZI_samples_design_with_micro.xlsx"),
  col_types = 
    c("text", "numeric", "text", "text", "text", "numeric", "numeric", "text",
      "numeric", "text", "numeric", "numeric", "numeric", "numeric",
      "text", "text", "text")) %>%
  mutate(HASHTAG = paste0("Human_HTO_", HASHTAG)) %>%
  mutate(sample.id = ave(Donor_ID, Donor_ID, 
                         FUN = function(i) paste0("sample_", i, '.', seq_along(i)))) %>%
  dplyr::rename(Batch = BATCH, 
                Donor = Donor_ID, 
                Hashtag = HASHTAG,
                arest.id = AREST_ID) %>%
  mutate(Donor = as.factor(Donor))
knitr::kable(ext_metadata_df)
arest.id Donor Disease Treatment Severity Experiment Age Sex Batch Hashtag Micobiology_done Bacteria_detected Virus_detected Fungi_detected Bacteria_type Virus_type Fungi_type sample.id
M1C141 25 CF untreated Severe 2 4.9100000 F 1 Human_HTO_3 1 1 0 1 Haemophilus parainfluenzae NA Aspergillus fumigatus sample_25.1
M1C156 26 CF untreated mild 2 5.0493151 M 1 Human_HTO_6 1 1 0 0 S. aureus NA NA sample_26.1
M1C130 27 CF treated (ivacaftor) mild 2 4.9178082 M 1 Human_HTO_7 1 0 0 0 NA NA NA sample_27.1
M1C122 29 CF untreated severe 2 5.9890411 F 1 Human_HTO_1 1 1 1 0 S. aureus, Stenotrophomonas, E.coli, Haemophilus influenzae Parainfluenza virus 2 NA sample_29.1
M1C118 30 CF untreated severe 2 6.0164384 F 1 Human_HTO_2 1 1 0 0 Mycobacterium Avium, Haemophilus parainfluenzae NA NA sample_30.1
M1C125 31 CF untreated mild 2 5.8904110 F 1 Human_HTO_4 1 0 0 0 NA NA NA sample_31.1
M1C1265 32 CF untreated mild 2 5.9260274 F 1 Human_HTO_5 1 0 0 0 NA NA NA sample_32.1
M1C129 33 CF treated (ivacaftor) severe 2 5.9506849 M 1 Human_HTO_8 1 1 0 0 S. aureus, Haemophilus parainfluenzae, Stenotrophomonas NA NA sample_33.1
M1N050 5 Healthy Healthy Healthy 3 8.3972603 F 2 Human_HTO_7 0 NA NA NA NA NA NA sample_5.1
M1N057 10 Wheeze Wheeze Wheeze 3 3.6657534 M 2 Human_HTO_6 1 1 0 0 Haemophilus influenzae NA NA sample_10.1
M1C161.1 37 CF untreated mild 3 2.9479452 F 2 Human_HTO_9 1 1 0 0 Haemophilus influenzae NA NA sample_37.1
M1C161.2 37 CF treated (orkambi) mild 3 4.5726027 F 2 Human_HTO_10 1 1 0 0 Haemphilus parainfluenzae, Haemophilus Parahemolyticus NA NA sample_37.2
M1C161.3 37 CF treated (orkambi) mild 3 4.9178082 F 2 Human_HTO_12 1 1 1 0 S. aureus, Haemophilus parainfluenza Rhinovirus, Enterovirus NA sample_37.3
M1C159.1 38 CF treated (ivacaftor) mild 3 2.9698630 M 2 Human_HTO_13 1 1 0 0 Haemophilus parainfluenzae, Haemophilus parahaemolyticus, Haemophilus haemolyticus NA NA sample_38.1
M1C159.2 38 CF treated (ivacaftor) mild 3 4.0575342 M 2 Human_HTO_14 1 1 1 0 Haemophilus influenzae Enterovirus, Human metapneumovirus NA sample_38.2
M1C159.3 38 CF treated (ivacaftor) mild 3 5.0931507 M 2 Human_HTO_15 1 1 0 0 Haemophilus Haemolyticus, Haemophilus parainfluenza NA NA sample_38.3
M1N047 1 Healthy Healthy Healthy 3 0.8356164 M 3 Human_HTO_6 0 NA NA NA NA NA NA sample_1.1
M1N063 2 Healthy Healthy Healthy 3 1.1068493 M 3 Human_HTO_7 1 1 0 0 moraxella catarrhalis NA NA sample_2.1
M1N048 6 Healthy Healthy Healthy 3 5.3534247 M 3 Human_HTO_14 1 1 0 0 S. aureus NA NA sample_6.1
M1N062 7 Healthy Healthy Healthy 3 4.7095890 F 3 Human_HTO_15 1 0 0 0 NA NA NA sample_7.1
M1C178 15 CF untreated mild 3 0.9369863 M 3 Human_HTO_12 1 1 0 1 Enterobacter cloacae, Haemophilus parainfluenzae NA Candida sample_15.1
M1C179 16 CF untreated mild 3 0.9232877 F 3 Human_HTO_13 1 0 0 1 NA NA Aspergillus fumigatus sample_16.1
M1C168 21 CF untreated severe 3 2.9780822 F 3 Human_HTO_9 1 1 0 0 Haemophilus influenzae, MRSA NA NA sample_21.1
M1C164 22 CF treated (ivacaftor) mild 3 3.0301370 M 3 Human_HTO_10 1 1 0 0 S. aureus, Haemophilus haemolyticus NA NA sample_22.1
M1N053 8 Wheeze Wheeze Wheeze 3 1.0438356 M 4 Human_HTO_7 1 1 1 0 Haemophilus influenzae, Haemophilus parainfluenzae RSV NA sample_8.1
M1N061 9 Wheeze Wheeze Wheeze 3 0.9698630 M 4 Human_HTO_9 1 0 1 0 NA Rhinovirus, Adenovirus NA sample_9.1
M1N059 12 CSLD CSLD CSLD 3 3.8739726 F 4 Human_HTO_6 1 0 0 0 NA NA NA sample_12.1
M1N056 13 CSLD CSLD CSLD 3 5.9287671 F 4 Human_HTO_10 1 1 0 1 S. aureus, Haemophilus parainfluenzae NA Aspergillus fumigatus sample_13.1
M1C181 17 CF untreated mild 3 0.9698630 F 4 Human_HTO_12 1 1 1 0 Burkholderia cenocepacia Parainfluenza virus 3, RSV NA sample_17.1
M1C193 18 CF untreated mild 3 1.1041096 M 4 Human_HTO_13 1 1 1 0 Klebsiella Aerogens, Haemophilus parainfluenzae Rhnovirus, Enterovirus NA sample_18.1
M1C152.1 39 CF untreated mild 3 3.9260274 F 4 Human_HTO_14 1 1 0 0 Streptococcus pneumoniae, Moraxella catarrhalis , Haemophilus parainfluenzae, Haemophilus influenzae NA NA sample_39.1
M1C152.2 39 CF treated (orkambi) mild 3 6.1890411 F 4 Human_HTO_15 1 1 1 0 Streptococcus pneumoniae, Haemophilus influenzae Rhinovirus NA sample_39.2
M1N055 3 Healthy Healthy Healthy 3 1.0958904 M 5 Human_HTO_6 1 1 1 0 Haemophilus influenzae, Moraxella catarrhalis Rhinovirus NA sample_3.1
M1N065 11 Wheeze Wheeze Wheeze 3 5.9013699 M 5 Human_HTO_12 1 0 0 0 NA NA NA sample_11.1
M1N054 14 CSLD CSLD CSLD 3 7.4602740 F 5 Human_HTO_7 1 1 0 0 Haemophilus influenzae NA NA sample_14.1
M1C177 19 CF untreated mild 3 0.9506849 F 5 Human_HTO_9 1 0 0 0 NA NA NA sample_19.1
M1C188 20 CF untreated severe 3 1.4739726 M 5 Human_HTO_10 1 1 0 0 Klebsiella Oxytoca, Staph Aureus (Methicillin Resistant) NA NA sample_20.1
M1C158 23 CF untreated severe 3 3.9232877 F 5 Human_HTO_13 1 1 1 0 MRSA Influenza A NA sample_23.1
M1C155 24 CF treated (ivacaftor) mild 3 5.2356164 M 5 Human_HTO_14 1 0 0 0 NA NA NA sample_24.1
M1C123 28 CF untreated mild 3 5.9753425 F 5 Human_HTO_15 1 1 1 NA Pseudomonas aeruginosa Influenza A NA sample_28.1
M1N051 4 Healthy Healthy Healthy 3 1.2246575 M 6 Human_HTO_6 1 1 1 0 Haemophilus influenzae RSV NA sample_4.1
M1C184.1 34 CF untreated mild 3 0.5232877 F 6 Human_HTO_7 1 0 1 0 NA Influenza A, RSV NA sample_34.1
M1C184.2 34 CF untreated mild 3 1.0657534 F 6 Human_HTO_9 1 1 0 0 Haemophilus parainfluenzae NA NA sample_34.2
M1C184.3 34 CF untreated mild 3 2.0575342 F 6 Human_HTO_10 1 1 0 0 Psuedomonas aeuroginosa, Haemophilus parainfluenzae, haemophilus parahaemolyticus NA NA sample_34.3
M1C183.1 35 CF untreated mild 3 1.0575342 M 6 Human_HTO_12 1 1 1 0 Serratia marcescens, Haemophilus parainfluenzae, Bordetella pertussis Rhinovirus, Adenovirus NA sample_35.1
M1C183.2 35 CF untreated mild 3 1.9917808 M 6 Human_HTO_13 1 1 1 0 Serratia Marcesens, Pseudomonas Aeruginosa, Haemophilus parainfluenzae Rhinovirus, Enterovirus NA sample_35.2
M1C185.1 36 CF untreated severe 3 0.9616438 F 6 Human_HTO_14 1 0 1 0 NA RSV NA sample_36.1
M1C185.2 36 CF treated (orkambi) severe 3 2.2602740 F 6 Human_HTO_15 1 1 0 1 S. aureus, Haemophilus parainfluenzae NA Candida sample_36.2
M1C160  40 CF untreated severe 1 2.9900000 M 0 Human_HTO_A 1 1 1 1 Stenotrophomonas maltophilia, Enterobacter cloacae, Burkholderia gladioli. RSV Cadida, Aspergillus sample_40.1
M1C162  41 CF untreated mild 1 2.9900000 M 0 Human_HTO_B 1 1 0 0 Bortadella Pertussis NA NA sample_41.1
M1C163  42 CF treated (ivacaftor) severe 1 3.0300000 M 0 Human_HTO_D 1 0 0 0 NA NA NA sample_42.1
M1N052 43 Healthy Healthy Healthy 1 3.0000000 M 0 Human_HTO_C 1 1 0 0 S. aureus, Haemophilus parainfluenzae, Haemophilus Hemolyticus, Haemophilus Parahemolyticus NA NA sample_43.1

Assign sample IDs to cells

For genetically distinct samples, cells are assigned to a sample using the genetic assignment; the sample information is then assigned using the corresponding HTO. For longitudinal samples only, both cells and sample information are assigned to the individual samples using HTOs.

sceLst <- sapply(1:length(sceLst), function(i){
  message(i)
  sce <- sceLst[[i]]
  
  # convert genetic and HTO labels to character
  sce$GeneticDonor <- as.character(sce$GeneticDonor)
  sce$dmmHTO <- ifelse(!stringr::str_detect(sce$dmmHTO, "Human_HTO_") & 
                         !(stringr::str_detect(sce$dmmHTO, "Doublet|Negative")),
                       paste0("Human_HTO_", sce$dmmHTO),
                       as.character(sce$dmmHTO))
  
  # match genetics and HTOs
  tab <- table(sce$GeneticDonor, sce$dmmHTO)
  tab <- tab[!rownames(tab) %in% c("Doublet","Unknown"),
             !colnames(tab) %in% c("Doublet","Negative")]
  # extract all genetically unique samples in this batch
  sub <- tab[rowSums(tab/rowSums(tab) > 0.2) == 1, , drop = FALSE]
  # create dictionary to translate genetics to HTO
  dict <- apply(sub, 1, function(x){
    colnames(tab)[which(x == max(x))]
  })
  dict <- c(dict, "Doublet" = "Doublet", "Unknown" = "Negative")
 
  sce$ProcessedHTO <- NA
  # assign HTO based on genetics
  genetics <- sce$GeneticDonor %in% names(dict)
  sce$ProcessedHTO[genetics] <- unname(dict[sce$GeneticDonor[genetics]])
  
  # if there are longitudinal samples also assign HTO based on hashtags
  if(dim(sub)[1] != dim(sub)[2]){
    HTO <- !sce$GeneticDonor %in% names(dict)
    sce$ProcessedHTO[HTO] <- sce$dmmHTO[HTO]
    
  }
  
  # add sample metadata based on processed HTO
  colData(sce) %>%
    data.frame() %>%
    rownames_to_column("cell") %>%
    left_join(ext_metadata_df %>% 
                dplyr::filter(Batch == (i-1)) %>%
                data.frame, 
              by = c("ProcessedHTO" = "Hashtag")) %>%
    mutate(sample.id = ifelse(is.na(sample.id), 
                              ProcessedHTO,
                              sample.id)) %>%
    column_to_rownames("cell") %>% 
    DataFrame() -> colData(sce)
  
  sce
})

Number of cells per sample

p <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  colData(sce) %>%
    data.frame() %>%
    ggplot(aes(x = sample.id, fill = Donor)) +
    geom_bar(position = position_dodge()) + 
    geom_text(aes(label = ..count..),
              stat = "count", vjust = 1.5, 
              colour = "black", size = 2) +
    theme(legend.position = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
    ggtitle(glue("batch {i-1}"))
})

wrap_plots(p, ncol = 2)

Version Author Date
c2985bb Jovana Maksimovic 2024-02-27

No. of doublets called per batch

p <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  colData(sce) %>%
    data.frame() %>%
    mutate(scds = ifelse(hybrid_call, "Doublet", "Singlet"),
           scdf = ifelse(scDblFinder.class == "doublet", "Doublet", "Singlet")) %>%
    dplyr::select(ProcessedHTO, scds, scdf, Capture) %>% 
    rownames_to_column(var = "cell") %>%
    mutate(vireo_dmm_dbl = (ProcessedHTO == "Doublet"),
           scds_dbl = (scds == "Doublet"),
           scdf_dbl = (scdf == "Doublet")) %>%
    dplyr::select(cell, vireo_dmm_dbl, scds_dbl, scdf_dbl, Capture) %>% 
    pivot_longer(cols = c(vireo_dmm_dbl, scds_dbl, scdf_dbl), names_to = "method") %>%
    dplyr::filter(value == TRUE) %>%
    group_by(cell) %>%
    summarise(data = list(method), Capture) %>%
    rowwise() %>%
    ggplot(aes(x = data)) +
    geom_bar() +
    scale_x_upset(n_intersections = 20) +
    geom_text(stat = 'count', aes(label = after_stat(count)), 
              vjust = -0.5, size = 2) +
    ggtitle(glue("batch {i-1}"))
})

wrap_plots(p, ncol = 2)

Version Author Date
c2985bb Jovana Maksimovic 2024-02-27

Doublet filtering

We HAVE to filter out cells that are called doublets by either vireo or demuxmix (for relevant samples only) because we have no singlet assignment to a sample for them. We also HAVE to filter out negative cells as they also have no singlet assignment. We will also filter out cells that are called doublets by BOTH scds and scDblFinder (both methods were suggested in the demuxafy paper) that are not already in the vireo/demuxmix doublet pool. The percentages for those doublets calls per capture are shown below.

lapply(sceLst, function(sce){
  scds_scdf <- table(sce$Capture, sce$hybrid_call & sce$scDblFinder.class == "doublet" &
                       !sce$ProcessedHTO == "Doublet")
  vireo_dmm <- table(sce$Capture, sce$ProcessedHTO == "Doublet")
  
  if(length(levels(sce$Capture)) < 4) {
    data.frame(scds_scdf_dbl = scds_scdf[,2]/scds_scdf[,1]*100,
               vireo_dmm_dbl = vireo_dmm[,2]/vireo_dmm[,1]*100) -> tab
  } else {
    data.frame(scds_scdf_dbl = scds_scdf[,2]/scds_scdf[,1]*100) -> tab
  }
  
  tab %>% knitr::kable()
})

[[1]]

scds_scdf_dbl
A 3.104529
B 3.236475
C 6.003251
D 5.830370

[[2]]

scds_scdf_dbl vireo_dmm_dbl
C133_batch1_1 2.545172 7.79128
C133_batch1_2 2.533237 11.10678

[[3]]

scds_scdf_dbl vireo_dmm_dbl
C133_batch2_1 1.635894 29.69542
C133_batch2_2 1.523074 57.17682

[[4]]

scds_scdf_dbl vireo_dmm_dbl
C133_batch3_1 2.370001 24.63025
C133_batch3_2 2.425704 20.25649

[[5]]

scds_scdf_dbl vireo_dmm_dbl
C133_batch4_1 3.619517 18.65984
C133_batch4_2 2.475446 22.84497

[[6]]

scds_scdf_dbl vireo_dmm_dbl
C133_batch5_1 3.343934 13.83751
C133_batch5_2 3.217268 18.20225

[[7]]

scds_scdf_dbl vireo_dmm_dbl
C133_batch6_1 1.518363 42.77545
C133_batch6_2 1.508269 31.80439

First, filter out wheeze and CSLD samples as we will focus only on CF.

sceLst <- lapply(sceLst, function(sce){
  sce[, !sce$Disease %in% c("Wheeze", "CSLD")]
})

p <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  colData(sce) %>%
    data.frame() %>%
    ggplot(aes(x = sample.id, fill = Donor)) +
    geom_bar(position = position_dodge()) + 
    geom_text(aes(label = ..count..),
              stat = "count", vjust = 1.5, 
              colour = "black", size = 2) +
    theme(legend.position = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
    ggtitle(glue("batch {i-1}"))
})

wrap_plots(p, ncol = 2)

Version Author Date
c2985bb Jovana Maksimovic 2024-02-27

Then, filter out doublets.

sceLst <- lapply(sceLst, function(sce){
  sce[, !(sce$ProcessedHTO %in% c("Doublet") | 
                                  (sce$hybrid_call & sce$scDblFinder.class == "doublet"))]
})

p <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  colData(sce) %>%
    data.frame() %>%
    ggplot(aes(x = sample.id, fill = Donor)) +
    geom_bar(position = position_dodge()) + 
    geom_text(aes(label = ..count..),
              stat = "count", vjust = 1.5, 
              colour = "black", size = 2) +
    theme(legend.position = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
    ggtitle(glue("batch {i-1}"))
})

wrap_plots(p, ncol = 2)

Version Author Date
c2985bb Jovana Maksimovic 2024-02-27

Finally, filter out negatives cells.

sceLst <- lapply(sceLst, function(sce){
  sce[, !(sce$sample.id %in% "Negative")]
})

p <- lapply(1:length(sceLst), function(i){
  sce <- sceLst[[i]]
  colData(sce) %>%
    data.frame() %>%
    ggplot(aes(x = sample.id, fill = Donor)) +
    geom_bar(position = position_dodge()) + 
    geom_text(aes(label = ..count..),
              stat = "count", vjust = 1.5, 
              colour = "black", size = 2) +
    theme(legend.position = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
    ggtitle(glue("batch {i-1}"))
})

wrap_plots(p, ncol = 2)

Version Author Date
c2985bb Jovana Maksimovic 2024-02-27

Cell counts per sample after filtering

p <- lapply(sceLst, function(sce){
  colData(sce) %>%
    data.frame() %>%
    mutate(donor.fct = factor(Donor, labels = 1:length(unique(Donor)))) %>%
    ggplot(aes(x = sample.id, fill = donor.fct)) +
    geom_bar(position = position_dodge()) + 
    geom_text(aes(label = ..count..),
              stat = "count", vjust = 1.5, 
              colour = "black", size = 2) +
    theme(legend.position = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))
})

wrap_plots(p, ncol = 2)

Version Author Date
c2985bb Jovana Maksimovic 2024-02-27

QC per sample after filtering

p <- lapply(sceLst, function(sce){
  colData(sce) %>%
    data.frame() %>% 
    mutate(donor.fct = factor(Donor, labels = 1:length(unique(Donor)))) %>%
    ggplot(aes(x = sample.id, y = sum,
               fill = donor.fct)) +
    geom_violin() +
    scale_y_log10() +
    theme(legend.position = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
    labs(x = "Sample",
         y = "Library size (log10 scale)") -> p1
  
  colData(sce) %>%
    data.frame() %>% 
    mutate(donor.fct = factor(Donor, labels = 1:length(unique(Donor)))) %>%
    ggplot(aes(x = sample.id, y = detected,
               fill = donor.fct)) +
    geom_violin() +
    scale_y_log10() +
    theme(legend.position = "none") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
    labs(x = "Sample",
         y = "No. genes detected (log10 scale)") -> p2
  
  p1 + p2
})

wrap_plots(p, ncol = 1) &
  theme(axis.title.y = element_text(size = 9))

Version Author Date
c2985bb Jovana Maksimovic 2024-02-27

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("C133_Neeland_{batches[i]}.doublets_filtered.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")
})
[[1]]
NULL

[[2]]
NULL

[[3]]
NULL

[[4]]
NULL

[[5]]
NULL

[[6]]
NULL

[[7]]
NULL

Session info


sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.3

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] SingleCellExperiment_1.24.0 SummarizedExperiment_1.32.0
 [3] Biobase_2.62.0              GenomicRanges_1.54.1       
 [5] GenomeInfoDb_1.38.6         IRanges_2.36.0             
 [7] S4Vectors_0.40.2            BiocGenerics_0.48.1        
 [9] MatrixGenerics_1.14.0       matrixStats_1.2.0          
[11] ggupset_0.3.0               readxl_1.4.3               
[13] paletteer_1.6.0             patchwork_1.2.0            
[15] glue_1.7.0                  here_1.0.1                 
[17] lubridate_1.9.3             forcats_1.0.0              
[19] stringr_1.5.1               dplyr_1.1.4                
[21] purrr_1.0.2                 readr_2.1.5                
[23] tidyr_1.3.1                 tibble_3.2.1               
[25] ggplot2_3.4.4               tidyverse_2.0.0            
[27] BiocStyle_2.30.0            workflowr_1.7.1            

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0        farver_2.1.1            bitops_1.0-7           
 [4] fastmap_1.1.1           RCurl_1.98-1.14         promises_1.2.1         
 [7] digest_0.6.34           timechange_0.3.0        lifecycle_1.0.4        
[10] processx_3.8.3          magrittr_2.0.3          compiler_4.3.2         
[13] rlang_1.1.3             sass_0.4.8              tools_4.3.2            
[16] utf8_1.2.4              yaml_2.3.8              knitr_1.45             
[19] labeling_0.4.3          S4Arrays_1.2.0          DelayedArray_0.28.0    
[22] abind_1.4-5             withr_3.0.0             grid_4.3.2             
[25] fansi_1.0.6             git2r_0.33.0            colorspace_2.1-0       
[28] scales_1.3.0            cli_3.6.2               rmarkdown_2.25         
[31] crayon_1.5.2            generics_0.1.3          rstudioapi_0.15.0      
[34] httr_1.4.7              tzdb_0.4.0              cachem_1.0.8           
[37] zlibbioc_1.48.0         BiocManager_1.30.22     cellranger_1.1.0       
[40] XVector_0.42.0          vctrs_0.6.5             Matrix_1.6-5           
[43] jsonlite_1.8.8          callr_3.7.3             hms_1.1.3              
[46] jquerylib_0.1.4         rematch2_2.1.2          ps_1.7.6               
[49] stringi_1.8.3           gtable_0.3.4            later_1.3.2            
[52] munsell_0.5.0           pillar_1.9.0            htmltools_0.5.7        
[55] GenomeInfoDbData_1.2.11 R6_2.5.1                rprojroot_2.0.4        
[58] evaluate_0.23           lattice_0.22-5          highr_0.10             
[61] renv_1.0.3              httpuv_1.6.14           bslib_0.6.1            
[64] Rcpp_1.0.12             SparseArray_1.2.4       whisker_0.4.1          
[67] xfun_0.42               fs_1.6.3                getPass_0.2-4          
[70] pkgconfig_2.0.3