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	f50ebd3	KLRhodes	2020-08-10	wflow_publish("analysis/Integrate*")
html	421a225	KLRhodes	2020-08-10	Build site.
Rmd	bc8ec6f	KLRhodes	2020-08-10	cleaning various versions of merging/intCurrent working directory

---

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
  
}

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

Warning in `[<-`(`*tmp*`, i, value = rds): implicit list embedding of S4 objects
is deprecated

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.

#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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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

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Visualize Harmony embeddings

DimPlot(merged, reduction="harmony", group.by= c("individual", "Batch"), combine=F)

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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,'Harmony.Batchindividual.rds'))

#reassign idents
Idents(merged)<- 'SCT_snn_res.1'

VizDimLoadings(merged, dims = 1:2, reduction = "harmony")

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VizDimLoadings(merged, dims = 3:4, reduction = "harmony")

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VizDimLoadings(merged, dims = 5:6, reduction = "harmony")

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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))
}

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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 = "harmony", 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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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    2377   1416   2350
  1    2128   1191   1821
  2    2014   1306   1157
  3    1708    721    878
  4    1361    435    871
  5     406    855    741
  6     412    512   1075
  7     687    258    647
  8     778    221    526
  9     550    490    442
  10    249    273    859
  11    445    410    309
  12    615    207    329
  13    416    141    540
  14    443    190    404
  15    332    203    320
  16     36     92    592
  17    256    245    207
  18     11     55    633
  19    105    120    369
  20    289    255     43
  21    568      3      0
  22     82     75    252
  23    238     64     94
  24    103     60    134
  25     83     38     99
  26    101     19     29
  27     80     10     29

#percent of cells in each cluster per batch
t1colsum<- colSums(t1)
percT1<-t1/t1colsum
percT1

    
           Batch1       Batch2       Batch3
  0  0.1408759557 0.1435377598 0.1492063492
  1  0.2157121135 0.0756190476 0.1079239021
  2  0.1278730159 0.0774017661 0.1172833249
  3  0.1012268121 0.0730866700 0.0557460317
  4  0.1379624937 0.0276190476 0.0516209329
  5  0.0257777778 0.0506726723 0.0751140395
  6  0.0244177088 0.0519006589 0.0682539683
  7  0.0696401419 0.0163809524 0.0383452854
  8  0.0493968254 0.0130978486 0.0533198175
  9  0.0325964559 0.0496705525 0.0280634921
  10 0.0252407501 0.0173333333 0.0509097375
  11 0.0282539683 0.0242991762 0.0313228586
  12 0.0364487643 0.0209832742 0.0208888889
  13 0.0421692854 0.0089523810 0.0320037930
  14 0.0281269841 0.0112605938 0.0409528637
  15 0.0196764061 0.0205778003 0.0203174603
  16 0.0036492651 0.0058412698 0.0350856398
  17 0.0162539683 0.0145202394 0.0209832742
  18 0.0006519291 0.0055752661 0.0401904762
  19 0.0106436898 0.0076190476 0.0218692586
  20 0.0183492063 0.0151129023 0.0043588444
  21 0.0336632490 0.0003041054 0.0000000000
  22 0.0083122149 0.0047619048 0.0149351034
  23 0.0151111111 0.0037930421 0.0095286366
  24 0.0061044272 0.0060821085 0.0085079365
  25 0.0084135834 0.0024126984 0.0058673621
  26 0.0064126984 0.0011260594 0.0029396858
  27 0.0047413027 0.0010136847 0.0018412698

heatmap(t(percT1))

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#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          363        5633         147
  1          199        4859          82
  2         3005         237        1235
  3         1575         154        1578
  4          831          31        1805
  5          113        1741         148
  6          871         289         839
  7           84        1466          42
  8          155          34        1336
  9          971         103         408
  10         524          38         819
  11         106         965          93
  12         139          19         993
  13          89          11         997
  14         135           7         895
  15         581          37         237
  16         411          16         293
  17         416          56         236
  18         450           5         244
  19         155          15         424
  20         133         435          19
  21         328          33         210
  22         108           6         295
  23          83         135         178
  24          90           3         204
  25          18         193           9
  26           0         149           0
  27          16           0         103

t2colsums<-colSums(t2)

percT2<- t2/t2colsums

percT2

    
      SNG-NA18511  SNG-NA18858  SNG-NA19160
  0  0.0303791112 0.3379124175 0.0105991780
  1  0.0119376125 0.3503497008 0.0068624990
  2  0.2166702718 0.0198342958 0.0740851830
  3  0.1318101933 0.0092381524 0.1137789314
  4  0.0498500300 0.0022352008 0.1510586660
  5  0.0081476675 0.1457025693 0.0088782244
  6  0.0728931291 0.0173365327 0.0604946283
  7  0.0050389922 0.1057033672 0.0035149385
  8  0.0111760040 0.0028454264 0.0801439712
  9  0.0812620303 0.0061787642 0.0294181268
  10 0.0314337133 0.0027399236 0.0685413005
  11 0.0076429447 0.0807598962 0.0055788842
  12 0.0116327726 0.0011397720 0.0715985291
  13 0.0053389322 0.0007931358 0.0834379446
  14 0.0097339390 0.0005858231 0.0536892621
  15 0.0486233158 0.0022195561 0.0170884707
  16 0.0246550690 0.0011536520 0.0245208804
  17 0.0299949528 0.0046865847 0.0141571686
  18 0.0376600552 0.0002999400 0.0175931935
  19 0.0092981404 0.0010815488 0.0354841409
  20 0.0095897325 0.0364047201 0.0011397720
  21 0.0274499958 0.0019796041 0.0151416829
  22 0.0064787043 0.0004326195 0.0246882584
  23 0.0059845699 0.0112980166 0.0106778644
  24 0.0075320110 0.0001799640 0.0147090634
  25 0.0010797840 0.0139159276 0.0007532011
  26 0.0000000000 0.0124696627 0.0000000000
  27 0.0013390242 0.0000000000 0.0074266349

heatmap(t(percT2))

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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

Past versions of unnamed-chunk-31-1.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$SCT_snn_res.1, merged.Batch1$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

Version	Author	Date
421a225	KLRhodes	2020-08-10

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

Version	Author	Date
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

Version	Author	Date
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
 3 dimensional reductions calculated: pca, harmony, 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      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