This vignette shows how to use SignacX with Seurat and MASC. There are three parts: Seurat, SignacX and then MASC. We use an example data set of kidney cells from AMP from this publication.
Load data
Read the CEL-seq2 data.
ReadCelseq <- function(counts.file, meta.file) {
E = suppressWarnings(readr::read_tsv(counts.file))
gns <- E$gene
E = E[, -1]
M = suppressWarnings(readr::read_tsv(meta.file))
S = lapply(unique(M$sample), function(x) {
logik = colnames(E) %in% M$cell_name[M$sample == x]
Matrix::Matrix(as.matrix(E[, logik]), sparse = TRUE)
})
names(S) <- unique(M$sample)
S = lapply(S, function(x) {
rownames(x) <- gns
x
})
S
}
counts.file = "./SDY997_EXP15176_celseq_matrix_ru10_molecules.tsv.gz"
meta.file = "./SDY997_EXP15176_celseq_meta.tsv"
E = ReadCelseq(counts.file = counts.file, meta.file = meta.file)
M = suppressWarnings(readr::read_tsv(meta.file))
Merge into a single matrix
# keep only kidney cells
E = lapply(E, function(x) {
x[, grepl("K", colnames(x))]
})
# remove any sample with no cells
E = E[sapply(E, ncol) > 0]
# merge into a single matrix
E = do.call(cbind, E)
Seurat
Start with the standard pre-processing steps for a Seurat object.
Create a Seurat object, and then perform SCTransform normalization. Note:
- You can use the legacy functions here (i.e., NormalizeData, ScaleData, etc.), use SCTransform or any other normalization method (including no normalization). We did not notice a significant difference in cell type annotations with different normalization methods.
- We think that it is best practice to use SCTransform, but it is not a necessary step. Signac will work fine without it.
# load data
kidney <- CreateSeuratObject(counts = E, project = "celseq")
# run sctransform
kidney <- SCTransform(kidney)
Perform dimensionality reduction by PCA and UMAP embedding. Note:
- SignacX actually needs these functions since it uses the nearest neighbor graph generated by Seurat.
# These are now standard steps in the Seurat workflow for visualization and clustering
kidney <- RunPCA(kidney, verbose = FALSE)
kidney <- RunUMAP(kidney, dims = 1:30, verbose = FALSE)
kidney <- FindNeighbors(kidney, dims = 1:30, verbose = FALSE)
SignacX
First, make sure that you have the SignacX package installed.
install.packages("SignacX")
Generate cellular phenotype labels for the Seurat object. Note:
- Optionally, you can do parallel computing by setting num.cores > 1 in the Signac function.
- Run time is ~10 minutes for ~10,000 cells on a single core.
# Run Signac
library(SignacX)
labels <- Signac(kidney, num.cores = 4)
celltypes = GenerateLabels(labels, E = kidney)
Visualizations
Now we can visualize the cell type classifications at many different levels:
kidney <- AddMetaData(kidney, metadata = celltypes$Immune, col.name = "immmune")
kidney <- SetIdent(kidney, value = "immmune")
png(filename = "fls/plot1_amp.png")
DimPlot(kidney)
dev.off()
kidney <- AddMetaData(kidney, metadata = celltypes$CellTypes, col.name = "celltypes")
kidney <- SetIdent(kidney, value = "celltypes")
png(filename = "fls/plot2_amp.png")
DimPlot(kidney)
dev.off()
kidney <- AddMetaData(kidney, metadata = celltypes$CellStates, col.name = "cellstates")
kidney <- SetIdent(kidney, value = "cellstates")
png(filename = "fls/plot3_amp.png")
DimPlot(kidney)
dev.off()
Identify immune marker genes (IMAGES)
# Downsample just the immune cells
kidney.small <- kidney[, !celltypes$CellStates %in% c("NonImmune", "Fibroblasts", "Unclassified",
"Endothelial", "Epithelial")]
# Find protein markers for all clusters, and draw a heatmap
markers <- FindAllMarkers(kidney.small, only.pos = TRUE, verbose = F, logfc.threshold = 1)
require(dplyr)
top5 <- markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC)
png(filename = "fls/plot4_amp.png", width = 640, height = 640)
DoHeatmap(kidney.small, features = unique(top5$gene), angle = 90)
dev.off()
Run MASC
Meta_mapped = M[match(colnames(kidney), M$cell_name), ]
Meta_mapped$CellStates = celltypes$CellStates
Meta_mapped$disease = factor(Meta_mapped$disease)
Q = MASC(dataset = Meta_mapped, cluster = Meta_mapped$CellStates, contrast = "disease", random_effects = c("plate",
"lane", "sample"))
MASC results reveals that fibroblasts, plasma cells and B memory cells are enriched (p value < 0.05) for disease.
|
cluster
|
size
|
model.pvalue
|
diseaseSLE.OR
|
diseaseSLE.OR.95pct.ci.lower
|
diseaseSLE.OR.95pct.ci.upper
|
|
B.memory
|
391
|
0.0197539
|
3.041576e+00
|
1.1841285
|
7.812654e+00
|
|
B.naive
|
144
|
0.4652596
|
1.548526e+00
|
0.4932181
|
4.861810e+00
|
|
DC
|
160
|
0.0536208
|
2.346694e+00
|
1.0278135
|
5.357951e+00
|
|
Endothelial
|
30
|
0.2290332
|
1.183979e+01
|
0.1021348
|
1.372508e+03
|
|
Epithelial
|
89
|
0.5615659
|
6.424482e-01
|
0.1488724
|
2.772440e+00
|
|
Fibroblasts
|
360
|
0.0174935
|
3.926459e+00
|
2.5047560
|
6.155124e+00
|
|
Macrophages
|
356
|
0.1061625
|
4.700437e-01
|
0.1959415
|
1.127587e+00
|
|
Mon.Classical
|
247
|
0.6346884
|
8.482458e-01
|
0.4416789
|
1.629059e+00
|
|
Mon.NonClassical
|
658
|
0.7360915
|
8.693916e-01
|
0.3903675
|
1.936231e+00
|
|
Neutrophils
|
11
|
0.3806773
|
1.800080e-02
|
0.0000020
|
1.590775e+02
|
|
NK
|
562
|
0.2814722
|
6.046977e-01
|
0.2470403
|
1.480160e+00
|
|
NonImmune
|
112
|
0.1608263
|
7.817140e-02
|
0.0021302
|
2.868614e+00
|
|
Plasma.cells
|
93
|
0.0055213
|
5.387766e+06
|
0.0000000
|
1.579379e+25
|
|
T.CD4.memory
|
283
|
0.5351536
|
6.933100e-01
|
0.2284183
|
2.104379e+00
|
|
T.CD4.naive
|
459
|
0.4627315
|
7.488830e-01
|
0.3546322
|
1.581429e+00
|
|
T.CD8.cm
|
482
|
0.1776758
|
1.917053e+00
|
0.7686826
|
4.781025e+00
|
|
T.CD8.em
|
1196
|
0.7985566
|
1.107881e+00
|
0.5097435
|
2.407877e+00
|
|
T.CD8.naive
|
39
|
0.8117720
|
1.132623e+00
|
0.4022482
|
3.189161e+00
|
|
T.regs
|
84
|
0.6186262
|
1.571148e+00
|
0.2607223
|
9.467957e+00
|
|
Unclassified
|
717
|
0.8900750
|
9.198574e-01
|
0.2840429
|
2.978908e+00
|
Save results
saveRDS(kidney, file = "fls/amp_kidney_signac.rds")
saveRDS(Q, file = "fls/amp_kidney_MASC_result.rds")
saveRDS(celltypes, file = "fls/amp_kidney_celltypes.rds")
Session Info
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_4.0.3 magrittr_2.0.1 formatR_1.7 htmltools_0.5.1.1
## [5] tools_4.0.3 yaml_2.2.1 stringi_1.5.3 rmarkdown_2.6
## [9] highr_0.8 knitr_1.30 stringr_1.4.0 digest_0.6.27
## [13] xfun_0.20 rlang_0.4.10 evaluate_0.14
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
