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Knit directory: Embryoid_Body_Pilot_Workflowr/

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library(knitr)
library(Seurat)
library(Matrix)
library(DropletUtils)
library(ggplot2)
library(dplyr)
library(here)

load rds

samp.obj<- readRDS('/project2/gilad/katie/Pilot_HumanEBs/HumanEB_QCmetadata/Batch1_Lane1_seurat_NoNorm_QCMetaAdded.rds')
qc<- knitr::knit_expand(file = here::here("analysis/child/SampleFilteringAndSCT.Rmd"))

load libraries

library(Seurat)
library(Matrix)
library(DropletUtils)
library(ggplot2)
library(dplyr)
options(future.globals.maxSize= 4000*1024^2) # allow global exceeding 4Gb
#print(params$samp)
#samp.obj<- readRDS(paste0(params$samp),".rds"))
head(samp.obj)
An object of class Seurat 
1 features across 4702 samples within 1 assay 
Active assay: RNA (1 features, 0 variable features)
#remove doublets
samp.obj<- subset(samp.obj, subset = individual != 'Doublet')

#remove cells assigned to individuals not actually in this sample
samp.obj<- subset(samp.obj, subset = individual != 'SNG-SCM-12.variant')
samp.obj<- subset(samp.obj, subset = individual != 'SNG-SCM-13.variant')
samp.obj<- subset(samp.obj, subset = individual != 'SNG-SCM-6.variant')
samp.obj<- subset(samp.obj, subset = individual != 'SNG-SCM-7.variant')
samp.obj<- subset(samp.obj, subset = individual != 'SNG-SCM-8.variant')
head(samp.obj)
An object of class Seurat 
1 features across 3550 samples within 1 assay 
Active assay: RNA (1 features, 0 variable features)
samp.obj<- subset(samp.obj, subset = EmptyDrops.FDR < .0001) # very strict, may lose some "good" cells, but limits set to high quality cells and there are enough cells remaining for downstream analyses
head(samp.obj)
An object of class Seurat 
1 features across 2442 samples within 1 assay 
Active assay: RNA (1 features, 0 variable features)
VlnPlot(samp.obj, features= "percent.mt", group.by = "individual", pt.size = 0)

#visualize feature-feature relationships with FeatureScatter
plot1<- FeatureScatter(samp.obj, feature1 = "nCount_RNA", feature2 = "percent.mt", group.by = "individual")
plot2<- FeatureScatter(samp.obj, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", group.by = "individual")
CombinePlots(plots= list(plot1, plot2), legend = "right")
Warning: CombinePlots is being deprecated. Plots should now be combined using
the patchwork system.

#filtering out cells with high percent.mt. cutting cells with >20% mt based on the figure above and previous analysis of processed data. 
samp.obj<- subset(samp.obj, subset = percent.mt < 20)
samp.obj<- subset(samp.obj, subset = percent.mt > 3) #because low mt cells cluster together into cluster of low quality cells downstream, can eliminate them here. this is very strict and will throw out potentially good cells from "good" clusters too. but those clusters have a range of mt percentage and will not be eliminated entirely like the junk clusters will.
head(samp.obj)
An object of class Seurat 
1 features across 2003 samples within 1 assay 
Active assay: RNA (1 features, 0 variable features)
VlnPlot(samp.obj, features = "nCount_RNA",group.by = "individual", pt.size = 0)

VlnPlot(samp.obj, features = "nFeature_RNA",group.by = "individual", pt.size = 0)

head(samp.obj)
An object of class Seurat 
1 features across 2003 samples within 1 assay 
Active assay: RNA (1 features, 0 variable features)
summary(samp.obj$nFeature_RNA)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1577    2482    3086    3539    4164    9632 
#set a feature threshold
samp.obj<- subset(samp.obj, subset = nFeature_RNA > 1500) #again, very strict, may lose some "good" cells, but limits set to high quality cells and there are enough cells remaining for downstream analyses
head(samp.obj)
An object of class Seurat 
1 features across 2003 samples within 1 assay 
Active assay: RNA (1 features, 0 variable features)
#in seurat SCTransform vignette, they regress out MT percentage, but we don't. Reasoning is that MT percentage probably correlates with cell type for us so its still useful information for clustering.

samp.obj<- suppressWarnings(SCTransform(samp.obj, verbose=F))
samp.obj<- RunPCA(samp.obj, npcs= 100, verbose = F)
DimPlot(samp.obj, reduction = "pca")

DimPlot(samp.obj, reduction = "pca", group.by = "individual")

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

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

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

xlim <- c(min(samp.obj@reductions$pca@cell.embeddings[,'PC_1']),
          max(samp.obj@reductions$pca@cell.embeddings[,'PC_1']))
ylim <- c(min(samp.obj@reductions$pca@cell.embeddings[,'PC_2']),
          max(samp.obj@reductions$pca@cell.embeddings[,'PC_2']))

individuals <- table(samp.obj$individual)
individuals <- individuals[individuals>50]
individuals <- names(individuals)
for (i in individuals)
{
  print(DimPlot(samp.obj, reduction = "pca", 
                cells = WhichCells(samp.obj, expression = individual == i)) +
          xlim(xlim) + ylim(ylim) + ggtitle(i))
}

#Keep all dims that explain more than 1% of variance
pva<- samp.obj@reductions$pca@stdev^2/samp.obj@reductions$pca@misc$total.variance
ndim <- length(which(pva>=0.01))
ElbowPlot(samp.obj, ndims = ndim*2) + geom_vline(xintercept=ndim, linetype="dashed", color = "red")

ndim
[1] 7
samp.clust<- FindNeighbors(samp.obj, dims = 1:ndim, verbose = F)
samp.clust<- FindClusters(samp.clust, verbose = F)
samp.clust<- RunUMAP(samp.clust, dims=1:ndim, 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
samp.clust<- RunTSNE(samp.clust, dims=1:ndim, verbose = F)
DimPlot(samp.clust, reduction = "umap")

DimPlot(samp.clust, reduction = "umap", group.by = "orig.ident")

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

xlim <- c(min(samp.clust@reductions$umap@cell.embeddings[,'UMAP_1']),
          max(samp.clust@reductions$umap@cell.embeddings[,'UMAP_1']))
ylim <- c(min(samp.clust@reductions$umap@cell.embeddings[,'UMAP_2']),
          max(samp.clust@reductions$umap@cell.embeddings[,'UMAP_2']))
for (i in individuals)
{
  print(DimPlot(samp.clust, reduction = "umap", 
                cells = WhichCells(samp.clust, expression = individual == i)) +
          xlim(xlim) + ylim(ylim) + ggtitle(i))
}

FeaturePlot(samp.clust, features = c("POU5F1", "PAX6", "HAND1", "SOX17"), pt.size = 0.2, ncol=3)

FeaturePlot(samp.clust, features = c("ALB", "AFP", "HNF4A", "GFB", "TTR", "ASGR1"), pt.size = 0.2, ncol=3)
Warning: Could not find ALB in the default search locations, found in RNA assay
instead
Warning in FetchData(object = object, vars = c(dims, "ident", features), : The
following requested variables were not found: GFB

DimPlot(samp.clust, reduction = "tsne")

DimPlot(samp.clust, reduction = "tsne", group.by = "orig.ident")

DimPlot(samp.clust, reduction = "tsne", group.by = "individual")

xlim <- c(min(samp.clust@reductions$tsne@cell.embeddings[,'tSNE_1']),
          max(samp.clust@reductions$tsne@cell.embeddings[,'tSNE_1']))
ylim <- c(min(samp.clust@reductions$tsne@cell.embeddings[,'tSNE_2']),
          max(samp.clust@reductions$tsne@cell.embeddings[,'tSNE_2']))
for (i in individuals)
{
  print(DimPlot(samp.clust, reduction = "tsne", 
                cells = WhichCells(samp.clust, expression = individual == i)) +
          xlim(xlim) + ylim(ylim) + ggtitle(i))
}

samp<- samp.clust@meta.data$SampleID[1]
saveRDS(samp.clust, file=paste0('/project2/gilad/katie/Pilot_HumanEBs/SampleQCrds/',samp,'.seurat.rds'))
#Not rerunning the save, already these saved. 
FeaturePlot(samp.clust, features = "nFeature_RNA")

FeaturePlot(samp.clust, features = "nCount_RNA")

VlnPlot(samp.clust, features = "nFeature_RNA", pt.size = 0)

VlnPlot(samp.clust, features = "percent.mt", pt.size = 0)

#how many cells per cluster from each individual?
table(samp.clust@meta.data$SCT_snn_res.0.8, samp.clust@meta.data$individual)
    
     SNG-NA18511.variant2 SNG-NA18858.variant2 SNG-NA19160.variant2
  0                     1                  298                    2
  1                   118                   33                   57
  2                    68                    5                   48
  3                     9                    5                   66
  4                    32                    2                   52
  5                     6                    0                   78
  6                    20                    4                   35
  7                    25                    6                   30
  8                    17                    2                   43
  9                     7                   86                    6
  10                   18                    6                   17
  11                    2                    0                   17
  12                    2                    0                   22
    
     SNG-SCM-10.variant SNG-SCM-9.variant
  0                   0                 1
  1                  50                24
  2                  46                50
  3                  26                74
  4                  46                44
  5                  28                43
  6                  45                29
  7                  23                30
  8                  26                22
  9                   0                 4
  10                 33                26
  11                 21                36
  12                 12                19

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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] here_0.1-11                 dplyr_1.0.0                
 [3] ggplot2_3.3.2               DropletUtils_1.6.1         
 [5] SingleCellExperiment_1.8.0  SummarizedExperiment_1.16.1
 [7] DelayedArray_0.12.3         BiocParallel_1.20.1        
 [9] matrixStats_0.56.0          Biobase_2.46.0             
[11] GenomicRanges_1.38.0        GenomeInfoDb_1.22.1        
[13] IRanges_2.20.2              S4Vectors_0.24.4           
[15] BiocGenerics_0.32.0         Matrix_1.2-18              
[17] Seurat_3.2.0                knitr_1.29                 
[19] workflowr_1.6.2            

loaded via a namespace (and not attached):
  [1] backports_1.1.8        plyr_1.8.6             igraph_1.2.5          
  [4] lazyeval_0.2.2         splines_3.6.1          listenv_0.8.0         
  [7] digest_0.6.25          htmltools_0.5.0        gdata_2.18.0          
 [10] magrittr_1.5           tensor_1.5             cluster_2.1.0         
 [13] ROCR_1.0-7             limma_3.42.2           globals_0.12.5        
 [16] R.utils_2.9.2          colorspace_1.4-1       rappdirs_0.3.1        
 [19] ggrepel_0.8.2          xfun_0.16              crayon_1.3.4          
 [22] RCurl_1.98-1.2         jsonlite_1.7.0         spatstat_1.64-1       
 [25] spatstat.data_1.4-3    survival_3.2-3         zoo_1.8-8             
 [28] ape_5.3                glue_1.4.1             polyclip_1.10-0       
 [31] gtable_0.3.0           zlibbioc_1.32.0        XVector_0.26.0        
 [34] leiden_0.3.3           Rhdf5lib_1.8.0         future.apply_1.6.0    
 [37] HDF5Array_1.14.4       abind_1.4-5            scales_1.1.1          
 [40] edgeR_3.28.1           miniUI_0.1.1.1         Rcpp_1.0.5            
 [43] viridisLite_0.3.0      xtable_1.8-4           dqrng_0.2.1           
 [46] reticulate_1.16        rsvd_1.0.3             htmlwidgets_1.5.1     
 [49] httr_1.4.2             gplots_3.0.4           RColorBrewer_1.1-2    
 [52] ellipsis_0.3.1         ica_1.0-2              farver_2.0.3          
 [55] pkgconfig_2.0.3        R.methodsS3_1.8.0      uwot_0.1.8            
 [58] deldir_0.1-28          locfit_1.5-9.4         labeling_0.3          
 [61] tidyselect_1.1.0       rlang_0.4.7            reshape2_1.4.4        
 [64] later_1.1.0.1          munsell_0.5.0          tools_3.6.1           
 [67] generics_0.0.2         ggridges_0.5.2         evaluate_0.14         
 [70] stringr_1.4.0          fastmap_1.0.1          yaml_2.2.1            
 [73] goftest_1.2-2          npsurv_0.4-0           fs_1.4.2              
 [76] fitdistrplus_1.0-14    caTools_1.18.0         purrr_0.3.4           
 [79] RANN_2.6.1             pbapply_1.4-2          future_1.18.0         
 [82] nlme_3.1-140           whisker_0.4            mime_0.9              
 [85] R.oo_1.23.0            compiler_3.6.1         plotly_4.9.2.1        
 [88] png_0.1-7              lsei_1.2-0             spatstat.utils_1.17-0 
 [91] tibble_3.0.3           stringi_1.4.6          RSpectra_0.16-0       
 [94] lattice_0.20-38        vctrs_0.3.2            pillar_1.4.6          
 [97] lifecycle_0.2.0        lmtest_0.9-37          RcppAnnoy_0.0.16      
[100] data.table_1.13.0      cowplot_1.0.0          bitops_1.0-6          
[103] irlba_2.3.3            httpuv_1.5.4           patchwork_1.0.1       
[106] R6_2.4.1               promises_1.1.1         KernSmooth_2.23-15    
[109] gridExtra_2.3          codetools_0.2-16       MASS_7.3-51.4         
[112] gtools_3.8.2           rhdf5_2.30.1           rprojroot_1.3-2       
[115] withr_2.2.0            sctransform_0.2.1      GenomeInfoDbData_1.2.2
[118] mgcv_1.8-28            grid_3.6.1             rpart_4.1-15          
[121] tidyr_1.1.0            rmarkdown_2.3          Rtsne_0.15            
[124] git2r_0.26.1           shiny_1.5.0