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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/
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library(Seurat)
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 17.05362 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")
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DimPlot(merged, reduction="pca", group.by= c("individual", "Batch"), combine=F)
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Now Running UMAP and identifying clusters, etc
merged<- RunUMAP(merged, reduction = "pca", 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="pca", 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.rds'))
#reassign idents
Idents(merged)<- 'SCT_snn_res.1'
VizDimLoadings(merged, dims = 1:2, reduction = "pca")
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VizDimLoadings(merged, dims = 3:4, reduction = "pca")
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VizDimLoadings(merged, dims = 5:6, reduction = "pca")
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xlim <- c(min(merged@reductions$pca@cell.embeddings[,'PC_1']),
max(merged@reductions$pca@cell.embeddings[,'PC_1']))
ylim <- c(min(merged@reductions$pca@cell.embeddings[,'PC_2']),
max(merged@reductions$pca@cell.embeddings[,'PC_2']))
individuals <- table(merged$individual)
individuals <- individuals[individuals>50]
individuals <- names(individuals)
for (i in individuals)
{
print(DimPlot(merged, reduction = "pca", group.by = c("Batch"), pt.size = 0.01,
cells = WhichCells(merged, expression = individual == i)) +
xlim(xlim) + ylim(ylim) + ggtitle(i))
}
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DimPlot(merged, reduction = "umap")
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DimPlot(merged, reduction = "umap", group.by = "Batch")
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DimPlot(merged, reduction = "umap", group.by = "individual")
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xlim <- c(min(merged@reductions$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))
}
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plots2<- DimPlot(merged, group.by = "individual", split.by = "Batch")
plots2
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DimPlot(merged, group.by = "Batch", split.by = c("individual"))
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DimPlot(merged, group.by = "SCT_snn_res.1", split.by = c("Batch"), label=T)
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DimPlot(merged, reduction = "pca", group.by = "SCT_snn_res.1", split.by = "Batch", combine = F)
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VlnPlot(merged, features = c("POU5F1", "PAX6", "TNNT2", "SOX17", "HAND1", "LUM"), ncol=2)
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#pluripotent markers
FeaturePlot(merged, features = c("POU5F1", "SOX2", "NANOG"), pt.size = 0.2, ncol=3)
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#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)
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#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)
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#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)
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#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)
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421a225 | KLRhodes | 2020-08-10 |
#how many cells per cluster?
t1<-table(merged@meta.data$SCT_snn_res.1, merged@meta.data$Batch)
t1
Batch1 Batch2 Batch3
0 4251 15 11
1 115 563 2902
2 61 2977 429
3 1362 444 845
4 1182 404 910
5 524 85 1811
6 1560 533 171
7 35 231 1581
8 202 368 1091
9 66 111 1458
10 571 575 458
11 1479 7 3
12 223 1184 22
13 246 274 866
14 498 658 207
15 1099 0 0
16 349 124 618
17 587 184 292
18 751 167 39
19 299 117 535
20 546 274 35
21 279 223 239
22 92 116 355
23 74 66 285
24 250 61 80
25 101 59 135
26 2 32 261
27 69 13 111
#percent of cells in each cluster per batch
t1colsum<- colSums(t1)
percT1<-t1/t1colsum
percT1
Batch1 Batch2 Batch3
0 0.2519409708 0.0015205271 0.0006984127
1 0.0116573746 0.0357460317 0.1719907545
2 0.0038730159 0.1764357257 0.0434870755
3 0.0807206780 0.0450076026 0.0536507937
4 0.1198175367 0.0256507937 0.0539323179
5 0.0332698413 0.0050376341 0.1835783071
6 0.0924554021 0.0540293969 0.0108571429
7 0.0035478966 0.0146666667 0.0936999941
8 0.0128253968 0.0218099923 0.1105930056
9 0.0039115747 0.0112519007 0.0925714286
10 0.0578813989 0.0365079365 0.0271439578
11 0.0939047619 0.0004148640 0.0003041054
12 0.0132163812 0.1200202737 0.0013968254
13 0.0249366447 0.0173968254 0.0513246014
14 0.0316190476 0.0389972145 0.0209832742
15 0.0651336455 0.0000000000 0.0000000000
16 0.0353775976 0.0078730159 0.0366265631
17 0.0372698413 0.0109049961 0.0295995945
18 0.0445089788 0.0169285352 0.0024761905
19 0.0303091738 0.0074285714 0.0317074616
20 0.0346666667 0.0162389617 0.0035478966
21 0.0165352931 0.0226051698 0.0151746032
22 0.0093258996 0.0073650794 0.0210395306
23 0.0046984127 0.0039115747 0.0288900152
24 0.0148165709 0.0061834769 0.0050793651
25 0.0102382159 0.0037460317 0.0080009483
26 0.0001269841 0.0018965211 0.0264571718
27 0.0040893736 0.0013177902 0.0070476190
heatmap(t(percT1))
Version | Author | Date |
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421a225 | KLRhodes | 2020-08-10 |
#how many cells per cluster from each individual?
t2<-table(merged@meta.data$SCT_snn_res.1, merged@meta.data$individual)
t2
SNG-NA18511 SNG-NA18858 SNG-NA19160
0 15 4259 3
1 19 3557 4
2 3 3462 2
3 843 14 1794
4 271 2 2223
5 2043 36 341
6 215 95 1954
7 4 1843 0
8 474 4 1183
9 1617 0 18
10 1155 29 420
11 1456 18 15
12 1402 16 11
13 553 2 831
14 135 1213 15
15 1 1098 0
16 59 3 1029
17 74 2 987
18 82 15 860
19 427 1 523
20 22 814 19
21 635 10 96
22 134 0 429
23 106 4 315
24 74 167 150
25 89 2 204
26 5 0 290
27 36 4 153
t2colsums<-colSums(t2)
percT2<- t2/t2colsums
percT2
SNG-NA18511 SNG-NA18858 SNG-NA19160
0 1.255335e-03 2.554889e-01 2.163098e-04
1 1.139772e-03 2.564713e-01 3.347560e-04
2 2.163098e-04 2.897314e-01 1.199760e-04
3 7.054984e-02 8.398320e-04 1.293532e-01
4 1.625675e-02 1.442065e-04 1.860407e-01
5 1.473069e-01 3.012804e-03 2.045591e-02
6 1.799314e-02 5.698860e-03 1.408898e-01
7 2.399520e-04 1.328863e-01 0.000000e+00
8 3.417694e-02 3.347560e-04 7.096581e-02
9 1.353251e-01 0.000000e+00 1.297859e-03
10 6.928614e-02 2.090994e-03 3.514938e-02
11 1.049823e-01 1.506402e-03 8.998200e-04
12 1.173320e-01 9.598080e-04 7.931358e-04
13 3.317337e-02 1.442065e-04 6.954557e-02
14 9.733939e-03 1.015148e-01 8.998200e-04
15 8.368901e-05 6.586683e-02 0.000000e+00
16 3.539292e-03 2.163098e-04 8.611599e-02
17 5.335641e-03 1.673780e-04 5.920816e-02
18 6.862499e-03 8.998200e-04 6.200880e-02
19 2.561488e-02 7.210325e-05 4.376935e-02
20 1.586272e-03 6.812286e-02 1.139772e-03
21 5.314252e-02 5.998800e-04 6.921912e-03
22 8.038392e-03 0.000000e+00 3.590259e-02
23 7.642945e-03 3.347560e-04 1.889622e-02
24 6.192987e-03 1.001800e-02 1.081549e-02
25 5.338932e-03 1.442065e-04 1.707256e-02
26 3.605163e-04 0.000000e+00 1.739652e-02
27 3.012804e-03 2.399520e-04 1.103180e-02
heatmap(t(percT2))
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421a225 | KLRhodes | 2020-08-10 |
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
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421a225 | KLRhodes | 2020-08-10 |
#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)")
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421a225 | KLRhodes | 2020-08-10 |
#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)
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421a225 | KLRhodes | 2020-08-10 |
#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")
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DimPlot(merged, reduction = "umap", group.by = "Batch")
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DimPlot(merged, reduction = "umap", group.by = "individual")
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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))
}
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#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$Batch3_19160)
colnames(fullpercs)<-cols2
fullpercs_cor<- round(cor(fullpercs),2)
fullpercs_melt<- melt(fullpercs_cor)
ggplot(data= fullpercs_melt, aes(x=Var1, y=Var2, fill=value)) +
geom_tile(color="white") +
scale_fill_gradient2(low="blue", high="red", mid="white", midpoint = 0, limit= c(-1,1), space= "Lab", name="Pearson\nCorrelation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 30, hjust=1))+
ggtitle("Pairwise Pearson Correlation of the percent of cells from \neach cell line assigned to each Seurat Cluster\ncluster res. 0.5")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
#now clustering individual_Batch samples with hierarchical clustering/they will get reordered based on similarity
beauty<- colorRampPalette(brewer.pal(9,"Purples"))(200)
rownames(fullpercs)<- c(0:(nrow(fullpercs)-1))
heatmap(as.matrix(fullpercs), scale="none", col=beauty, cexCol = .7, cexRow=.6)
text(1:ncol(fullpercs),labels=names(fullpercs),srt=30)
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
#generate a heatmap of the raw proportion of cells from each individual_batch in each seurat cluster. dendrograms based on similarity of the vectors. should be colored by the value(proportion), but some of the cluster/sample values to seem to match with the color
#reassign idents
Idents(merged)<- 'SCT_snn_res.0.1'
DimPlot(merged, reduction = "umap")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
DimPlot(merged, reduction = "umap", group.by = "Batch")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
DimPlot(merged, reduction = "umap", group.by = "individual")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
xlim <- c(min(merged@reductions$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$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
print(DimPlot(merged, reduction = "umap",
cells = WhichCells(merged, expression = individual == i)) +
xlim(xlim) + ylim(ylim) + ggtitle(i))
}
Version | Author | Date |
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421a225 | KLRhodes | 2020-08-10 |
Version | Author | Date |
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421a225 | KLRhodes | 2020-08-10 |
Version | Author | Date |
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421a225 | KLRhodes | 2020-08-10 |
#exploring similarity in the number of cells per individual between batches
merged.Batch1<- (subset(merged, Batch == "Batch1"))
b1t<- table(merged.Batch1$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")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
#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)
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
#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")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
DimPlot(merged, reduction = "umap", group.by = "Batch")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
DimPlot(merged, reduction = "umap", group.by = "individual")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
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))
}
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
#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")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
#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)
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
#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)
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
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)
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
FeaturePlot(merged, features = "nFeature_RNA")
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
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
2 dimensional reductions calculated: pca, umap
VlnPlot(merged, features= "nFeature_RNA", group.by = "SCT_snn_res.1", pt.size=0)
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
FeaturePlot(merged, features = c("POU5F1", "SOX17", "HAND1", "PAX6"), pt.size = 0.2, ncol=2, combine=T)
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
FeaturePlot(merged, features = c("FGB", "ECSCR", "NEUROD1", "SOX10"), pt.size = 0.2, ncol=2)
Version | Author | Date |
---|---|---|
421a225 | KLRhodes | 2020-08-10 |
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 Seurat_3.2.0 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] Rcpp_1.0.5 spatstat_1.64-1 vctrs_0.3.2
[46] gdata_2.18.0 ape_5.3 nlme_3.1-140
[49] lmtest_0.9-37 xfun_0.16 stringr_1.4.0
[52] globals_0.12.5 mime_0.9 miniUI_0.1.1.1
[55] lifecycle_0.2.0 irlba_2.3.3 gtools_3.8.2
[58] goftest_1.2-2 future_1.18.0 MASS_7.3-51.4
[61] zoo_1.8-8 scales_1.1.1 promises_1.1.1
[64] spatstat.utils_1.17-0 parallel_3.6.1 yaml_2.2.1
[67] reticulate_1.16 pbapply_1.4-2 gridExtra_2.3
[70] rpart_4.1-15 stringi_1.4.6 caTools_1.18.0
[73] rlang_0.4.7 pkgconfig_2.0.3 bitops_1.0-6
[76] evaluate_0.14 lattice_0.20-38 ROCR_1.0-7
[79] purrr_0.3.4 tensor_1.5 labeling_0.3
[82] patchwork_1.0.1 htmlwidgets_1.5.1 cowplot_1.0.0
[85] tidyselect_1.1.0 RcppAnnoy_0.0.16 plyr_1.8.6
[88] magrittr_1.5 R6_2.4.1 gplots_3.0.4
[91] generics_0.0.2 pillar_1.4.6 whisker_0.4
[94] withr_2.2.0 mgcv_1.8-28 fitdistrplus_1.0-14
[97] survival_3.2-3 abind_1.4-5 tibble_3.0.3
[100] future.apply_1.6.0 crayon_1.3.4 KernSmooth_2.23-15
[103] plotly_4.9.2.1 rmarkdown_2.3 grid_3.6.1
[106] data.table_1.13.0 git2r_0.26.1 digest_0.6.25
[109] xtable_1.8-4 tidyr_1.1.0 httpuv_1.5.4
[112] munsell_0.5.0 viridisLite_0.3.0