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Introduction

Load data

We reprocessed the data from Calcagno et al, using the original cell-type annotations from the paper and will load this dataset as a seurat object.

## Load reprocessed Calcagno et al seurat object
calcagno_et_al <- LoadH5Seurat("./data/Calcagno2022_int_logNorm_annot.h5Seurat")
Validating h5Seurat file
Initializing RNA with data
Adding counts for RNA
Adding miscellaneous information for RNA
Initializing integrated with data
Adding scale.data for integrated
Adding variable feature information for integrated
Adding miscellaneous information for integrated
Adding reduction pca
Adding cell embeddings for pca
Adding feature loadings for pca
Adding miscellaneous information for pca
Adding reduction umap
Adding cell embeddings for umap
Adding miscellaneous information for umap
Adding graph integrated_nn
Adding graph integrated_snn
Adding command information
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Adding miscellaneous information
Adding tool-specific results
Adding data that was not associated with an assay
Warning: Adding a command log without an assay associated with it
## Get only control cells for marker calculation
calcagno_et_al_d0 <- subset(calcagno_et_al,time == "D0")
calcagno_et_al_d1 <- subset(calcagno_et_al,time == "D1")

Let’s check the UMAP embedding from our reprocessed object.

## UMAP plot
DimPlot(calcagno_et_al,label = TRUE) + theme(legend.position = "none")

Version Author Date
5dee03d FloWuenne 2023-09-04
ed31d81 FloWuenne 2023-07-02

Next, let’s quickly verify, that Endocardial cells are expressing the proper markers before we compare them to our proteomic data.

plot_density(calcagno_et_al_d0, features = "Npr3")

Version Author Date
5dee03d FloWuenne 2023-09-04
ed31d81 FloWuenne 2023-07-02 
VlnPlot(calcagno_et_al_d0, features = "Npr3")

Version Author Date
5dee03d FloWuenne 2023-09-04
ed31d81 FloWuenne 2023-07-02

Expression of the endocardial specific marker Npr3 in this dataset fits with the original authors annotation, suggesting that we can use these endocardial single-cell signature to identify endocardial specific genes in our proteomics data.

Analyze cell-type specific proteins in proteomic data

We will use the snRNAseq data to identify proteins likely differentially expressed in endocardial cells. For this, we will first identify genes specifically expressed in endocardial cells.

Check subclustering of endocard cells at d1

endocard_d1 <- subset(calcagno_et_al, level_2 == "Endocardial" & time == "D1")
endocard_d1 <- SCTransform(endocard_d1)
Running SCTransform on assay: RNA
vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
`vst.flavor` is set to 'v2' but could not find glmGamPoi installed.
Please install the glmGamPoi package for much faster estimation.
--------------------------------------------
install.packages('BiocManager')
BiocManager::install('glmGamPoi')
--------------------------------------------
Falling back to native (slower) implementation.
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 11133 by 645
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 645 cells
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control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached
Warning in glm.nb(formula = as.formula(new_formula), data = data): alternation
limit reached
Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached
Warning in glm.nb(formula = as.formula(new_formula), data = data): alternation
limit reached
Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached
Warning in glm.nb(formula = as.formula(new_formula), data = data): alternation
limit reached
Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached
Warning in glm.nb(formula = as.formula(new_formula), data = data): alternation
limit reached
Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
control$trace > : iteration limit reached

Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
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Warning in glm.nb(formula = as.formula(new_formula), data = data): alternation
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Found 39 outliers - those will be ignored in fitting/regularization step
Second step: Get residuals using fitted parameters for 11133 genes
Computing corrected count matrix for 11133 genes
Calculating gene attributes
Wall clock passed: Time difference of 36.86647 secs
Determine variable features
Centering data matrix
Place corrected count matrix in counts slot
Set default assay to SCT
endocard_d1 <- RunPCA(endocard_d1, verbose = FALSE)
endocard_d1 <- RunUMAP(endocard_d1, dims = 1:10, verbose = FALSE)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
Found more than one class "dist" in cache; using the first, from namespace 'spam'
Also defined by 'BiocGenerics'
endocard_d1 <- FindNeighbors(endocard_d1, dims = 1:10, verbose = FALSE)
endocard_d1 <- FindClusters(endocard_d1, verbose = FALSE, resolution = 0.2)
DimPlot(endocard_d1, label = TRUE) + NoLegend()

Version Author Date
5dee03d FloWuenne 2023-09-04
ed31d81 FloWuenne 2023-07-02 
endo_diff_marker <- FindAllMarkers(endocard_d1, only.pos = TRUE)
Calculating cluster 0
For a (much!) faster implementation of the Wilcoxon Rank Sum Test,
(default method for FindMarkers) please install the presto package
--------------------------------------------
install.packages('devtools')
devtools::install_github('immunogenomics/presto')
--------------------------------------------
After installation of presto, Seurat will automatically use the more 
efficient implementation (no further action necessary).
This message will be shown once per session
Calculating cluster 1
Calculating cluster 2
Calculating cluster 3
endo_diff_marker_top <- endo_diff_marker %>%
  subset(p_val_adj < 0.05)
VlnPlot(endocard_d1, features = c("Npr3","Selp","Taco1","Serpine1","Prkg1","Nrg1"))

Version Author Date
5dee03d FloWuenne 2023-09-04
ed31d81 FloWuenne 2023-07-02

Correlate pseudobulk snRNA-seq expression in endocardial cells with proteomic measurements

Let’s load the proteomic data now:

limma_res <- fread("./output/proteomics/proteomics.limma.full_statistics.tsv")

## Extract statistics for different contrasts
miiz_vs_control_signature <- subset(limma_res,analysis == "MI_IZ_vs_control")
miiz_vs_remote_signature <- subset(limma_res,analysis == "MI_IZ_vs_MI_remote")

## Load the normalized protein matrix as well
protein_mat <- fread(file = "./output/proteomics/proteomics.vsn_norm_proteins.tsv")
protein_mat_avg <- protein_mat %>%
  mutate(avg_control=rowMeans(.[ , c("control_r1","control_r2","control_r3")], na.rm=TRUE)) %>%
  mutate(avg_MI_IZ=rowMeans(.[ , c("MI_IZ_r1","MI_IZ_r2","MI_IZ_r3","MI_IZ_r4")], na.rm=TRUE)) %>%
  mutate(avg_MI_remote=rowMeans(.[ , c("MI_remote_r1","MI_remote_r2","MI_remote_r3","MI_remote_r4")], na.rm=TRUE)) %>%
  dplyr::select(gene,avg_control,avg_MI_IZ,avg_MI_remote)
## Calculate pseudobulk expression profiles for endocardial cells
endocard_seurat <- subset(calcagno_et_al, level_2 == "Endocardial")
sn_endo_bulk <- AverageExpression(endocard_seurat, group.by = c("time"),slot= "data")
Warning: The `slot` argument of `AverageExpression()` is deprecated as of Seurat 5.0.0.
ℹ Please use the `layer` argument instead.
ℹ The deprecated feature was likely used in the Seurat package.
  Please report the issue at <https://github.com/satijalab/seurat/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
As of Seurat v5, we recommend using AggregateExpression to perform pseudo-bulk analysis.
First group.by variable `time` starts with a number, appending `g` to ensure valid variable names
This message is displayed once per session.
sn_endo_bulk_df <- as.data.frame(sn_endo_bulk$RNA)
sn_endo_bulk_df$gene <- rownames(sn_endo_bulk_df)
## Merge average protein expression values with average RNA expression
rna_protein_avg <- left_join(protein_mat_avg,sn_endo_bulk_df, by = "gene") %>%
  drop_na()

corrplot_rna_protein <- ggplot(rna_protein_avg,aes(avg_control,D0, label = gene)) +
  geom_point() +
  geom_point(data = subset(rna_protein_avg, gene == "Vwf"),color = "red", size =3) +
  labs(x = "Average protein expression (Control)",
       y = "Average snRNA-seq expression Control)")
corrplot_rna_protein

Version Author Date
5dee03d FloWuenne 2023-09-04
ed31d81 FloWuenne 2023-07-02

Calculate differentially expressed genes in endocardial cells in snRNA-seq

calcagno_et_al$cell_type_time <- paste(calcagno_et_al$level_2, calcagno_et_al$time,
    sep = "_")
Idents(calcagno_et_al) <- "cell_type_time"

endocard_de <- FindMarkers(calcagno_et_al, 
                           ident.1 = "Endocardial_D1", 
                           ident.2 = "Endocardial_D0",
                           min.diff.pct = 0.1,
                           logfc.threshold = 0,
                           verbose = FALSE)

colnames(endocard_de) <- gsub("\\.","_",colnames(endocard_de))
endocard_de <- endocard_de %>% 
  mutate("gene" = rownames(endocard_de)) %>%
  mutate("pct_ratio" = pct_2 /pct_1,
         "pct_diff" = pct_2 -pct_1) %>%
  arrange(desc(avg_log2FC))

Get endocardial specific genes

endo_marker <- FindMarkers(calcagno_et_al,ident.1 = "Endocardial_D0",
                           only.pos = TRUE)
endo_marker$gene <- rownames(endo_marker)
endo_marker <- endo_marker %>%
  mutate("pct_diff" = pct.1 - pct.2) %>% # Only 
  mutate("pct_ratio" = pct.1 / pct.2) %>%
  subset(pct.2 < 0.1)

Compare endocardial specific genes with differentially expressed proteins (DEPs)

merged_protein_rna <- left_join(endo_marker,miiz_vs_remote_signature, by = "gene")

merged_protein_rna <- merged_protein_rna %>%
  mutate("label_gene" = if_else(gene %in% c("Vwf","Npr3"),gene,""))

endo_proteomic_corr <- ggplot(merged_protein_rna,aes(avg_log2FC,logFC,
                              label = label_gene)) +
  geom_point(data =subset(merged_protein_rna,gene != "Vwf"), size =3, fill = "darkgrey", pch = 21) +
  geom_point(data = subset(merged_protein_rna,gene == "Vwf"),size = 4, fill = "red", pch = 21) +
  geom_point(data = subset(merged_protein_rna,gene == "Npr3"),size = 4, fill = "purple", pch = 21) +
  geom_label_repel() +
  labs(x = "Specificity for endocardial cells (snRNA-seq)",
       y = "Log-fold change MI_IZ vs control(proteomics)")

endo_proteomic_corr
Warning: Removed 3236 rows containing missing values (`geom_point()`).
Warning: Removed 3236 rows containing missing values (`geom_label_repel()`).

write.table(merged_protein_rna,
            file = "./output/proteomics/proteomics.snRNAseq_comp.tsv",
            sep = "\t",
            col.names = TRUE,
            row.names = FALSE,
            quote = FALSE)

Check coagulation pathway gene expression in all cell-types in snRNA-seq

coagulation_mgsea <- readRDS("./references/mh.all.v2023.1.Mm.symbols.sets.rds") %>%
  subset(source == "HALLMARK_COAGULATION")

coagulation_genes_proteomics <- miiz_vs_remote_signature %>%
  subset(gene %in% unique(coagulation_mgsea$target)) %>%
  arrange(desc(logFC)) %>%
  subset(logFC >1)

miiz_vs_remote_signature <- miiz_vs_remote_signature %>%
  arrange(desc(logFC)) %>%
  mutate("rank" = 1:nrow(miiz_vs_remote_signature))

library(scCustomize)
scCustomize v2.0.1
If you find the scCustomize useful please cite.
See 'samuel-marsh.github.io/scCustomize/articles/FAQ.html' for citation info.
Idents(calcagno_et_al) <- "level_2"

Clustered_DotPlot(calcagno_et_al,features = coagulation_genes_proteomics$gene, x_lab_rotate = TRUE, cluster_ident = FALSE)
Warning: The following features were omitted as they were not found:
ℹ Fgg and Fga

Version Author Date
ed31d81 FloWuenne 2023-07-02 
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[[2]]


sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.1.2

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: Europe/Berlin
tzcode source: internal

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

other attached packages:
 [1] scCustomize_2.0.1     RColorBrewer_1.1-3    ggsci_3.0.0          
 [4] cowplot_1.1.1         SeuratDisk_0.0.0.9021 plotly_4.10.3        
 [7] ggrepel_0.9.4         data.table_1.14.8     Nebulosa_1.12.0      
[10] patchwork_1.1.3       Libra_1.7             nnls_1.5             
[13] here_1.0.1            Seurat_5.0.1          SeuratObject_5.0.1   
[16] sp_2.1-2              lubridate_1.9.3       forcats_1.0.0        
[19] stringr_1.5.1         dplyr_1.1.4           purrr_1.0.2          
[22] readr_2.1.4           tidyr_1.3.0           tibble_3.2.1         
[25] ggplot2_3.4.4         tidyverse_2.0.0       workflowr_1.7.1      

loaded via a namespace (and not attached):
  [1] fs_1.6.3                    matrixStats_1.1.0          
  [3] spatstat.sparse_3.0-3       bitops_1.0-7               
  [5] httr_1.4.7                  doParallel_1.0.17          
  [7] tools_4.3.1                 sctransform_0.4.1          
  [9] utf8_1.2.4                  R6_2.5.1                   
 [11] lazyeval_0.2.2              uwot_0.1.16                
 [13] GetoptLong_1.0.5            withr_2.5.2                
 [15] gridExtra_2.3               progressr_0.14.0           
 [17] cli_3.6.1                   Biobase_2.62.0             
 [19] Cairo_1.6-2                 spatstat.explore_3.2-5     
 [21] fastDummies_1.7.3           labeling_0.4.3             
 [23] sass_0.4.7                  prismatic_1.1.1            
 [25] mvtnorm_1.2-4               spatstat.data_3.0-3        
 [27] ggridges_0.5.4              pbapply_1.7-2              
 [29] parallelly_1.36.0           limma_3.58.1               
 [31] rstudioapi_0.15.0           generics_0.1.3             
 [33] shape_1.4.6                 ica_1.0-3                  
 [35] spatstat.random_3.2-2       Matrix_1.6-4               
 [37] ggbeeswarm_0.7.2            fansi_1.0.5                
 [39] S4Vectors_0.40.2            abind_1.4-5                
 [41] lifecycle_1.0.4             whisker_0.4.1              
 [43] yaml_2.3.7                  snakecase_0.11.1           
 [45] SummarizedExperiment_1.32.0 SparseArray_1.2.2          
 [47] Rtsne_0.16                  paletteer_1.5.0            
 [49] grid_4.3.1                  promises_1.2.1             
 [51] crayon_1.5.2                miniUI_0.1.1.1             
 [53] lattice_0.22-5              magick_2.8.1               
 [55] pillar_1.9.0                knitr_1.45                 
 [57] ComplexHeatmap_2.18.0       GenomicRanges_1.54.1       
 [59] rjson_0.2.21                future.apply_1.11.0        
 [61] codetools_0.2-19            leiden_0.4.3.1             
 [63] glue_1.6.2                  getPass_0.2-2              
 [65] vctrs_0.6.5                 png_0.1-8                  
 [67] spam_2.10-0                 gtable_0.3.4               
 [69] rematch2_2.1.2              cachem_1.0.8               
 [71] ks_1.14.1                   xfun_0.41                  
 [73] S4Arrays_1.2.0              mime_0.12                  
 [75] pracma_2.4.4                survival_3.5-7             
 [77] SingleCellExperiment_1.24.0 iterators_1.0.14           
 [79] statmod_1.5.0               ellipsis_0.3.2             
 [81] fitdistrplus_1.1-11         ROCR_1.0-11                
 [83] nlme_3.1-164                bit64_4.0.5                
 [85] RcppAnnoy_0.0.21            GenomeInfoDb_1.38.1        
 [87] rprojroot_2.0.4             bslib_0.6.1                
 [89] irlba_2.3.5.1               vipor_0.4.5                
 [91] KernSmooth_2.23-22          colorspace_2.1-0           
 [93] BiocGenerics_0.48.1         ggrastr_1.0.2              
 [95] tidyselect_1.2.0            processx_3.8.2             
 [97] bit_4.0.5                   compiler_4.3.1             
 [99] git2r_0.33.0                hdf5r_1.3.8                
[101] DelayedArray_0.28.0         scales_1.3.0               
[103] lmtest_0.9-40               callr_3.7.3                
[105] digest_0.6.33               goftest_1.2-3              
[107] spatstat.utils_3.0-4        rmarkdown_2.25             
[109] XVector_0.42.0              htmltools_0.5.7            
[111] pkgconfig_2.0.3             MatrixGenerics_1.14.0      
[113] highr_0.10                  fastmap_1.1.1              
[115] rlang_1.1.2                 GlobalOptions_0.1.2        
[117] htmlwidgets_1.6.3           shiny_1.8.0                
[119] farver_2.1.1                jquerylib_0.1.4            
[121] zoo_1.8-12                  jsonlite_1.8.8             
[123] mclust_6.0.1                RCurl_1.98-1.13            
[125] magrittr_2.0.3              GenomeInfoDbData_1.2.11    
[127] dotCall64_1.1-1             munsell_0.5.0              
[129] Rcpp_1.0.11                 reticulate_1.34.0          
[131] stringi_1.8.2               zlibbioc_1.48.0            
[133] MASS_7.3-60                 plyr_1.8.9                 
[135] parallel_4.3.1              listenv_0.9.0              
[137] deldir_2.0-2                splines_4.3.1              
[139] tensor_1.5                  hms_1.1.3                  
[141] circlize_0.4.15             ps_1.7.5                   
[143] igraph_1.5.1                spatstat.geom_3.2-7        
[145] RcppHNSW_0.5.0              reshape2_1.4.4             
[147] stats4_4.3.1                evaluate_0.23              
[149] renv_1.0.3                  BiocManager_1.30.22        
[151] ggprism_1.0.4               tzdb_0.4.0                 
[153] foreach_1.5.2               httpuv_1.6.12              
[155] RANN_2.6.1                  polyclip_1.10-6            
[157] future_1.33.0               clue_0.3-65                
[159] scattermore_1.2             janitor_2.2.0              
[161] xtable_1.8-4                RSpectra_0.16-1            
[163] later_1.3.1                 viridisLite_0.4.2          
[165] beeswarm_0.4.0              IRanges_2.36.0             
[167] cluster_2.1.6               timechange_0.2.0           
[169] globals_0.16.2