Integrate lanes from all batches of EB pilot (Human data only, no doublets

workflowr

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Last updated: 2020-08-10

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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

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Rmd	bc8ec6f	KLRhodes	2020-08-10	cleaning various versions of merging/intCurrent working directory

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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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  |                                                                            
  |=============================================================         |  88%

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  |======================================================================| 100%

Second step: Get residuals using fitted parameters for 18065 genes

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Computing corrected count matrix for 18065 genes

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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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#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))

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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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#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)")

Past versions of unnamed-chunk-33-1.png

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)

Past versions of unnamed-chunk-34-1.png

Version	Author	Date
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")

Past versions of unnamed-chunk-36-1.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

DimPlot(merged, reduction = "umap", group.by = "Batch")

Past versions of unnamed-chunk-36-2.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

DimPlot(merged, reduction = "umap", group.by = "individual")

Past versions of unnamed-chunk-36-3.png

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))
}

Past versions of unnamed-chunk-36-4.png

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421a225	KLRhodes	2020-08-10

Past versions of unnamed-chunk-36-5.png

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421a225	KLRhodes	2020-08-10

Past versions of unnamed-chunk-36-6.png

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$SCT_snn_res.0.5, merged.Batch1$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")

Past versions of unnamed-chunk-38-1.png

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)

Past versions of unnamed-chunk-39-1.png

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")

Past versions of unnamed-chunk-41-1.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

DimPlot(merged, reduction = "umap", group.by = "Batch")

Past versions of unnamed-chunk-41-2.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

DimPlot(merged, reduction = "umap", group.by = "individual")

Past versions of unnamed-chunk-41-3.png

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))
}

Past versions of unnamed-chunk-41-4.png

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421a225	KLRhodes	2020-08-10

Past versions of unnamed-chunk-41-5.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

Past versions of unnamed-chunk-41-6.png

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$SCT_snn_res.0.1, merged.Batch1$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")

Past versions of unnamed-chunk-43-1.png

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)

Past versions of unnamed-chunk-44-1.png

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")

Past versions of unnamed-chunk-46-1.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

DimPlot(merged, reduction = "umap", group.by = "Batch")

Past versions of unnamed-chunk-46-2.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

DimPlot(merged, reduction = "umap", group.by = "individual")

Past versions of unnamed-chunk-46-3.png

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))
}

Past versions of unnamed-chunk-46-4.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

Past versions of unnamed-chunk-46-5.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

Past versions of unnamed-chunk-46-6.png

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")

Past versions of unnamed-chunk-48-1.png

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)

Past versions of unnamed-chunk-49-1.png

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)

Past versions of unnamed-chunk-50-1.png

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)

Past versions of unnamed-chunk-51-1.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

FeaturePlot(merged, features = "nFeature_RNA")

Past versions of unnamed-chunk-52-1.png

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)

Past versions of unnamed-chunk-54-1.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

FeaturePlot(merged, features = c("POU5F1", "SOX17",  "HAND1", "PAX6"), pt.size = 0.2, ncol=2, combine=T)

Past versions of unnamed-chunk-55-1.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

FeaturePlot(merged, features = c("FGB", "ECSCR",  "NEUROD1", "SOX10"), pt.size = 0.2, ncol=2)

Past versions of unnamed-chunk-56-1.png

Version	Author	Date
421a225	KLRhodes	2020-08-10

Session information

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