You will need to download raw data from: https://lambrechtslab.sites.vib.be/en/immune-atlas
The page requires login to download data, but any Google account is good. After login, there will be multiple links for downloading. The 2069-Allcells.counts.rds is available in the “Clustering all cells” section. The 2076-OnlineTMEM106B.rds is in the “TMEM106B” section.
library(Seurat)Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObject
Attaching spdata <- readRDS("2076-OnlineTMEM106B.rds")
data@meta.data
obj <- readRDS("2069-Allcells.counts.rds")
meta.data <- data@meta.data[colnames(obj), ]
obj <- CreateSeuratObject(obj, meta.data = meta.data)
objAn object of class Seurat
33538 features across 65166 samples within 1 assay
Active assay: RNA (33538 features, 0 variable features)obj$Patient <- stringr::str_match(colnames(obj), "^(.+)_")[, 2]
table(obj$Patient, obj$PatientType)
ICU mild severe Ward
BAL001 0 0 1884 0
BAL002 0 3775 0 0
BAL003 0 2117 0 0
BAL009 0 1212 0 0
BAL010 0 6395 0 0
BAL011 0 448 0 0
BAL012 0 0 498 0
BAL013 0 0 1950 0
BAL014 0 0 1939 0
BAL015 0 0 1989 0
BAL016 0 0 1399 0
BAL017 0 1128 0 0
BAL018 0 504 0 0
BAL019 0 11574 0 0
BAL020 3 0 1405 0
BAL021 0 0 2593 0
BAL022 55 0 1427 0
BAL023 71 0 1153 0
BAL024 0 0 2267 0
BAL025 0 0 1175 0
BAL026 115 0 1996 0
BAL027 40 0 1447 0
BAL028 0 1183 0 0
BAL029 0 1278 0 0
BAL030 0 310 0 0
BAL031 0 0 3795 0
BAL032 0 0 582 0
BAL033 31 0 974 0
BAL034 0 0 1199 0
BAL035 0 0 1144 0
BAL036 0 230 0 0
BAL037 0 896 0 18
BAL038 0 1097 0 0
BAL039 0 0 1017 0
BAL040 10 0 843 0obj$PatientType2 <- obj$PatientType
obj$PatientType2[obj$PatientType2 == 'ICU'] <- 'severe'
obj$PatientType2[obj$PatientType2 == 'Ward'] <- 'mild'obj <- NormalizeData(obj, verbose = F)library(Seurat)
library(cowplot)
library(grid)
library(gridExtra)
library(ggplot2)
library(lattice)
median.stat <- function(x){
out <- quantile(x, probs = c(0.5))
names(out) <- c("ymed")
return(out)
}
median.stat75 <- function(x){
out <- quantile(x, probs = c(0.75))
names(out) <- c("ymed")
return(out)
}
`%ni%`<- Negate(`%in%`)data <- obj
goi <- read.table("negative-candidates.txt")$V1
#cnt = 0
data$Domain_disease_patient_type <- paste(data$Domain, data$Disease, data$PatientType2, sep='_')
data$Domain_disease_patient_type <- factor(data$Domain_disease_patient_type,
levels = c("Epithelial_control_mild", "Epithelial_control_severe",
"Epithelial_COVID19_mild", "Epithelial_COVID19_severe"
))
Idents(data) <- 'Domain_disease_patient_type'
my_comparisons <- list( c("Epithelial_control_mild", "Epithelial_control_severe"),
c("Epithelial_control_severe", "Epithelial_COVID19_mild"),
c("Epithelial_COVID19_mild", "Epithelial_COVID19_severe"),
c("Epithelial_control_mild", "Epithelial_COVID19_mild"),
c("Epithelial_control_severe", "Epithelial_COVID19_severe"),
c("Epithelial_control_mild", "Epithelial_COVID19_severe"))
df <- data.frame(nrows = goi)
rownames(df) <- goi
for (cmp in my_comparisons) {
df[[paste0(cmp[1], '_vs_', cmp[2], '_p')]] <- NA
df[[paste0(cmp[1], '_vs_', cmp[2], '_p_adj')]] <- NA
df[[paste0(cmp[1], '_vs_', cmp[2], '_logfc')]] <- NA
mutual_features <- intersect(goi, rownames(data))
deg <- FindMarkers(data, cmp[1], cmp[2], features = mutual_features, slot = "data",
min.cells.group = 0,
min.cells.feature = 0,
min.pct = -1,
logfc.threshold = -1,
only.pos = FALSE)
df[mutual_features, paste0(cmp[1], '_vs_', cmp[2], '_p')] <- deg[mutual_features, 'p_val']
df[mutual_features, paste0(cmp[1], '_vs_', cmp[2], '_p_adj')] <- deg[mutual_features, 'p_val_adj']
df[mutual_features, paste0(cmp[1], '_vs_', cmp[2], '_logfc')] <- deg[mutual_features, 'avg_log2FC']
}
| | 0 % ~calculating
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| | 0 % ~calculating
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| | 0 % ~calculating
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| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s write.csv(df, "epithelial-severity-de-negative-targets.csv")
goi <- read.table("positive-candidates.txt")$V1
#cnt = 0
data$Domain_disease_patient_type <- paste(data$Domain, data$Disease, data$PatientType2, sep='_')
data$Domain_disease_patient_type <- factor(data$Domain_disease_patient_type,
levels = c("Epithelial_control_mild", "Epithelial_control_severe",
"Epithelial_COVID19_mild", "Epithelial_COVID19_severe"
))
Idents(data) <- 'Domain_disease_patient_type'
my_comparisons <- list( c("Epithelial_control_mild", "Epithelial_control_severe"),
c("Epithelial_control_severe", "Epithelial_COVID19_mild"),
c("Epithelial_COVID19_mild", "Epithelial_COVID19_severe"),
c("Epithelial_control_mild", "Epithelial_COVID19_mild"),
c("Epithelial_control_severe", "Epithelial_COVID19_severe"),
c("Epithelial_control_mild", "Epithelial_COVID19_severe"))
df <- data.frame(nrows = goi)
rownames(df) <- goi
for (cmp in my_comparisons) {
df[[paste0(cmp[1], '_vs_', cmp[2], '_p')]] <- NA
df[[paste0(cmp[1], '_vs_', cmp[2], '_p_adj')]] <- NA
df[[paste0(cmp[1], '_vs_', cmp[2], '_logfc')]] <- NA
mutual_features <- intersect(goi, rownames(data))
deg <- FindMarkers(data, cmp[1], cmp[2], features = mutual_features, slot = "data",
min.cells.group = 0,
min.cells.feature = 0,
min.pct = -1,
logfc.threshold = -1,
only.pos = FALSE)
df[mutual_features, paste0(cmp[1], '_vs_', cmp[2], '_p')] <- deg[mutual_features, 'p_val']
df[mutual_features, paste0(cmp[1], '_vs_', cmp[2], '_p_adj')] <- deg[mutual_features, 'p_val_adj']
df[mutual_features, paste0(cmp[1], '_vs_', cmp[2], '_logfc')] <- deg[mutual_features, 'avg_log2FC']
}
| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
| | 0 % ~calculating
|+ | 2 % ~00s
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| | 0 % ~calculating
|+ | 2 % ~01s
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| | 0 % ~calculating
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| | 0 % ~calculating
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| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s write.csv(df, "epithelial-severity-de-positive-targets.csv")