Last updated: 2019-07-09

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Knit directory: Porello-heart-snRNAseq/

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    Modified:   analysis/01-QualityControl.Rmd
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File	Version	Author	Date	Message
Rmd	f2c0317	Belinda Phipson	2019-07-09	update fetal analysis
html	b0f9c92	Belinda Phipson	2019-07-04	Build site.
Rmd	e590fe0	Belinda Phipson	2019-07-04	update fetal and young integrated analysis
html	4596c10	Belinda Phipson	2019-07-04	Build site.
Rmd	e1e1fd0	Belinda Phipson	2019-07-04	updated fetal analysis
html	c5d9a33	Belinda Phipson	2019-06-20	Build site.
Rmd	8766821	Belinda Phipson	2019-06-20	add new clustering analysis of fetal samples
Rmd	9c1e030	Belinda Phipson	2019-06-19	added new clustering analysis of single samples and updated integrated analysis
html	16a9bdf	Belinda Phipson	2019-06-17	Build site.
Rmd	1947fe3	Belinda Phipson	2019-06-17	Add changes to fetal clustering
Rmd	97c5925	Belinda Phipson	2019-06-14	updated clustering
html	6f2bf8d	Belinda Phipson	2019-06-09	Build site.
html	5839497	Belinda Phipson	2019-06-07	Build site.
html	2b103a6	Belinda Phipson	2019-06-06	Build site.
Rmd	ce619de	Belinda Phipson	2019-06-06	updated QC analysis and added fetal clustering ananalysis (incomplete)

Introduction

I tried a number of approaches to integrate the data and used the distribution of the cells from the fetal sample replicates across the clusters to determine how successful the integration was across samples. I also examined sex and batch as additional covariates.

I settled on the following approach:

Remove mitochondrial, ribosomal and genes with no annotation
Gene filtering of lowly expressed genes assuming min cluster size of 20
Remove genes on X and Y chromosome so they are not selected to perform clustering
Use SCTransform to normalise the data without including additional covariates
Perform data integration across biological replicates
- Use 30 CCA dimensions
- Use 3000 anchor features
Scale data
Run principle components analysis
Perform clustering
- Use 20 CCA dimensions
- Resolution = 0.3 for marker analysis
Visualisation with TSNE and clustree
Marker analysis

Load libraries and functions

library(edgeR)

Loading required package: limma

library(RColorBrewer)
library(org.Hs.eg.db)

Loading required package: AnnotationDbi

Loading required package: stats4

Loading required package: BiocGenerics

Loading required package: parallel


Attaching package: 'BiocGenerics'

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    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

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    pmin.int, Position, rank, rbind, Reduce, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which,
    which.max, which.min

Loading required package: Biobase

Welcome to Bioconductor

    Vignettes contain introductory material; view with
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library(limma)
library(Seurat)

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library(monocle)

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library(cowplot)


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library(scran)

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library(NMF)

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library(workflowr)

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library(ggplot2)
library(clustree)

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library(dplyr)


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source("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/code/normCounts.R")
source("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/code/findModes.R")
source("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/code/ggplotColors.R")

Read in the fetal data

targets <- read.delim("/misc/card2-single_cell_nuclei_rnaseq/Porello-heart-snRNAseq/data/targets.txt",header=TRUE, stringsAsFactors = FALSE)
targets$FileName2 <- paste(targets$FileName,"/",sep="")
targets$Group_ID2 <- gsub("LV_","",targets$Group_ID)
group <- c("Fetal_1","Fetal_2","Fetal_3",
           "Young_1","Young_2","Young_3",
           "Adult_1","Adult_2","Adult_3", 
           "Diseased_1","Diseased_2",
           "Diseased_3","Diseased_4")
m <- match(group, targets$Group_ID2)
targets <- targets[m,]

f1 <- Read10X(data.dir = targets$FileName2[targets$Group_ID2=="Fetal_1"])
colnames(f1) <- paste(colnames(f1),"f1",sep="_")
f2 <- Read10X(data.dir = targets$FileName2[targets$Group_ID2=="Fetal_2"])
colnames(f2) <- paste(colnames(f2),"f2",sep="_")
f3 <- Read10X(data.dir = targets$FileName2[targets$Group_ID2=="Fetal_3"])
colnames(f3) <- paste(colnames(f3),"f3",sep="_")

# Combine 3 samples into one big data matrix
allf <- cbind(f1,f2,f3)

Gene filtering

Get gene annotation

I’m using gene annotation information from the org.Hs.eg.db package.

columns(org.Hs.eg.db)

 [1] "ACCNUM"       "ALIAS"        "ENSEMBL"      "ENSEMBLPROT" 
 [5] "ENSEMBLTRANS" "ENTREZID"     "ENZYME"       "EVIDENCE"    
 [9] "EVIDENCEALL"  "GENENAME"     "GO"           "GOALL"       
[13] "IPI"          "MAP"          "OMIM"         "ONTOLOGY"    
[17] "ONTOLOGYALL"  "PATH"         "PFAM"         "PMID"        
[21] "PROSITE"      "REFSEQ"       "SYMBOL"       "UCSCKG"      
[25] "UNIGENE"      "UNIPROT"

ann <- AnnotationDbi:::select(org.Hs.eg.db,keys=rownames(allf),columns=c("SYMBOL","ENTREZID","ENSEMBL","GENENAME","CHR"),keytype = "SYMBOL")

Warning in .deprecatedColsMessage(): Accessing gene location information via 'CHR','CHRLOC','CHRLOCEND'
  is deprecated. Please use a range based accessor like genes(), or
  select() with columns values like TXCHROM and TXSTART on a TxDb or
  OrganismDb object instead.

'select()' returned 1:many mapping between keys and columns

m <- match(rownames(allf),ann$SYMBOL)
ann <- ann[m,]
table(ann$SYMBOL==rownames(allf))


 TRUE 
33939

mito <- grep("mitochondrial",ann$GENENAME)
length(mito)

[1] 226

ribo <- grep("ribosomal",ann$GENENAME)
length(ribo)

[1] 198

missingEZID <- which(is.na(ann$ENTREZID))
length(missingEZID)

[1] 10530

Remove mitochondrial and ribosomal genes and genes with no ENTREZID

These genes are not informative for downstream analysis.

chuck <- unique(c(mito,ribo,missingEZID))
length(chuck)

[1] 10875

allf.keep <- allf[-chuck,]
ann.keep <- ann[-chuck,]
table(ann.keep$SYMBOL==rownames(allf.keep))


 TRUE 
23064

Remove very lowly expressed genes

Removing very lowly expressed genes helps to reduce the noise in the data. Here I am choosing to keep genes with at least 1 count in at least 20 cells. This means that a cluster made up of at least 20 cells can potentially be detected (minimum cluster size = 20 cells).

numzero.genes <- rowSums(allf.keep==0)

#avg.exp <- rowMeans(cpm.DGEList(y.kid,log=TRUE))

#plot(avg.exp,numzero.genes,xlab="Average log-normalised-counts",ylab="Number zeroes per gene")

table(numzero.genes > (ncol(allf.keep)-20))


FALSE  TRUE 
18467  4597

keep.genes <- numzero.genes < (ncol(allf.keep)-20)
table(keep.genes)

keep.genes
FALSE  TRUE 
 4647 18417

allf.keep <- allf.keep[keep.genes,]
dim(allf.keep)

[1] 18417 27760

ann.keep <- ann.keep[keep.genes,]

The total size of the fetal dataset is 27760 cells and 18417 genes.

Remove sex chromosome genes

I will remove the sex chromosome genes before clustering so that the sex doesn’t play a role in determining the clusters.

sexchr <- ann.keep$CHR %in% c("X","Y")

allf.nosex <- allf.keep[!sexchr,]
dim(allf.nosex)

[1] 17723 27760

ann.nosex <- ann.keep[!sexchr,]

Save/load data objects

#save(ann,ann.keep,ann.nosex,allf,allf.keep,allf.nosex,file="./output/RDataObjects/fetalObjs.Rdata")
#load(file="./output/RDataObjects/fetalObjs.Rdata")

Create Seurat objects

biorep <- factor(rep(c("f1","f2","f3"),c(ncol(f1),ncol(f2),ncol(f3))))
names(biorep) <- colnames(allf.keep)
sex <- factor(rep(c("m","m","f"),c(ncol(f1),ncol(f2),ncol(f3))))
names(sex) <- colnames(allf.keep)
age <- rep(c(0.475,0.475,0.5),c(ncol(f1),ncol(f2),ncol(f3)))
names(age) <- colnames(allf.keep)
batch <- rep(c("B2","B1","B2"),c(ncol(f1),ncol(f2),ncol(f3)))
names(batch) <- colnames(allf.keep)

fetal <- CreateSeuratObject(counts = allf.nosex, project = "fetal")
fetal <- AddMetaData(object=fetal, metadata = biorep, col.name="biorep")
fetal <- AddMetaData(object=fetal, metadata = sex, col.name="sex")
fetal <- AddMetaData(object=fetal, metadata = age, col.name="age")
fetal <- AddMetaData(object=fetal, metadata = batch, col.name="batch")

fetal.list <- SplitObject(fetal, split.by = "biorep")

Try new normalisation method: SCTransform

This new method replaces the NormalizeData, FindVariableFeatures and ScaleData functions. It performs regularised negative binomial regression with the total sequencing depth per cell as the covariate (i.e. library size), as well as any other user supplied covariates. The Pearson residuals are then used in downstream analysis.

Upon further reading, it is not clear that this is compatible with the integration method in Seurat (future work by the Seurat team). The method is slower than previous functions but not prohibitively so (~10 minutes for the fetal dataset of 27760 cells).

Although this method isn’t fully compatible with the integration approach, it seemed to contribute to giving the ``best" clustering results following data integration of the three samples.

# This is a bit slow
for (i in 1:length(fetal.list)) {
    fetal.list[[i]] <- SCTransform(fetal.list[[i]], verbose = FALSE)
#    fetal.list[[i]] <- GetResidual(fetal.list[[i]])
}

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Perform the usual normalisation

#for (i in 1:length(fetal.list)) {
#    fetal.list[[i]] <- NormalizeData(fetal.list[[i]], verbose = FALSE)
#    fetal.list[[i]] <- FindVariableFeatures(fetal.list[[i]], selection.method #= "vst", 
#                                            nfeatures = 2000, verbose = FALSE)
#}

Perform integration

There are two steps:

Find integration anchors
Perform integration which should batch-correct the data

The default number of dimensions is 30. Should increase the number of integration anchors from 2000 (default) to 3000 as suggested by Satija lab vignette on SCTransform use.

fetal.anchors <- FindIntegrationAnchors(object.list = fetal.list, dims = 1:30, anchor.features = 3000)

Computing 3000 integration features

Scaling features for provided objects

Finding all pairwise anchors

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 20883 anchors

Filtering anchors

    Retained 7221 anchors

Extracting within-dataset neighbors

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 16600 anchors

Filtering anchors

    Retained 7051 anchors

Extracting within-dataset neighbors

Running CCA

Merging objects

Finding neighborhoods

Finding anchors

    Found 19019 anchors

Filtering anchors

    Retained 8501 anchors

Extracting within-dataset neighbors

fetal.integrated <- IntegrateData(anchorset = fetal.anchors, dims = 1:30)

Merging dataset 3 into 2

Extracting anchors for merged samples

Finding integration vectors

Finding integration vector weights

Integrating data

Merging dataset 1 into 2 3

Extracting anchors for merged samples

Finding integration vectors

Finding integration vector weights

Integrating data

Perform clustering

DefaultAssay(object = fetal.integrated) <- "integrated"

Perform scaling and PCA

fetal.integrated <- ScaleData(fetal.integrated, verbose = FALSE)
fetal.integrated <- RunPCA(fetal.integrated, npcs = 50, verbose = FALSE)
ElbowPlot(fetal.integrated,ndims=50)

Version	Author	Date
c5d9a33	Belinda Phipson	2019-06-20

VizDimLoadings(fetal.integrated, dims = 1:4, reduction = "pca")

Version	Author	Date
c5d9a33	Belinda Phipson	2019-06-20

DimPlot(fetal.integrated, reduction = "pca",group.by="biorep")

Version	Author	Date
c5d9a33	Belinda Phipson	2019-06-20

DimPlot(fetal.integrated, reduction = "pca",group.by="sex")

Version	Author	Date
c5d9a33	Belinda Phipson	2019-06-20

DimPlot(fetal.integrated, reduction = "pca",group.by="batch")

Version	Author	Date
c5d9a33	Belinda Phipson	2019-06-20

DimHeatmap(fetal.integrated, dims = 1:15, cells = 500, balanced = TRUE)

Version	Author	Date
c5d9a33	Belinda Phipson	2019-06-20
5839497	Belinda Phipson	2019-06-07

DimHeatmap(fetal.integrated, dims = 16:30, cells = 500, balanced = TRUE)

Version	Author	Date
c5d9a33	Belinda Phipson	2019-06-20

Perform nearest neighbours clustering

fetal.integrated <- FindNeighbors(fetal.integrated, dims = 1:30)

Computing nearest neighbor graph

Computing SNN

fetal.integrated <- FindClusters(fetal.integrated, resolution = 0.3)

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9444
Number of communities: 22
Elapsed time: 8 seconds

table(Idents(fetal.integrated))


    0     1     2     3     4     5     6     7     8     9    10    11 
10141  2755  2016  1571  1565  1309  1111  1103  1017   940   929   728 
   12    13    14    15    16    17    18    19    20    21 
  604   464   349   311   242   210   127   123   116    29

barplot(table(Idents(fetal.integrated)),ylab="Number of cells",xlab="Clusters")
title("Number of cells in each cluster")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20
16a9bdf	Belinda Phipson	2019-06-17
5839497	Belinda Phipson	2019-06-07

Visualisation with TSNE

set.seed(10)
fetal.integrated <- RunTSNE(fetal.integrated, reduction = "pca", dims = 1:30)

pdf(file="./output/Figures/tsne-fetalALL.pdf",width=10,height=8,onefile = FALSE)
DimPlot(fetal.integrated, reduction = "tsne",label=TRUE,label.size = 6,pt.size = 0.5)+NoLegend()
dev.off()

png 
  2

DimPlot(fetal.integrated, reduction = "tsne",label=TRUE,label.size = 6)+NoLegend()

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20
16a9bdf	Belinda Phipson	2019-06-17
5839497	Belinda Phipson	2019-06-07

DimPlot(fetal.integrated, reduction = "tsne", split.by = "biorep",label=TRUE,label.size = 5)+NoLegend()

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20
16a9bdf	Belinda Phipson	2019-06-17

DimPlot(fetal.integrated, reduction = "tsne", split.by = "sex",label=TRUE,label.size = 5)+NoLegend()

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20

DimPlot(fetal.integrated, reduction = "tsne", group.by = "biorep")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
5839497	Belinda Phipson	2019-06-07

DimPlot(fetal.integrated, reduction = "tsne", group.by = "sex")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20
16a9bdf	Belinda Phipson	2019-06-17
6f2bf8d	Belinda Phipson	2019-06-09

DimPlot(fetal.integrated, reduction = "tsne", group.by = "batch")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
16a9bdf	Belinda Phipson	2019-06-17

#FeaturePlot(fetal.integrated, features = c("XIST"))

par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(fetal.integrated),fetal@meta.data$biorep)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(3),legend=TRUE)

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20
16a9bdf	Belinda Phipson	2019-06-17

Visualisation with clustree

clusres <- c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2)

for(i in 1:length(clusres)){
  fetal.integrated <- FindClusters(fetal.integrated, 
                                   resolution = clusres[i])
}

Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9706
Number of communities: 15
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9566
Number of communities: 18
Elapsed time: 4 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9444
Number of communities: 22
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9353
Number of communities: 24
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9267
Number of communities: 24
Elapsed time: 6 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9189
Number of communities: 27
Elapsed time: 5 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9131
Number of communities: 30
Elapsed time: 7 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9069
Number of communities: 32
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.9020
Number of communities: 35
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8971
Number of communities: 36
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8925
Number of communities: 36
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 27760
Number of edges: 1080857

Running Louvain algorithm...
Maximum modularity in 10 random starts: 0.8877
Number of communities: 38
Elapsed time: 7 seconds

pct.male <- function(x) {mean(x=="m")}
pct.female <- function(x) {mean(x=="f")}
biorep1 <- function(x) {mean(x=="f1")}
biorep2 <- function(x) {mean(x=="f2")}
biorep3 <- function(x) {mean(x=="f3")}

clustree(fetal.integrated, prefix = "integrated_snn_res.")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20

clustree(fetal.integrated, prefix = "integrated_snn_res.",
         node_colour = "biorep", node_colour_aggr = "biorep1",assay="RNA")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20

clustree(fetal.integrated, prefix = "integrated_snn_res.",
         node_colour = "biorep", node_colour_aggr = "biorep2",assay="RNA")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20

clustree(fetal.integrated, prefix = "integrated_snn_res.",
         node_colour = "biorep", node_colour_aggr = "biorep3",assay="RNA")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
16a9bdf	Belinda Phipson	2019-06-17

clustree(fetal.integrated, prefix = "integrated_snn_res.",
         node_colour = "sex", node_colour_aggr = "pct.female",assay="RNA")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
c5d9a33	Belinda Phipson	2019-06-20
16a9bdf	Belinda Phipson	2019-06-17

clustree(fetal.integrated, prefix = "integrated_snn_res.",
         node_colour = "sex", node_colour_aggr = "pct.male",assay="RNA")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04
16a9bdf	Belinda Phipson	2019-06-17

#clustree(fetal.integrated, prefix = "integrated_snn_res.",
#         node_colour = "XIST", node_colour_aggr = "median",
#         assay="RNA")

clustree(fetal.integrated, prefix = "integrated_snn_res.",
         node_colour = "TNNT2", node_colour_aggr = "median",
         assay="RNA")

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04

Save/load Seurat object

#saveRDS(fetal.integrated,file="./output/RDataObjects/fetal-int.Rds")
#fetal.integrated <- readRDS(file="./output/RDataObjects/fetal-int.Rds")

Find Markers

DefaultAssay(fetal.integrated) <- "RNA"
Idents(fetal.integrated) <- fetal.integrated$integrated_snn_res.0.3

par(mfrow=c(1,1))
par(mar=c(4,4,2,2))
tab <- table(Idents(fetal.integrated),fetal.integrated@meta.data$biorep)
barplot(t(tab/rowSums(tab)),beside=TRUE,col=ggplotColors(3),legend=TRUE)

Version	Author	Date
4596c10	Belinda Phipson	2019-07-04

#fetalmarkers <- FindAllMarkers(fetal.integrated, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)

# Limma-trend for DE

y.fetal <- DGEList(allf.keep)
y.fetal$genes <- ann.keep

logcounts <- normCounts(y.fetal,log=TRUE,prior.count=0.5)

maxclust <- length(levels(Idents(fetal.integrated)))-1

grp <- paste("c",Idents(fetal.integrated),sep = "")
grp <- factor(grp,levels = paste("c",0:maxclust,sep=""))

design <- model.matrix(~0+grp)
colnames(design) <- levels(grp)

mycont <- matrix(NA,ncol=length(levels(grp)),nrow=length(levels(grp)))
rownames(mycont)<-colnames(mycont)<-levels(grp)
diag(mycont)<-1
mycont[upper.tri(mycont)]<- -1/(length(levels(factor(grp)))-1)
mycont[lower.tri(mycont)]<- -1/(length(levels(factor(grp)))-1)

fit <- lmFit(logcounts,design)
fit.cont <- contrasts.fit(fit,contrasts=mycont)
fit.cont <- eBayes(fit.cont,trend=TRUE,robust=TRUE)

fit.cont$genes <- ann.keep

summary(decideTests(fit.cont))

          c0    c1    c2    c3    c4    c5    c6    c7    c8    c9   c10
Down    9766  8522  6350  6820 15019  7380  6803  5089  8239  4545  4664
NotSig  4606  6032  6394  6786  2846  6607  7898  7404  7036  8069  9164
Up      4045  3863  5673  4811   552  4430  3716  5924  3142  5803  4589
         c11   c12   c13   c14   c15   c16   c17   c18   c19   c20   c21
Down    7172  4412  1355  4318  3396  4972  2678  1606  8840  2052  1504
NotSig  7269  8603  9829 11320 10915 10013 12716 11264  8404 12756 15483
Up      3976  5402  7233  2779  4106  3432  3023  5547  1173  3609  1430

treat <- treat(fit.cont,lfc=0.5)

dt<-decideTests(treat)

summary(dt)

          c0    c1    c2    c3    c4    c5    c6    c7    c8    c9   c10
Down     328   276   228   411  3009   265   253   395   272   391   319
NotSig 17456 17485 17387 17341 15363 17452 17518 17320 17544 17199 17562
Up       633   656   802   665    45   700   646   702   601   827   536
         c11   c12   c13   c14   c15   c16   c17   c18   c19   c20   c21
Down     739   169     4   328   194   493   136   198  1717   281   374
NotSig 16922 17169 17882 17570 17698 17750 17833 17495 16339 17330 17641
Up       756  1079   531   519   525   174   448   724   361   806   402

par(mfrow=c(3,3))
for(i in 1:ncol(mycont)){
  plotMD(treat,coef=i,status = dt[,i],hl.cex=0.5)
  abline(h=0,col=colours()[c(226)])
  lines(lowess(treat$Amean,treat$coefficients[,i]),lwd=1.5,col=4)
}

Write out marker genes for each cluster

contnames <- colnames(mycont)

for(i in 1:length(contnames)){
  topsig <- topTreat(treat,coef=i,n=Inf,p.value=0.05)
  write.csv(topsig[topsig$logFC>0,],file=paste("./output/MarkerAnalysis/Fetal/Up-Cluster-",contnames[i],".csv",sep=""))
  write.csv(topGO(goana(de=topsig$ENTREZID[topsig$logFC>0],universe=treat$genes$ENTREZID,species="Hs"),number=50),
            file=paste("./output/MarkerAnalysis/Fetal/GO-Cluster-",contnames[i],".csv",sep=""))

}

#write.csv(fetalmarkers,file="./output/AllFetal-clustermarkers.csv")

Heatmap of pre-defined heart cell type markers

hm <- read.delim("./data/heart-markers-long.txt",stringsAsFactors = FALSE)
hgene <- toupper(hm$Gene)
hgene <- unique(hgene)

m <- match(hgene,rownames(logcounts))
m <- m[!is.na(m)]

sam <- factor(fetal.integrated$biorep)
newgrp <- paste(grp,sam,sep=".")
newgrp <- factor(newgrp,levels=paste(rep(levels(grp),each=3),levels(sam),sep="."))
o <-order(newgrp)

annot <- data.frame(CellType=grp,Sample=sam,NewGroup=newgrp)

mycelltypes <- hm$Celltype[match(rownames(logcounts)[m],toupper(hm$Gene))]
mycelltypes <- factor(mycelltypes)

mygenes <- rownames(logcounts)[m]
mygenelab <- paste(mygenes,mycelltypes,sep="_")

myColors <- list(Clust=NA,Sample=NA,Celltypes=NA)
myColors$Clust<-ggplotColors(22)
names(myColors$Clust)<-levels(grp)
myColors$Sample <- brewer.pal(3, "Set1")
names(myColors$Sample) <- levels(sam)
myColors$Celltypes <- ggplotColors(24)
names(myColors$Celltypes) <- levels(mycelltypes) 

pdf(file="./output/Figures/fetal-heatmap-hmarkers.pdf",width=20,height=15,onefile = FALSE)
aheatmap(logcounts[m,o],Rowv=NA,Colv=NA,labRow=mygenelab,labCol=NA,
         annCol=list(Clust=grp[o],Sample=sam[o]),
         annRow = list(Celltypes=mycelltypes),
         annColors = myColors, 
         fontsize=16,color="-RdYlBu")
dev.off()

png 
  2

Summarise expression across cells

sumexpr <- matrix(NA,nrow=nrow(logcounts),ncol=length(levels(newgrp)))
rownames(sumexpr) <- rownames(logcounts)
colnames(sumexpr) <- levels(newgrp)

for(i in 1:nrow(sumexpr)){
  sumexpr[i,] <- tapply(logcounts[i,],newgrp,median)
}

clust <- rep(levels(grp),each=3)
samps <- rep(levels(sam),22)
pdf(file="./output/Figures/fetal-heatmap-hmarkers-summarised.pdf",width=20,height=15,onefile = FALSE)
aheatmap(sumexpr[m,],Rowv = NA,Colv = NA, labRow = mygenelab,
         annCol=list(Clust=clust,Sample=samps),
         annRow=list(Celltypes=mycelltypes),
         annColors=myColors,
         fontsize=16,color="-RdYlBu")
dev.off()

png 
  2

aheatmap(sumexpr[m,],Rowv = NA,Colv = NA, labRow = mygenelab,
         annCol=list(Clust=clust,Sample=samps),
#         annRow=list(Celltypes=mycelltypes),
         annColors=myColors,
         fontsize=12,color="-RdYlBu")

sig.genes <- gene.label <- vector("list", length(levels(grp)))
for(i in 1:length(sig.genes)){
  top <- topTreat(treat,coef=i,n=1000)
  sig.genes[[i]] <- rownames(top)[top$logFC>0][1:5]
  gene.label[[i]] <- paste(rownames(top)[top$logFC>0][1:5],levels(grp)[i],sep="-")
} 

csig <- unlist(sig.genes)
genes <- unlist(gene.label)

pdf(file="./output/Figures/fetal-heatmap-siggenes-summarised.pdf",width=20,height=15,onefile = FALSE)
aheatmap(sumexpr[csig,],Rowv = NA,Colv = NA, labRow = genes,
         annCol=list(Clust=clust,Sample=samps),
         annColors=myColors,
         fontsize=16,color="-RdYlBu",
         scale="none")
dev.off()

png 
  2

aheatmap(sumexpr[csig,],Rowv = NA,Colv = NA, labRow = genes,
         annCol=list(Clust=clust,Sample=samps),
         annColors=myColors,
         fontsize=16,color="-RdYlBu",
         scale="none")

Assign cell types to clusters

Evangelyn and Enzo annotated the clusters to cell types using a variety of information available including:

Heatmap of known marker genes from literature
Lists of marker genes for each cluster
GO analysis of marker genes
tSNEs with various covariates highlighted

#fetal.integrated <- readRDS(file="./output/RDataObjects/fetal-int.Rds")
#DefaultAssay(fetal.integrated) <- "RNA"
#Idents(fetal.integrated) <- fetal.integrated$integrated_snn_res.0.3

fetal.annot <- fetal.integrated
new.cluster.ids <- c("Cardiomyocytes1","Cardiomyocyte (conduction)","Proliferative CM1",
                     "Fibroblast","Vascular (endothelial?) (Female)","Cardiomyocytes (Male)",
                     "Cardiomyocytes (Female)","Fibroblast1","Cardiomyocytes2","Endothelial cells",
                     "Epicardial-like2","Macrophages","Proliferative CM2","Epicardial-like1",
                     "Neurons","Fibroblast (Female?)","Proliferative CM (Male)","Smooth muscle cells",
                     "Endothelial cells/ Immune?","Erythroid","Lymphatic endothelial cells","Mast cells")

  
  
names(new.cluster.ids) <- levels(fetal.annot)
fetal.annot <- RenameIdents(fetal.annot, new.cluster.ids)
DimPlot(fetal.annot, reduction = "tsne", label = TRUE, pt.size = 0.5) + NoLegend()

Create list of marker genes for GST purposes

sig.genes.gst <- vector("list", length(levels(grp)))
names(sig.genes.gst) <- levels(fetal.annot)
for(i in 1:length(sig.genes.gst)){
  top <- topTreat(treat,coef=i,n=Inf,p.value=0.05)
  sig.genes.gst[[i]] <- rownames(top)[top$logFC>0]
} 
save(sig.genes.gst,file="./data/gstlist-fetal.Rdata")

sessionInfo()

R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS release 6.7 (Final)

Matrix products: default
BLAS:   /usr/local/installed/R/3.6.0/lib64/R/lib/libRblas.so
LAPACK: /usr/local/installed/R/3.6.0/lib64/R/lib/libRlapack.so

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] splines   parallel  stats4    stats     graphics  grDevices utils    
 [8] datasets  methods   base     

other attached packages:
 [1] dplyr_0.8.1                 clustree_0.4.0             
 [3] ggraph_1.0.2                workflowr_1.3.0            
 [5] NMF_0.21.0                  bigmemory_4.5.33           
 [7] cluster_2.0.9               rngtools_1.3.1.1           
 [9] pkgmaker_0.27               registry_0.5-1             
[11] scran_1.12.0                SingleCellExperiment_1.6.0 
[13] SummarizedExperiment_1.14.0 GenomicRanges_1.36.0       
[15] GenomeInfoDb_1.20.0         DelayedArray_0.10.0        
[17] BiocParallel_1.18.0         matrixStats_0.54.0         
[19] cowplot_0.9.4               monocle_2.12.0             
[21] DDRTree_0.1.5               irlba_2.3.3                
[23] VGAM_1.1-1                  ggplot2_3.1.1              
[25] Matrix_1.2-17               Seurat_3.0.2               
[27] org.Hs.eg.db_3.8.2          AnnotationDbi_1.46.0       
[29] IRanges_2.18.0              S4Vectors_0.22.0           
[31] Biobase_2.44.0              BiocGenerics_0.30.0        
[33] RColorBrewer_1.1-2          edgeR_3.26.3               
[35] limma_3.40.2               

loaded via a namespace (and not attached):
  [1] reticulate_1.12          R.utils_2.8.0           
  [3] tidyselect_0.2.5         RSQLite_2.1.1           
  [5] htmlwidgets_1.3          grid_3.6.0              
  [7] combinat_0.0-8           docopt_0.6.1            
  [9] Rtsne_0.15               munsell_0.5.0           
 [11] codetools_0.2-16         ica_1.0-2               
 [13] statmod_1.4.30           future_1.12.0           
 [15] withr_2.1.2              colorspace_1.4-1        
 [17] fastICA_1.2-1            knitr_1.22              
 [19] ROCR_1.0-7               gbRd_0.4-11             
 [21] listenv_0.7.0            Rdpack_0.11-0           
 [23] labeling_0.3             git2r_0.25.2            
 [25] slam_0.1-45              GenomeInfoDbData_1.2.1  
 [27] polyclip_1.10-0          bit64_0.9-7             
 [29] farver_1.1.0             pheatmap_1.0.12         
 [31] rprojroot_1.3-2          xfun_0.6                
 [33] R6_2.4.0                 doParallel_1.0.14       
 [35] ggbeeswarm_0.6.0         rsvd_1.0.0              
 [37] locfit_1.5-9.1           bitops_1.0-6            
 [39] assertthat_0.2.1         SDMTools_1.1-221.1      
 [41] scales_1.0.0             beeswarm_0.2.3          
 [43] gtable_0.3.0             npsurv_0.4-0            
 [45] globals_0.12.4           tidygraph_1.1.2         
 [47] rlang_0.3.4              lazyeval_0.2.2          
 [49] checkmate_1.9.3          yaml_2.2.0              
 [51] reshape2_1.4.3           backports_1.1.4         
 [53] tools_3.6.0              gridBase_0.4-7          
 [55] gplots_3.0.1.1           dynamicTreeCut_1.63-1   
 [57] ggridges_0.5.1           Rcpp_1.0.1              
 [59] plyr_1.8.4               zlibbioc_1.30.0         
 [61] purrr_0.3.2              RCurl_1.95-4.12         
 [63] densityClust_0.3         pbapply_1.4-0           
 [65] viridis_0.5.1            zoo_1.8-5               
 [67] ggrepel_0.8.1            fs_1.3.1                
 [69] magrittr_1.5             data.table_1.11.6       
 [71] lmtest_0.9-37            RANN_2.6.1              
 [73] whisker_0.3-2            fitdistrplus_1.0-14     
 [75] lsei_1.2-0               evaluate_0.13           
 [77] xtable_1.8-4             sparsesvd_0.1-4         
 [79] gridExtra_2.3            HSMMSingleCell_1.4.0    
 [81] compiler_3.6.0           scater_1.12.2           
 [83] tibble_2.1.1             KernSmooth_2.23-15      
 [85] crayon_1.3.4             R.oo_1.22.0             
 [87] htmltools_0.3.6          tidyr_0.8.3             
 [89] DBI_1.0.0                tweenr_1.0.1            
 [91] MASS_7.3-51.4            R.methodsS3_1.7.1       
 [93] gdata_2.18.0             metap_1.1               
 [95] igraph_1.2.4.1           pkgconfig_2.0.2         
 [97] bigmemory.sri_0.1.3      plotly_4.9.0            
 [99] foreach_1.4.4            vipor_0.4.5             
[101] dqrng_0.2.1              XVector_0.24.0          
[103] bibtex_0.4.2             stringr_1.4.0           
[105] digest_0.6.18            sctransform_0.2.0       
[107] tsne_0.1-3               rmarkdown_1.12.6        
[109] DelayedMatrixStats_1.6.0 gtools_3.8.1            
[111] nlme_3.1-139             jsonlite_1.6            
[113] BiocNeighbors_1.2.0      viridisLite_0.3.0       
[115] pillar_1.3.1             lattice_0.20-38         
[117] GO.db_3.8.2              httr_1.4.0              
[119] survival_2.44-1.1        glue_1.3.1              
[121] qlcMatrix_0.9.7          FNN_1.1.3               
[123] png_0.1-7                iterators_1.0.10        
[125] bit_1.1-14               ggforce_0.2.2           
[127] stringi_1.4.3            blob_1.1.1              
[129] BiocSingular_1.0.0       caTools_1.17.1.2        
[131] memoise_1.1.0            future.apply_1.2.0      
[133] ape_5.3

Clustering the fetal samples

Belinda Phipson

6/6/2019