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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/
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library(Seurat)
library(harmony)
library(ggplot2)
library(DataCombine)
library(here)
library(RColorBrewer)
options(future.globals.maxSize= 15000*1024^2) #allow global exceeding 4Gb
Read in the files, add metadata, and create an object list
filelist<-list.files(here::here('output/sampleQCrds/'), full.names = T)
objectlist<- list()
for (i in 1:length(filelist)){
rds<- readRDS(filelist[i])
objectlist[i]<- rds
}
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create a merged seurat object
ids<-substr(basename(filelist),1,12)
merged<- merge(objectlist[[1]], c(objectlist[[2]], objectlist[[3]],objectlist[[4]],objectlist[[5]],objectlist[[6]],objectlist[[7]],objectlist[[8]],objectlist[[9]],objectlist[[10]],objectlist[[11]],objectlist[[12]],objectlist[[13]],objectlist[[14]],objectlist[[15]],objectlist[[16]]),add.cell.ids=ids, merge.data=T)
#need to fix the individual names because they are slightly different from batch1
replacements<- data.frame(from= c("SNG-NA18511.variant2", "SNG-NA18858.variant2", "SNG-NA19160.variant2"), to=c("SNG-NA18511", "SNG-NA18858", "SNG-NA19160"))
merged@meta.data<-FindReplace(merged@meta.data, "individual", replacements, from = "from", to= "to", exact=T, vector=F )
Only exact matches will be replaced.
merged <- SCTransform(merged, variable.features.n = 5000, vars.to.regress = c("Batch","individual"))
Calculating cell attributes for input UMI matrix
Variance stabilizing transformation of count matrix of size 18065 by 42488
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 42488 cells
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|
|================== | 25%
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|
|========================== | 38%
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|=================================== | 50%
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|==================================================== | 75%
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|
|============================================================= | 88%
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Calculating gene attributes
Wall clock passed: Time difference of 16.86706 mins
Determine variable features
Set 5000 variable features
Place corrected count matrix in counts slot
Regressing out Batch, individual
Centering data matrix
Set default assay to SCT
#run PCA on full dataset pre-alignment
all.genes= rownames(merged)
merged<-FindVariableFeatures(merged,selection.method="vst", nfeatures = 5000)
#have previously used all genes (nfeatures=25000) and clustering by individual rather than batch (based on proportion of cells per cluster) was still observed downstream. Now using 5000 because it is the upper bound of what has been recommended in the literature.
merged<- ScaleData(merged, features = all.genes)
Centering and scaling data matrix
merged<-RunPCA(merged, npcs = 100, verbose=F)
DimPlot(merged, reduction = "pca", group.by = "Batch")
Now, running harmony to integrate. Here, using Batch, SampleID(10x Lane), and individual to integrate. Since Batch and Lane are confounded, this may over correct for Batch.
merged<- RunHarmony(merged, c("Batch", "individual"), plot_convergence = T, assay.use = "SCT")
Harmony 1/10
Harmony 2/10
Harmony 3/10
Harmony converged after 3 iterations
Warning: Invalid name supplied, making object name syntactically valid. New
object name is Seurat..ProjectDim.SCT.harmony; see ?make.names for more details
on syntax validity
Visualize Harmony embeddings
DimPlot(merged, reduction="harmony", group.by= c("individual", "Batch"), combine=F)
[[1]]
[[2]]
Now Running UMAP and identifying clusters, etc
merged<- RunUMAP(merged, reduction = "harmony", dims = 1:100, verbose = F)
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
merged<- FindNeighbors(merged, reduction="harmony", dims = 1:100, verbose = F)
merged<- FindClusters(merged, resolution=1, verbose = F)
merged<- FindClusters(merged, resolution=0.8, verbose = F)
merged<- FindClusters(merged, resolution=0.5, verbose = F)
merged<- FindClusters(merged, resolution=0.1, verbose = F)
SAVING merged/aligned/reclustered object
path<- here::here("output/mergedObjects/")
saveRDS(merged, file=paste0(path,'SCTregress.Batchindividual.Harmony.Batchindividual.rds'))
#reassign idents
Idents(merged)<- 'SCT_snn_res.1'
VizDimLoadings(merged, dims = 1:2, reduction = "harmony")
VizDimLoadings(merged, dims = 3:4, reduction = "harmony")
VizDimLoadings(merged, dims = 5:6, reduction = "harmony")
xlim <- c(min(merged@reductions$harmony@cell.embeddings[,'harmony_1']),
max(merged@reductions$harmony@cell.embeddings[,'harmony_1']))
ylim <- c(min(merged@reductions$harmony@cell.embeddings[,'harmony_2']),
max(merged@reductions$harmony@cell.embeddings[,'harmony_2']))
individuals <- table(merged$individual)
individuals <- individuals[individuals>50]
individuals <- names(individuals)
for (i in individuals)
{
print(DimPlot(merged, reduction = "harmony", group.by = c("Batch"), pt.size = 0.01,
cells = WhichCells(merged, expression = individual == i)) +
xlim(xlim) + ylim(ylim) + ggtitle(i))
}
DimPlot(merged, reduction = "umap")
DimPlot(merged, reduction = "umap", group.by = "Batch")
DimPlot(merged, reduction = "umap", group.by = "individual")
xlim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_1']),
max(merged@reductions$umap@cell.embeddings[,'UMAP_1']))
ylim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_2']),
max(merged@reductions$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
print(DimPlot(merged, reduction = "umap",
cells = WhichCells(merged, expression = individual == i)) +
xlim(xlim) + ylim(ylim) + ggtitle(i))
}
plots2<- DimPlot(merged, group.by = "individual", split.by = "Batch")
plots2
DimPlot(merged, group.by = "Batch", split.by = c("individual"))
DimPlot(merged, group.by = "SCT_snn_res.1", split.by = c("Batch"), label=T)
DimPlot(merged, reduction = "harmony", group.by = "SCT_snn_res.1", split.by = "Batch", combine = F)
[[1]]
VlnPlot(merged, features = c("POU5F1", "PAX6", "TNNT2", "SOX17", "HAND1", "LUM"), ncol=2)
#pluripotent markers
FeaturePlot(merged, features = c("POU5F1", "SOX2", "NANOG"), pt.size = 0.2, ncol=3)
#Endoderm markers (first 3 definitive endo, 4-6 liver markers, )
FeaturePlot(merged, features = c("SOX17","CLDN6","FOXA2", "TTR", "AFP", "FGB"), pt.size = 0.2, combine = F)
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
#Mesoderm Markers (first 3 early meso markers, 4-6 heart markers, 7-9 endothelial markers (which comes from mesoderm), then some other general muscle markers)
FeaturePlot(merged, features = c("HAND1", "BMP4", "TNNT2","KDR", "GNG11", "ECSCR", "COL3A1", "ACTC1"), pt.size = 0.2, combine=F)
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
#Ectoderm Markers (3-1 early ectoderm markers, 4-6schwann cell (myelinating, non myelinating, or precursor), 7-8 oligodendrocytes, 9-10 radial glia)
FeaturePlot(merged, features = c("PAX6", "GBX2", "NES", "MPZ", "SOX10","GAP43", "OLIG1", "OLIG2", "VIM", "HES5"), pt.size = 0.2, ncol=3, combine=F)
[[1]]
[[2]]
[[3]]
[[4]]
[[5]]
[[6]]
[[7]]
[[8]]
[[9]]
[[10]]
#More ectoderm, specifically neurons
#immature neurons: NEUROD1
#Mature Neurons: MAP2, SYP
#dopaminergic: TH, FOXA2,
FeaturePlot(merged, features = c("MAP2", "SYP","NEUROD1", "TH" ), pt.size = 0.2, ncol=3)
Identify cluster markers
#how many cells per cluster?
t1<-table(merged@meta.data$SCT_snn_res.1, merged@meta.data$Batch)
t1
Batch1 Batch2 Batch3
0 3386 1805 2758
1 2178 1180 1055
10 533 332 342
11 471 142 551
12 613 215 321
13 165 260 496
14 334 207 321
15 384 138 280
16 17 49 667
17 264 244 211
18 37 81 594
19 104 120 371
2 2017 896 935
20 289 169 39
21 97 75 266
22 233 62 98
23 104 59 135
24 107 36 111
25 100 10 25
3 1248 928 1566
4 1359 437 858
5 1206 407 936
6 472 954 882
7 378 322 693
8 247 279 855
9 530 458 384
#percent of cells in each cluster per batch
t1colsum<- colSums(t1)
percT1<-t1/t1colsum
percT1
Batch1 Batch2 Batch3
0 0.2006756356 0.1146031746 0.2795742524
1 0.2207805373 0.0699342144 0.0669841270
10 0.0338412698 0.0336543335 0.0202690689
11 0.0279144195 0.0090158730 0.0558540294
12 0.0621388748 0.0127422509 0.0203809524
13 0.0104761905 0.0263558033 0.0293960766
14 0.0197949387 0.0131428571 0.0325392803
15 0.0389254942 0.0081787471 0.0177777778
16 0.0010793651 0.0049670552 0.0395306110
17 0.0156462988 0.0154920635 0.0213887481
18 0.0037506336 0.0048005690 0.0377142857
19 0.0066031746 0.0121642169 0.0219877911
2 0.1195400936 0.0568888889 0.0947795236
20 0.0292954891 0.0100160019 0.0024761905
21 0.0061587302 0.0076026356 0.0157648314
22 0.0138090440 0.0039365079 0.0099341105
23 0.0105423213 0.0034967107 0.0085714286
24 0.0067936508 0.0036492651 0.0065785575
25 0.0059266283 0.0006349206 0.0025342119
3 0.1265078561 0.0549991110 0.0994285714
4 0.0862857143 0.0442980233 0.0508504712
5 0.0714751378 0.0258412698 0.0948808920
6 0.0478459199 0.0565400344 0.0560000000
7 0.0240000000 0.0326406488 0.0410715344
8 0.0146387720 0.0177142857 0.0866700456
9 0.0537252914 0.0271439578 0.0243809524
heatmap(t(percT1))
#how many cells per cluster from each individual?
t2<-table(merged@meta.data$SCT_snn_res.1, merged@meta.data$individual)
t2
SNG-NA18511 SNG-NA18858 SNG-NA19160
0 282 7549 118
1 2985 166 1262
10 113 999 95
11 89 10 1065
12 139 21 989
13 403 99 419
14 591 36 235
15 61 714 27
16 480 7 246
17 440 59 220
18 406 6 300
19 155 14 426
2 1825 316 1707
20 127 356 14
21 123 8 307
22 82 133 178
23 90 4 204
24 23 220 11
25 0 135 0
3 239 3393 110
4 820 36 1798
5 281 37 2231
6 126 2020 162
7 651 201 541
8 525 39 817
9 893 92 387
t2colsums<-colSums(t2)
percT2<- t2/t2colsums
percT2
SNG-NA18511 SNG-NA18858 SNG-NA19160
0 0.0236003013 0.5443074483 0.0070785843
1 0.1790641872 0.0138923759 0.0909943038
10 0.0081476675 0.0599280144 0.0079504561
11 0.0074483220 0.0007210325 0.0638872226
12 0.0083383323 0.0017574692 0.0713101161
13 0.0290576105 0.0059388122 0.0350656959
14 0.0494602059 0.0025957171 0.0140971806
15 0.0036592681 0.0597539543 0.0019467878
16 0.0346095609 0.0004199160 0.0205874969
17 0.0368231651 0.0042540919 0.0131973605
18 0.0243551290 0.0005021341 0.0216309756
19 0.0111760040 0.0008398320 0.0356515190
2 0.1527324462 0.0227846276 0.1023995201
20 0.0076184763 0.0297932881 0.0010094455
21 0.0088687000 0.0004799040 0.0256925266
22 0.0068624990 0.0095897325 0.0106778644
23 0.0053989202 0.0003347560 0.0147090634
24 0.0016583748 0.0131973605 0.0009205791
25 0.0000000000 0.0097339390 0.0000000000
3 0.0143371326 0.2839568165 0.0079313577
4 0.0591246665 0.0021595681 0.1504728429
5 0.0235166123 0.0026678203 0.1338332334
6 0.0075584883 0.1690518035 0.0116807268
7 0.0469392170 0.0120575885 0.0452757553
8 0.0439367311 0.0028120268 0.0490101980
9 0.0535692861 0.0076993891 0.0279039585
heatmap(t(percT2))
cormat<-round(cor(percT2),2)
library(reshape2)
melted_cormat<-melt(cormat)
ugly<-ggplot(data= melted_cormat, aes(x=Var1, y=Var2, fill=value)) +
geom_tile() +
ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster")
get_lower_tri<- function(cormat){
cormat[upper.tri(cormat)]<-NA
return(cormat)
}
lower_tri<- get_lower_tri(cormat)
melted_tri<- melt(lower_tri)
pretty<-ggplot(data= melted_tri, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white") +
scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
theme_minimal() +
ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster")
pretty
#exploring similarity in the number of cells per individual between batches
merged.Batch1<- (subset(merged, Batch == "Batch1"))
b1t<- table(merged.Batch1$SCT_snn_res.1, merged.Batch1$individual)
remove("merged.Batch1")
b1tcolsums<- colSums(b1t)
percb1t<- b1t/b1tcolsums
merged.Batch2<- (subset(merged, Batch == "Batch2"))
b2t<- table(merged.Batch2$SCT_snn_res.1, merged.Batch2$individual)
remove("merged.Batch2")
b2tcolsums<- colSums(b2t)
percb2t<- b2t/b2tcolsums
merged.Batch3<- (subset(merged, Batch == "Batch3"))
b3t<- table(merged.Batch3$SCT_snn_res.1, merged.Batch3$individual)
remove("merged.Batch3")
b3tcolsums<- colSums(b3t)
percb3t<- b3t/b3tcolsums
cols1<- c("Batch1_18511","Batch1_18858","Batch1_19160", "Batch2_18511", "Batch2_18858","Batch2_19160",
"Batch3_18511","Batch3_18858", "Batch3_19160")
cols2<- c("Batch1_18511", "Batch2_18511", "Batch3_18511","Batch1_18858", "Batch2_18858", "Batch3_18858","Batch1_19160", "Batch2_19160", "Batch3_19160")
fullpercs<- as.data.frame(cbind(percb1t[,1:3], percb2t,percb3t))
colnames(fullpercs)<-cols1
fullpercs<- cbind(fullpercs$Batch1_18511, fullpercs$Batch2_18511, fullpercs$Batch3_18511,
fullpercs$Batch1_18858, fullpercs$Batch2_18858, fullpercs$Batch3_18858,
fullpercs$Batch1_19160, fullpercs$Batch2_19160, fullpercs$Batch3_19160)
colnames(fullpercs)<-cols2
fullpercs_cor<- round(cor(fullpercs),2)
fullpercs_melt<- melt(fullpercs_cor)
ggplot(data= fullpercs_melt, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white") +
scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 30, hjust=1))+
ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster (res 1)")
#now clustering individual_Batch samples with hierarchical clustering/they will get reordered based on similarity
beauty<- colorRampPalette(brewer.pal(9,"Purples"))(200)
rownames(fullpercs)<- c(0:(nrow(fullpercs)-1))
heatmap(as.matrix(fullpercs), scale="none", col=beauty, cexCol = .7, cexRow=.6)
text(1:ncol(fullpercs),labels=names(fullpercs),srt=30)
#generate a heatmap of the proportion of cells from each individual_batch in each seurat cluster. dendrograms based on similarity of the vectors. should be colored by the value(proportion), but some of the cluster/sample values to seem to match with the color
Reclustering with less resolution, check if everything is robust
#reassign idents
Idents(merged)<- 'SCT_snn_res.0.5'
DimPlot(merged, reduction = "umap")
DimPlot(merged, reduction = "umap", group.by = "Batch")
DimPlot(merged, reduction = "umap", group.by = "individual")
xlim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_1']),
max(merged@reductions$umap@cell.embeddings[,'UMAP_1']))
ylim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_2']),
max(merged@reductions$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
print(DimPlot(merged, reduction = "umap",
cells = WhichCells(merged, expression = individual == i)) +
xlim(xlim) + ylim(ylim) + ggtitle(i))
}
#exploring similarity in the number of cells per individual between batches
merged.Batch1<- (subset(merged, Batch == "Batch1"))
b1t<- table(merged.Batch1$SCT_snn_res.0.5, merged.Batch1$individual)
remove("merged.Batch1")
b1tcolsums<- colSums(b1t)
percb1t<- b1t/b1tcolsums
merged.Batch2<- (subset(merged, Batch == "Batch2"))
b2t<- table(merged.Batch2$SCT_snn_res.0.5, merged.Batch2$individual)
remove("merged.Batch2")
b2tcolsums<- colSums(b2t)
percb2t<- b2t/b2tcolsums
merged.Batch3<- (subset(merged, Batch == "Batch3"))
b3t<- table(merged.Batch3$SCT_snn_res.0.5, merged.Batch3$individual)
remove("merged.Batch3")
b3tcolsums<- colSums(b3t)
percb3t<- b3t/b3tcolsums
cols1<- c("Batch1_18511","Batch1_18858","Batch1_19160", "Batch2_18511", "Batch2_18858","Batch2_19160",
"Batch3_18511","Batch3_18858", "Batch3_19160")
cols2<- c("Batch1_18511", "Batch2_18511", "Batch3_18511","Batch1_18858", "Batch2_18858", "Batch3_18858","Batch1_19160", "Batch2_19160", "Batch3_19160")
fullpercs<- as.data.frame(cbind(percb1t[,1:3], percb2t,percb3t))
colnames(fullpercs)<-cols1
fullpercs<- cbind(fullpercs$Batch1_18511, fullpercs$Batch2_18511, fullpercs$Batch3_18511,
fullpercs$Batch1_18858, fullpercs$Batch2_18858, fullpercs$Batch3_18858,
fullpercs$Batch1_19160, fullpercs$Batch2_19160, fullpercs$Batch3_19160)
colnames(fullpercs)<-cols2
fullpercs_cor<- round(cor(fullpercs),2)
fullpercs_melt<- melt(fullpercs_cor)
ggplot(data= fullpercs_melt, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white") +
scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 30, hjust=1))+
ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster\ncluster res. 0.5")
#now clustering individual_Batch samples with hierarchical clustering/they will get reordered based on similarity
beauty<- colorRampPalette(brewer.pal(9,"Purples"))(200)
rownames(fullpercs)<- c(0:(nrow(fullpercs)-1))
heatmap(as.matrix(fullpercs), scale="none", col=beauty, cexCol = .7, cexRow=.6)
text(1:ncol(fullpercs),labels=names(fullpercs),srt=30)
#generate a heatmap of the raw proportion of cells from each individual_batch in each seurat cluster. dendrograms based on similarity of the vectors. should be colored by the value(proportion), but some of the cluster/sample values to seem to match with the color
#reassign idents
Idents(merged)<- 'SCT_snn_res.0.1'
DimPlot(merged, reduction = "umap")
DimPlot(merged, reduction = "umap", group.by = "Batch")
DimPlot(merged, reduction = "umap", group.by = "individual")
xlim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_1']),
max(merged@reductions$umap@cell.embeddings[,'UMAP_1']))
ylim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_2']),
max(merged@reductions$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
print(DimPlot(merged, reduction = "umap",
cells = WhichCells(merged, expression = individual == i)) +
xlim(xlim) + ylim(ylim) + ggtitle(i))
}
#exploring similarity in the number of cells per individual between batches
merged.Batch1<- (subset(merged, Batch == "Batch1"))
b1t<- table(merged.Batch1$SCT_snn_res.0.1, merged.Batch1$individual)
remove("merged.Batch1")
b1tcolsums<- colSums(b1t)
percb1t<- b1t/b1tcolsums
merged.Batch2<- (subset(merged, Batch == "Batch2"))
b2t<- table(merged.Batch2$SCT_snn_res.0.1, merged.Batch2$individual)
remove("merged.Batch2")
b2tcolsums<- colSums(b2t)
percb2t<- b2t/b2tcolsums
merged.Batch3<- (subset(merged, Batch == "Batch3"))
b3t<- table(merged.Batch3$SCT_snn_res.0.1, merged.Batch3$individual)
remove("merged.Batch3")
b3tcolsums<- colSums(b3t)
percb3t<- b3t/b3tcolsums
cols1<- c("Batch1_18511","Batch1_18858","Batch1_19160", "Batch2_18511", "Batch2_18858","Batch2_19160",
"Batch3_18511","Batch3_18858", "Batch3_19160")
cols2<- c("Batch1_18511", "Batch2_18511", "Batch3_18511","Batch1_18858", "Batch2_18858", "Batch3_18858","Batch1_19160", "Batch2_19160", "Batch3_19160")
fullpercs<- as.data.frame(cbind(percb1t[,1:3], percb2t,percb3t))
colnames(fullpercs)<-cols1
fullpercs<- cbind(fullpercs$Batch1_18511, fullpercs$Batch2_18511, fullpercs$Batch3_18511,
fullpercs$Batch1_18858, fullpercs$Batch2_18858, fullpercs$Batch3_18858,
fullpercs$Batch1_19160, fullpercs$Batch2_19160, fullpercs$Batch3_19160)
colnames(fullpercs)<-cols2
fullpercs_cor<- round(cor(fullpercs),2)
fullpercs_melt<- melt(fullpercs_cor)
ggplot(data= fullpercs_melt, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white") +
scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 30, hjust=1))+
ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster\ncluster res. 0.1")
#now clustering individual_Batch samples with hierarchical clustering/they will get reordered based on similarity
beauty<- colorRampPalette(brewer.pal(9,"Purples"))(200)
#fullnonorm<- as.data.frame(cbind(b1t[,1:3], b2t,b3t))
#colnames(fullnonorm)<-cols1
#heatmap((as.matrix(fullnonorm)), scale="column", col= beauty)
rownames(fullpercs)<- c(0:(nrow(fullpercs)-1))
heatmap(as.matrix(fullpercs), scale="none", col=beauty, cexCol = .7, cexRow=.6)
text(1:ncol(fullpercs),labels=names(fullpercs),srt=30)
#generate a heatmap of the raw proportion of cells from each individual_batch in each seurat cluster. dendrograms based on similarity of the vectors. should be colored by the value(proportion), but some of the cluster/sample values to seem to match with the color
#reassign idents
Idents(merged)<- 'SCT_snn_res.0.8'
DimPlot(merged, reduction = "umap")
DimPlot(merged, reduction = "umap", group.by = "Batch")
DimPlot(merged, reduction = "umap", group.by = "individual")
xlim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_1']),
max(merged@reductions$umap@cell.embeddings[,'UMAP_1']))
ylim <- c(min(merged@reductions$umap@cell.embeddings[,'UMAP_2']),
max(merged@reductions$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
print(DimPlot(merged, reduction = "umap",
cells = WhichCells(merged, expression = individual == i)) +
xlim(xlim) + ylim(ylim) + ggtitle(i))
}
#exploring similarity in the number of cells per individual between batches
merged.Batch1<- (subset(merged, Batch == "Batch1"))
b1t<- table(merged.Batch1$SCT_snn_res.0.8, merged.Batch1$individual)
remove("merged.Batch1")
b1tcolsums<- colSums(b1t)
percb1t<- b1t/b1tcolsums
merged.Batch2<- (subset(merged, Batch == "Batch2"))
b2t<- table(merged.Batch2$SCT_snn_res.0.8, merged.Batch2$individual)
remove("merged.Batch2")
b2tcolsums<- colSums(b2t)
percb2t<- b2t/b2tcolsums
merged.Batch3<- (subset(merged, Batch == "Batch3"))
b3t<- table(merged.Batch3$SCT_snn_res.0.8, merged.Batch3$individual)
remove("merged.Batch3")
b3tcolsums<- colSums(b3t)
percb3t<- b3t/b3tcolsums
cols1<- c("Batch1_18511","Batch1_18858","Batch1_19160", "Batch2_18511", "Batch2_18858","Batch2_19160",
"Batch3_18511","Batch3_18858", "Batch3_19160")
cols2<- c("Batch1_18511", "Batch2_18511", "Batch3_18511","Batch1_18858", "Batch2_18858", "Batch3_18858","Batch1_19160", "Batch2_19160", "Batch3_19160")
fullpercs<- as.data.frame(cbind(percb1t[,1:3], percb2t,percb3t))
colnames(fullpercs)<-cols1
fullpercs<- cbind(fullpercs$Batch1_18511, fullpercs$Batch2_18511, fullpercs$Batch3_18511,
fullpercs$Batch1_18858, fullpercs$Batch2_18858, fullpercs$Batch3_18858,
fullpercs$Batch1_19160, fullpercs$Batch2_19160, fullpercs$Batch3_19160)
colnames(fullpercs)<-cols2
fullpercs_cor<- round(cor(fullpercs),2)
fullpercs_melt<- melt(fullpercs_cor)
ggplot(data= fullpercs_melt, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white") +
scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
theme_minimal() +
ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster\ncluster res. 0.8")
#now clustering individual_Batch samples with hierarchical clustering/they will get reordered based on similarity
beauty<- colorRampPalette(brewer.pal(9,"Purples"))(200)
rownames(fullpercs)<- c(0:(nrow(fullpercs)-1))
heatmap(as.matrix(fullpercs), scale="none", col=beauty, cexCol = .7, cexRow=.6)
text(1:ncol(fullpercs),labels=names(fullpercs),srt=30)
#generate a heatmap of the raw proportion of cells from each individual_batch in each seurat cluster. dendrograms based on similarity of the vectors. should be colored by the value(proportion), but some of the cluster/sample values to seem to match with the color
VlnPlot(merged, features= "percent.mt", group.by = "SCT_snn_res.1", pt.size = 0)
merged[["percent.rps"]]<- PercentageFeatureSet(merged, pattern = "^RPS")
merged[["percent.rpl"]]<- PercentageFeatureSet(merged, pattern = "^RPL")
merged[["percent.rp"]]<- merged[["percent.rps"]]+merged[["percent.rpl"]]
VlnPlot(merged, features= "percent.rp", group.by = "SCT_snn_res.1", pt.size=0)
FeaturePlot(merged, features = "nFeature_RNA")
head(merged)
An object of class Seurat
2 features across 42488 samples within 2 assays
Active assay: SCT (1 features, 1 variable features)
1 other assay present: RNA
3 dimensional reductions calculated: pca, harmony, umap
VlnPlot(merged, features= "nFeature_RNA", group.by = "SCT_snn_res.1", pt.size=0)
FeaturePlot(merged, features = c("POU5F1", "SOX17", "HAND1", "PAX6"), pt.size = 0.2, ncol=2, combine=T)
FeaturePlot(merged, features = c("FGB", "ECSCR", "NEUROD1", "SOX10"), pt.size = 0.2, ncol=2)
sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.4.4 RColorBrewer_1.1-2 here_0.1-11 DataCombine_0.2.21
[5] ggplot2_3.3.2 harmony_1.0 Rcpp_1.0.5 Seurat_3.2.0
[9] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_1.4-1 deldir_0.1-28
[4] ellipsis_0.3.1 ggridges_0.5.2 rprojroot_1.3-2
[7] fs_1.4.2 spatstat.data_1.4-3 farver_2.0.3
[10] leiden_0.3.3 listenv_0.8.0 npsurv_0.4-0
[13] ggrepel_0.8.2 RSpectra_0.16-0 codetools_0.2-16
[16] splines_3.6.1 lsei_1.2-0 knitr_1.29
[19] polyclip_1.10-0 jsonlite_1.7.0 ica_1.0-2
[22] cluster_2.1.0 png_0.1-7 uwot_0.1.8
[25] shiny_1.5.0 sctransform_0.2.1 compiler_3.6.1
[28] httr_1.4.2 backports_1.1.8 Matrix_1.2-18
[31] fastmap_1.0.1 lazyeval_0.2.2 later_1.1.0.1
[34] htmltools_0.5.0 tools_3.6.1 rsvd_1.0.3
[37] igraph_1.2.5 gtable_0.3.0 glue_1.4.1
[40] RANN_2.6.1 dplyr_1.0.0 rappdirs_0.3.1
[43] spatstat_1.64-1 vctrs_0.3.2 gdata_2.18.0
[46] ape_5.3 nlme_3.1-140 lmtest_0.9-37
[49] xfun_0.16 stringr_1.4.0 globals_0.12.5
[52] mime_0.9 miniUI_0.1.1.1 lifecycle_0.2.0
[55] irlba_2.3.3 gtools_3.8.2 goftest_1.2-2
[58] future_1.18.0 MASS_7.3-51.4 zoo_1.8-8
[61] scales_1.1.1 promises_1.1.1 spatstat.utils_1.17-0
[64] parallel_3.6.1 yaml_2.2.1 reticulate_1.16
[67] pbapply_1.4-2 gridExtra_2.3 rpart_4.1-15
[70] stringi_1.4.6 caTools_1.18.0 rlang_0.4.7
[73] pkgconfig_2.0.3 bitops_1.0-6 evaluate_0.14
[76] lattice_0.20-38 ROCR_1.0-7 purrr_0.3.4
[79] tensor_1.5 labeling_0.3 patchwork_1.0.1
[82] htmlwidgets_1.5.1 cowplot_1.0.0 tidyselect_1.1.0
[85] RcppAnnoy_0.0.16 plyr_1.8.6 magrittr_1.5
[88] R6_2.4.1 gplots_3.0.4 generics_0.0.2
[91] withr_2.2.0 pillar_1.4.6 whisker_0.4
[94] mgcv_1.8-28 fitdistrplus_1.0-14 survival_3.2-3
[97] abind_1.4-5 tibble_3.0.3 future.apply_1.6.0
[100] crayon_1.3.4 KernSmooth_2.23-15 plotly_4.9.2.1
[103] rmarkdown_2.3 grid_3.6.1 data.table_1.13.0
[106] git2r_0.26.1 digest_0.6.25 xtable_1.8-4
[109] tidyr_1.1.0 httpuv_1.5.4 munsell_0.5.0
[112] viridisLite_0.3.0