ReferenceIntegration

Last updated: 2021-12-08

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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

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File	Version	Author	Date	Message
Rmd	c54da7b	KLRhodes	2021-12-08	Publish reference integration w/ 5 additional EB lines

library(Seurat)
library(edgeR)

Loading required package: limma

library(loomR)

Loading required package: R6

Loading required package: hdf5r

library(dplyr)


Attaching package: 'dplyr'

The following object is masked from 'package:loomR':

    combine

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tidyr)
library(tibble)
library(purrr)


Attaching package: 'purrr'

The following object is masked from 'package:hdf5r':

    flatten_df

library(harmony)

Loading required package: Rcpp

library(ggplot2)

loading data

first, my data

merged<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/FiveNewLines.rds")

loading in hESC and iPS-to-EB raw dges from scHCL reference set

hESC<- read.table("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/hESC1.rawdge.txt.gz", header=T, row.names = 1)

iPStoEBday20<- read.table("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/iPS-to-EB_20Day_dge.txt.gz", header=T, row.names = 1)

Note: there is no available metadata for these iPS to EB differentiations (no cell annotations online)

make seurat objects with all of the data

hESC.obj<- CreateSeuratObject(hESC)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

EB20.obj<- CreateSeuratObject(iPStoEBday20)

Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')

checking quality of cells

FeatureScatter(hESC.obj, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")

#check MT%
#add %MT to metadata
hESC.obj[["percent.mt"]]<- PercentageFeatureSet(hESC.obj, pattern= "^MT-")

FeatureScatter(hESC.obj, feature1 = "nCount_RNA", feature2 = "percent.mt")

summary(hESC.obj$percent.mt)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.200   4.121   4.838   4.922   5.609  11.596

#normalizing each scHCL dataset
hESC.obj<- suppressWarnings(SCTransform(hESC.obj, variable.features.n=5000,verbose=F))
EB20.obj<-suppressWarnings(SCTransform(EB20.obj, variable.features.n=5000,verbose=F))

load loom file from Cao et al (fetal tissue scRNA-seq)

Cao<- connect("/project2/gilad/katie/Pilot_HumanEBs/DataFromCaoetal202/GSE156793_S3_gene_count.loom", mode="r+")

Cao

Cao[["matrix"]]

#which genes are protein coding genes
protein<- which(Cao$row.attrs$gene_type[]=="protein_coding")

#sample genes of all main_cluster_name groups
#making a data fram of cell names and their cluster identity
cell.idents<- Cao$get.attribute.df(MARGIN = 2, col.names = "sample", attributes="Main_cluster_name")

cell.idents<- cell.idents %>% rownames_to_column("cellid")

#I'd like to sample ~500 cells from each cluster. Some clusters have fewer than 500 cells, so for those clusters I will take all cells. To do this, I make a dataframe listing the sample size of each cell type.
s.size<- table(cell.idents$Main_cluster_name)
s.size[s.size > 500]<- 500
#reorder
s.size<- s.size[unique(cell.idents$Main_cluster_name)]

#sample from each cluster
#make nested df
nest<- cell.idents %>% group_by(Main_cluster_name) %>% nest() %>% ungroup() %>% mutate(n = s.size)
 
sampled_nest<- nest %>% mutate(samp= map2(data, n, sample_n))

cells.keep<- sampled_nest %>% select(-data) %>% unnest(samp)
cells.keep<- cells.keep$cellid
cells<- which(cell.idents$cellid %in% cells.keep)

#get metrix for cells and genes of interest
Cao.matrix<- Cao[["matrix"]][cells,protein]

#get metadata
attrs<- c("Assay", "Batch", "Experiment_batch", "Main_cluster_name", "Fetus_id", "Sex")
attr.df<-Cao$get.attribute.df(MARGIN = 2, col.names = "sample", attributes=attrs)

#subset the attr.df to just the subset of cells
attr.df<- attr.df[cells,]

#name matrix rows and columns
rownames(Cao.matrix)<- rownames(attr.df)
colnames(Cao.matrix)<- Cao[["row_attrs/gene_short_name"]][protein]

#seems like there are some non-unique gene names, which is expected but annoying
dups<- which(duplicated(colnames(Cao.matrix)))

length(dups)

#sum the counts for duplicate gene names
Cao.matrix.test<- (t(Cao.matrix))
rowname<- rownames(Cao.matrix.test)
Cao.matrix.test<- cbind(rowname, as.data.frame(Cao.matrix.test))

rownames(Cao.matrix.test)<- NULL


Cao.agg.dups<- aggregate(.~rowname, Cao.matrix.test, sum)

anyDuplicated(Cao.agg.dups$rowname)

#rename rows
rownames(Cao.agg.dups)<- Cao.agg.dups$rowname

#remove rowname column
Cao.agg.dups<- Cao.agg.dups[, 2:ncol(Cao.agg.dups)]

colnames(Cao.agg.dups)<- rownames(attr.df)

Now, Convert to Seurat Object. Major question: is this raw data? or are these counts normalized?

Cao.obj<- CreateSeuratObject(Cao.agg.dups, meta.data= attr.df)

Normalize the fetal reference set

#first normalizing with SCTransform
Cao.obj<- SCTransform(Cao.obj, variable.features.n=5000,verbose=F)

saveRDS(Cao.obj,"/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds" )

Cao.obj<-readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds")

#check features, QC between batches/individuals
#Cao data has no MT genes
FeatureScatter(Cao.obj, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "Fetus_id")

FeatureScatter(Cao.obj, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "Batch")

FeatureScatter(Cao.obj, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "Experiment_batch")

summary(Cao.obj@meta.data$nCount_RNA)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  139.0   319.0   467.0   763.5   808.0 59044.0

summary(Cao.obj@meta.data$nFeature_RNA)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  103.0   255.0   358.0   494.6   573.0  8627.0

Cao.obj[["percent.mt"]]<- PercentageFeatureSet(Cao.obj, pattern= "^MT-")

summary(Cao.obj@meta.data$percent.mt)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0       0       0       0       0       0

Cao.obj<- RunPCA(Cao.obj, npcs= 100, verbose = F)

DimPlot(Cao.obj, reduction = "pca")

DimPlot(Cao.obj, reduction = "pca", group.by = "Fetus_id")

DimPlot(Cao.obj, reduction = "pca", group.by = "Experiment_batch")

DimPlot(Cao.obj, reduction = "pca", group.by = "Batch")

DimPlot(Cao.obj, reduction = "pca", group.by = "Main_cluster_name")

VizDimLoadings(Cao.obj, dims = 1:2, reduction = "pca")

VizDimLoadings(Cao.obj, dims = 3:4, reduction = "pca")

VizDimLoadings(Cao.obj, dims = 5:6, reduction = "pca")

Cao.obj<- FindNeighbors(Cao.obj, dims = 1:30, verbose = F)
Cao.obj<- RunUMAP(Cao.obj, dims=1:30, 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

DimPlot(Cao.obj, group.by = "Main_cluster_name", label = T, label.size = 2) + NoLegend()

Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
Please use `as_label()` or `as_name()` instead.
This warning is displayed once per session.

DimPlot(Cao.obj, group.by = "Fetus_id")

DimPlot(Cao.obj, group.by = "Experiment_batch")

#rename Cao metadata so none match with EB object (just need to replace Batch)
colnames(Cao.obj@meta.data)

 [1] "orig.ident"        "nCount_RNA"        "nFeature_RNA"     
 [4] "Assay"             "Batch"             "Experiment_batch" 
 [7] "Main_cluster_name" "Fetus_id"          "Sex"              
[10] "nCount_SCT"        "nFeature_SCT"      "percent.mt"

colnames(Cao.obj@meta.data)[5]<-"Batch_week"

#rename orig.idents
hESC.obj$orig.ident<- "scHCL.hESC"
EB20.obj$orig.ident<- "scHCL.EB20"
merged$orig.ident<- "EB.5New"
Cao.obj$orig.ident<- "Cao.EtAl"

#merge objects
obj.list<- list(Cao.obj, merged, hESC.obj, EB20.obj)
merge.all<- merge(x=obj.list[[1]], y=c(obj.list[[2]], obj.list[[3]], obj.list[[4]]), merge.data=T)

merge.all<- SCTransform(merge.all, variable.features.n = 5000, vars.to.regress = c("orig.ident"), assay= "SCT")

Calculating cell attributes for input UMI matrix

Variance stabilizing transformation of count matrix of size 34116 by 72464

Model formula is y ~ log_umi

Get Negative Binomial regression parameters per gene

Using 2000 genes, 72464 cells


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  |                                                                            
  |=========                                                             |  12%

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  |==================                                                    |  25%

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  |==========================                                            |  38%

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Warning: glm.fit: fitted rates numerically 0 occurred

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

Warning in theta.ml(y = y, mu = fit$fitted): iteration limit reached

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Warning: glm.fit: fitted rates numerically 0 occurred

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Warning: glm.fit: fitted rates numerically 0 occurred

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Warning: glm.fit: fitted rates numerically 0 occurred

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Warning: glm.fit: fitted rates numerically 0 occurred

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Warning: glm.fit: fitted rates numerically 0 occurred

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Warning: glm.fit: fitted rates numerically 0 occurred

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

Found 2 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 34116 genes


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Computing corrected count matrix for 34116 genes


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Calculating gene attributes

Wall clock passed: Time difference of 43.73656 mins

Determine variable features

Set 5000 variable features

Place corrected count matrix in counts slot

Regressing out orig.ident

Centering data matrix

Set default assay to SCT

all.genes= rownames(merge.all)
merge.all<-FindVariableFeatures(merge.all,selection.method="vst", nfeatures = 5000)
merge.all<- ScaleData(merge.all, features = all.genes, assay = "SCT")

Centering and scaling data matrix

merge.all<-RunPCA(merge.all, npcs = 100, verbose=F, Assay="SCT")

merge.all<- RunHarmony(merge.all, c("orig.ident"), theta = c(3), plot_convergence = T, assay.use = "SCT")

Warning: Quick-TRANSfer stage steps exceeded maximum (= 3623200)

Harmony 1/10

Harmony 2/10

Harmony converged after 2 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

merge.all<- RunUMAP(merge.all,dims=1:100, reduction="harmony")

14:31:22 UMAP embedding parameters a = 0.9922 b = 1.112

14:31:22 Read 72464 rows and found 100 numeric columns

14:31:22 Using Annoy for neighbor search, n_neighbors = 30

14:31:22 Building Annoy index with metric = cosine, n_trees = 50

0%   10   20   30   40   50   60   70   80   90   100%

[----|----|----|----|----|----|----|----|----|----|

**************************************************|
14:31:47 Writing NN index file to temp file /tmp/jobs/14911987/Rtmpn02nTu/file17ee3d07d02a
14:31:47 Searching Annoy index using 1 thread, search_k = 3000
14:32:38 Annoy recall = 100%
14:32:40 Commencing smooth kNN distance calibration using 1 thread
14:32:44 Initializing from normalized Laplacian + noise
14:32:56 Commencing optimization for 200 epochs, with 3595098 positive edges
14:34:38 Optimization finished

saveRDS(merge.all,"/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5newlines.merge.all.SCTwRegressOrigIdent.Harmony.rds" )

merge.all<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5newlines.merge.all.SCTwRegressOrigIdent.Harmony.rds")

V<- DimPlot(merge.all, group.by = "Main_cluster_name", label = T, label.size = 2.5, repel = T)+NoLegend()

V

Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

pdf(file = "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_MainClusterID_SCTregress.pdf")

V

dev.off()

Add new metadata to include Main_cluster_name as well as cluster labels from EB Pilot DE results, and orig ident of the HCL data

merge.all<- AddMetaData(merge.all, col.name = "all.labels", metadata = merge.all@meta.data$Main_cluster_name)

#for cells with NA for main_cluster_name, replace label from SCT_snn_res 0.1
for (i in (1:nrow(merge.all@meta.data))){
if (is.na(merge.all@meta.data$all.labels[i]) == T){
  merge.all@meta.data$all.labels[i]<- merge.all@meta.data$SCT_snn_res.0.1[i]
}
}

#and now replace remaining NAs with orig.ident
for (i in (1:nrow(merge.all@meta.data))){
if (is.na(merge.all@meta.data$all.labels[i]) == T){
  merge.all@meta.data$all.labels[i]<- merge.all@meta.data$orig.ident[i]
}
}

V<- DimPlot(merge.all, group.by = "all.labels", label = T, label.size = 2.5, repel = T)+NoLegend()

V

Warning: ggrepel: 4 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

pdf(file = "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_allLabels_SCTregress.pdf")

V

dev.off()

V<- DimPlot(merge.all, group.by = "orig.ident", label = F, order= c("scHCL.hESC","scHCL.EB20", "EB.5New","Cao.EtAl"))
  

V

pdf(file = "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_origident_all_SCTregress.pdf")

V

dev.off()

DimPlot(merge.all, split.by = "orig.ident", label = F, order= c("scHCL.hESC","scHCL.EB20", "EB.5New","Cao.EtAl"))

options(ggrepel.max.overlaps = Inf)

#subset object to just my data and Cao reference, plot UMAP
Idents(merge.all)<- "orig.ident"
sub<- subset(merge.all, idents= c("Cao.EtAl", "EB.5New"))
V<- DimPlot(sub, group.by = "all.labels", label = T, repel = T, label.size = 2.5)+NoLegend()
V

pdf(file = "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilotAndCao_SCTregress.pdf")

V

dev.off()

V<- DimPlot(sub, group.by = "Main_cluster_name", label = T, repel = T, label.size = 2.5)+NoLegend()
V

pdf(file = "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilotAndCao_groupbyMainClusterName_SCTregress.pdf")

V

dev.off()

V<- DimPlot(sub, group.by = "SCT_snn_res.0.1", label = T, repel = T, label.size = 2.5)+NoLegend()
V

pdf(file = "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilotAndCao_groupbySeuratClusterRes0.1_SCTregress.pdf")

V

dev.off()

DimPlot(sub, group.by = "orig.ident")

DimPlot(sub, group.by = "orig.ident", order="Cao.EtAl")

#Subset object to just my data and HCL references, plot UMAP
sub<- subset(merge.all, idents= c("scHCL.EB20", "EB.5New"))
V<- DimPlot(sub, group.by = "orig.ident", label = F)
V

sub<- subset(merge.all, idents= c("scHCL.EB20", "EB.5New", "Cao.EtAl"))
DimPlot(sub, group.by="orig.ident", pt.size = 0.2, label=F)

FeaturePlot(merge.all, split.by = "orig.ident", features = c("TERF1", "POU5F1", "DPPA3"), slot = "data", pt.size = 0.1)

Warning in FeaturePlot(merge.all, split.by = "orig.ident", features =
c("TERF1", : All cells have the same value (0) of POU5F1.

Warning in FeaturePlot(merge.all, split.by = "orig.ident", features =
c("TERF1", : All cells have the same value (0) of DPPA3.

Warning in FeaturePlot(merge.all, split.by = "orig.ident", features =
c("TERF1", : All cells have the same value (0) of DPPA3.

DimPlot(merge.all, split.by="orig.ident",group.by = "all.labels", pt.size = 0.2, label=F) +NoLegend()

Now, will transfer labels for Cao Et Al and hESC onto my data.

#subset to remove scHCL.EB20
sub<- subset(merge.all, idents= c("Cao.EtAl", "EB.5New", "scHCL.hESC"))
#compute distance matrix based on harmony embeddings, dims 1:100
har_embeds<- sub@reductions$harmony@cell.embeddings

har_distmat<- as.matrix(dist(har_embeds, method="euclidean", upper=TRUE))

#vectors with cell ids from Cao.EtAl, EB.5New, and scHCL.hESC
EB.5New.ids<-rownames(merge.all@meta.data[merge.all@meta.data$orig.ident == "EB.5New",])

#subset rows to only cells in EB.5New
sub_har_distmat<- har_distmat[rownames(har_distmat) %in% EB.5New.ids,]

#subset cols to only cells not in EB.5New
'%notin%'<- Negate('%in%')
sub_har_distmat<- sub_har_distmat[,colnames(sub_har_distmat) %notin% EB.5New.ids]

nearest.ref.cell.id<- NULL
nearest.ref.cell.dist<- NULL
#for loop, loop through each row
for (i in 1:nrow(sub_har_distmat)){
  nearest.ref.cell.dist[i]<- min(sub_har_distmat[i,])
  nearest.ref.cell.id[i]<- names(which.min(sub_har_distmat[i,]))
}

nearest.ref.table<- cbind(rownames(sub_har_distmat), nearest.ref.cell.id,nearest.ref.cell.dist)

colnames(nearest.ref.table)<- c("EB.cell.id", "nearest.ref.cell.id", "harmony.dist.to.nearest.ref.cell")

#add annotation
ann<- as.data.frame(merge.all@meta.data$all.labels)
ann<- cbind(rownames(merge.all@meta.data), ann)
colnames(ann)<- c("nearest.ref.cell.id", "annotation")

nearest.ref.table<- as.data.frame(nearest.ref.table)
nearest.ann<- left_join(nearest.ref.table, ann, by=c("nearest.ref.cell.id"))

write.csv(nearest.ann, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.NearestReferenceCell.Cao.hESC.EuclideanDistanceinHarmonySpace.csv")

nearest.ann<- read.csv("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.NearestReferenceCell.Cao.hESC.EuclideanDistanceinHarmonySpace.csv", header=T, row.names = 1)

a<- as.data.frame(table(nearest.ann$annotation))
a<- a[a$Var1 != "scHCL.EB20",]
a<- a[a$Var1 != "0",]
a<- a[a$Var1 != "1",]
a<- a[a$Var1 != "2",]
a<- a[a$Var1 != "3",]
a<- a[a$Var1 != "4",]
a<- a[a$Var1 != "5",]
a<- a[a$Var1 != "6",]
a<- a[a$Var1 != "7",]
colnames(a)<- c("reference.cell.type", "Frequency")

write.csv(a, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.NearestReferenceCell.Cao.hESC.FrequencyofEachAnnotation.csv")

a<- read.csv("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.NearestReferenceCell.Cao.hESC.FrequencyofEachAnnotation.csv", header=T, row.names = 1)

sub<- subset(merge.all, idents= c("EB.5New"))
EB.cell.id<- rownames(sub@meta.data)
sub@meta.data<- cbind(sub@meta.data, EB.cell.id)
sub@meta.data<- full_join(sub@meta.data, nearest.ann, by= c("EB.cell.id"))
rownames(sub@meta.data)<- EB.cell.id

V<- DimPlot(sub, group.by="annotation", pt.size = 0.2, label.size = 2.5,label=T, repel=T) +NoLegend()

V

pdf(file = "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilot_annotationsFromReference.pdf")

V

dev.off()

Instead of matching to just one nearest cell, we get a more robust annotation if we check a group of nearest neighbors for common annotations

mostcommon.ann<- NULL
maxann.FIVEnearest<- NULL

#for loop, loop through each row
for (i in 1:nrow(sub_har_distmat)){
  cell<- sub_har_distmat[i,]
  cell<- cell[order(cell)]
  topfive<- names(cell[1:5])
  
  #get the annotations of the nearest 5 reference cells
  topfiveann<- merge.all@meta.data$all.labels[rownames(merge.all@meta.data) %in% topfive]
  
  #if/else at least 3/5 match annotations
  maxann<- max(table(topfiveann))
  finalann<- names(which.max(table(topfiveann)))
  
  maxann.FIVEnearest[i]<- maxann
  
  if(maxann >= 3){
    mostcommon.ann[i]<- finalann
  } else {
    mostcommon.ann[i]<- "uncertain"
  }
  
}

CommonAnnDF<- as.data.frame(cbind(rownames(sub_har_distmat), mostcommon.ann, maxann.FIVEnearest))
colnames(CommonAnnDF)<- c("EB.cell.id", "Annotation", "NoutofFIVErefneighborsWithSameAnnotation")

write.csv(CommonAnnDF, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.MostCommonAnnotation.FiveNearestRefCells.csv")

CommonAnnDF<- read.csv("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.MostCommonAnnotation.FiveNearestRefCells.csv", header=T, row.names = 1)

b<- table(CommonAnnDF$Annotation)

write.csv(b, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.Frequency.MostCommonAnnotation.FiveNearestRefCells.csv")

b<- read.csv("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.Frequency.MostCommonAnnotation.FiveNearestRefCells.csv", header=T, row.names = 1)

#print fetal celltypes not present in EB data
'%notin%'<- Negate('%in%')
unique(merge.all@meta.data$Main_cluster_name)[unique(merge.all@meta.data$Main_cluster_name) %notin% b$Var1]

 [1] "Corneal and conjunctival epithelial cells"        
 [2] "SLC26A4_PAEP positive cells"                      
 [3] "CSH1_CSH2 positive cells"                         
 [4] "SATB2_LRRC7 positive cells"                       
 [5] "Satellite cells"                                  
 [6] "CCL19_CCL21 positive cells"                       
 [7] "Syncytiotrophoblasts and villous cytotrophoblasts"
 [8] "STC2_TLX1 positive cells"                         
 [9] "PDE1C_ACSM3 positive cells"                       
[10] "Goblet cells"                                     
[11] "Antigen presenting cells"                         
[12] NA

sub<- subset(merge.all, idents= c("EB.5New"))
EB.cell.id<- rownames(sub@meta.data)
sub@meta.data<- cbind(sub@meta.data, EB.cell.id)
sub@meta.data<- full_join(sub@meta.data, CommonAnnDF, by= c("EB.cell.id"))
rownames(sub@meta.data)<- EB.cell.id

V<- DimPlot(sub, group.by="Annotation", pt.size = 0.2, label.size = 2.5,label=T, repel=T) +NoLegend()

V

pdf(file = "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilot_CommonAnnotationsFromFiveReferenceNeighbors.pdf")

V

dev.off()

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] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggplot2_3.3.5    harmony_1.0      Rcpp_1.0.6       purrr_0.3.4     
 [5] tibble_3.1.5     tidyr_1.1.0      dplyr_1.0.2      loomR_0.2.1.9000
 [9] hdf5r_1.3.1      R6_2.5.1         edgeR_3.28.1     limma_3.42.2    
[13] Seurat_3.2.0     workflowr_1.6.2 

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-2      deldir_0.1-28        
  [4] ellipsis_0.3.2        ggridges_0.5.2        rprojroot_2.0.2      
  [7] fs_1.4.2              spatstat.data_1.4-3   farver_2.1.0         
 [10] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [13] bit64_4.0.5           ggrepel_0.9.0         RSpectra_0.16-0      
 [16] fansi_0.5.0           codetools_0.2-16      splines_3.6.1        
 [19] lsei_1.2-0            knitr_1.29            polyclip_1.10-0      
 [22] jsonlite_1.7.2        ica_1.0-2             cluster_2.1.0        
 [25] png_0.1-7             uwot_0.1.10           shiny_1.5.0          
 [28] sctransform_0.2.1     compiler_3.6.1        httr_1.4.2           
 [31] Matrix_1.2-18         fastmap_1.0.1         lazyeval_0.2.2       
 [34] later_1.1.0.1         htmltools_0.5.0       tools_3.6.1          
 [37] rsvd_1.0.3            igraph_1.2.6          gtable_0.3.0         
 [40] glue_1.4.2            RANN_2.6.1            reshape2_1.4.4       
 [43] rappdirs_0.3.3        spatstat_1.64-1       vctrs_0.3.8          
 [46] ape_5.4-1             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_1.0.1      
 [55] irlba_2.3.3           goftest_1.2-2         future_1.18.0        
 [58] MASS_7.3-51.4         zoo_1.8-8             scales_1.1.1         
 [61] promises_1.1.1        spatstat.utils_1.17-0 parallel_3.6.1       
 [64] RColorBrewer_1.1-2    yaml_2.2.1            reticulate_1.20      
 [67] pbapply_1.4-2         gridExtra_2.3         rpart_4.1-15         
 [70] stringi_1.5.3         highr_0.8             rlang_0.4.11         
 [73] pkgconfig_2.0.3       evaluate_0.14         lattice_0.20-38      
 [76] ROCR_1.0-11           tensor_1.5            labeling_0.4.2       
 [79] patchwork_1.1.1       htmlwidgets_1.5.1     bit_4.0.4            
 [82] cowplot_1.1.1         tidyselect_1.1.0      RcppAnnoy_0.0.18     
 [85] plyr_1.8.6            magrittr_2.0.1        generics_0.1.0       
 [88] withr_2.4.2           pillar_1.6.3          whisker_0.4          
 [91] mgcv_1.8-28           fitdistrplus_1.0-14   survival_3.2-3       
 [94] abind_1.4-5           future.apply_1.6.0    crayon_1.4.1         
 [97] KernSmooth_2.23-15    utf8_1.2.2            plotly_4.9.2.1       
[100] rmarkdown_2.3         locfit_1.5-9.4        grid_3.6.1           
[103] data.table_1.13.4     git2r_0.26.1          digest_0.6.28        
[106] xtable_1.8-4          httpuv_1.5.4          munsell_0.5.0        
[109] viridisLite_0.4.0

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] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggplot2_3.3.5    harmony_1.0      Rcpp_1.0.6       purrr_0.3.4     
 [5] tibble_3.1.5     tidyr_1.1.0      dplyr_1.0.2      loomR_0.2.1.9000
 [9] hdf5r_1.3.1      R6_2.5.1         edgeR_3.28.1     limma_3.42.2    
[13] Seurat_3.2.0     workflowr_1.6.2 

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-2      deldir_0.1-28        
  [4] ellipsis_0.3.2        ggridges_0.5.2        rprojroot_2.0.2      
  [7] fs_1.4.2              spatstat.data_1.4-3   farver_2.1.0         
 [10] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [13] bit64_4.0.5           ggrepel_0.9.0         RSpectra_0.16-0      
 [16] fansi_0.5.0           codetools_0.2-16      splines_3.6.1        
 [19] lsei_1.2-0            knitr_1.29            polyclip_1.10-0      
 [22] jsonlite_1.7.2        ica_1.0-2             cluster_2.1.0        
 [25] png_0.1-7             uwot_0.1.10           shiny_1.5.0          
 [28] sctransform_0.2.1     compiler_3.6.1        httr_1.4.2           
 [31] Matrix_1.2-18         fastmap_1.0.1         lazyeval_0.2.2       
 [34] later_1.1.0.1         htmltools_0.5.0       tools_3.6.1          
 [37] rsvd_1.0.3            igraph_1.2.6          gtable_0.3.0         
 [40] glue_1.4.2            RANN_2.6.1            reshape2_1.4.4       
 [43] rappdirs_0.3.3        spatstat_1.64-1       vctrs_0.3.8          
 [46] ape_5.4-1             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_1.0.1      
 [55] irlba_2.3.3           goftest_1.2-2         future_1.18.0        
 [58] MASS_7.3-51.4         zoo_1.8-8             scales_1.1.1         
 [61] promises_1.1.1        spatstat.utils_1.17-0 parallel_3.6.1       
 [64] RColorBrewer_1.1-2    yaml_2.2.1            reticulate_1.20      
 [67] pbapply_1.4-2         gridExtra_2.3         rpart_4.1-15         
 [70] stringi_1.5.3         highr_0.8             rlang_0.4.11         
 [73] pkgconfig_2.0.3       evaluate_0.14         lattice_0.20-38      
 [76] ROCR_1.0-11           tensor_1.5            labeling_0.4.2       
 [79] patchwork_1.1.1       htmlwidgets_1.5.1     bit_4.0.4            
 [82] cowplot_1.1.1         tidyselect_1.1.0      RcppAnnoy_0.0.18     
 [85] plyr_1.8.6            magrittr_2.0.1        generics_0.1.0       
 [88] withr_2.4.2           pillar_1.6.3          whisker_0.4          
 [91] mgcv_1.8-28           fitdistrplus_1.0-14   survival_3.2-3       
 [94] abind_1.4-5           future.apply_1.6.0    crayon_1.4.1         
 [97] KernSmooth_2.23-15    utf8_1.2.2            plotly_4.9.2.1       
[100] rmarkdown_2.3         locfit_1.5-9.4        grid_3.6.1           
[103] data.table_1.13.4     git2r_0.26.1          digest_0.6.28        
[106] xtable_1.8-4          httpuv_1.5.4          munsell_0.5.0        
[109] viridisLite_0.4.0

absolute	relative
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/FiveNewLines.rds	../output/mergedObjects/FiveNewLines.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/hESC1.rawdge.txt.gz	../data/dge/hESC1.rawdge.txt.gz
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/data/dge/iPS-to-EB_20Day_dge.txt.gz	../data/dge/iPS-to-EB_20Day_dge.txt.gz
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds	../output/CaoEtAl.Obj.CellsOfAllClusters.ProteinCodingGenes.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5newlines.merge.all.SCTwRegressOrigIdent.Harmony.rds	../output/5newlines.merge.all.SCTwRegressOrigIdent.Harmony.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_MainClusterID_SCTregress.pdf	../output/pdfs/5NEWLINES.IntegrateReference_UMAP_MainClusterID_SCTregress.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_allLabels_SCTregress.pdf	../output/pdfs/5NEWLINES.IntegrateReference_UMAP_allLabels_SCTregress.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_origident_all_SCTregress.pdf	../output/pdfs/5NEWLINES.IntegrateReference_UMAP_origident_all_SCTregress.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilotAndCao_SCTregress.pdf	../output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilotAndCao_SCTregress.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilotAndCao_groupbyMainClusterName_SCTregress.pdf	../output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilotAndCao_groupbyMainClusterName_SCTregress.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilotAndCao_groupbySeuratClusterRes0.1_SCTregress.pdf	../output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilotAndCao_groupbySeuratClusterRes0.1_SCTregress.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.NearestReferenceCell.Cao.hESC.EuclideanDistanceinHarmonySpace.csv	../output/5NEWLINES.NearestReferenceCell.Cao.hESC.EuclideanDistanceinHarmonySpace.csv
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.NearestReferenceCell.Cao.hESC.FrequencyofEachAnnotation.csv	../output/5NEWLINES.NearestReferenceCell.Cao.hESC.FrequencyofEachAnnotation.csv
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilot_annotationsFromReference.pdf	../output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilot_annotationsFromReference.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.MostCommonAnnotation.FiveNearestRefCells.csv	../output/5NEWLINES.MostCommonAnnotation.FiveNearestRefCells.csv
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/5NEWLINES.Frequency.MostCommonAnnotation.FiveNearestRefCells.csv	../output/5NEWLINES.Frequency.MostCommonAnnotation.FiveNearestRefCells.csv
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilot_CommonAnnotationsFromFiveReferenceNeighbors.pdf	../output/pdfs/5NEWLINES.IntegrateReference_UMAP_EBpilot_CommonAnnotationsFromFiveReferenceNeighbors.pdf