Last updated: 2021-07-05

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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

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File Version Author Date Message
html aca99c7 KLRhodes 2020-08-04 Build site.
Rmd a7fdd37 KLRhodes 2020-08-04 wflow_publish(c("analysis/SampleFilteringandSCT_Batch1Lane3.Rmd", 
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_Lane4_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

Note: This gets run after EB.metadata.Rmd (located in code directory), which read star solo output, adds metadata for species, runs emptydrops and adds QC metadata, runs demuxlet and adds individual metadata, and %MT and %ribosomal to the metadata. This facilitates filtering done here.

head(samp.obj)
An object of class Seurat 
1 features across 3726 samples within 1 assay 
Active assay: RNA (1 features, 0 variable features)
#remove doublets (and chimp, which are assigned to doublet in this case)
samp.obj<- subset(samp.obj, subset = individual != 'Doublet')

#remove cells assigned to individuals not actually in this sample. presumably these are low quality cells that could not be accurately assigned, so their closest match was another individual in the vcf file.
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')

#remove non-YRI human individuals (non-YRI humans only included in 1 replicate, so not needed for exploration of bio and tech variance)
samp.obj<- subset(samp.obj, subset = individual != 'SNG-SCM-10.variant')
samp.obj<- subset(samp.obj, subset = individual != 'SNG-SCM-9.variant')
head(samp.obj)
An object of class Seurat 
1 features across 1847 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 1495 samples within 1 assay 
Active assay: RNA (1 features, 0 variable features)
VlnPlot(samp.obj, features= "percent.mt", group.by = "individual", pt.size = 0)

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#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.

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#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 1112 samples within 1 assay 
Active assay: RNA (1 features, 0 variable features)
VlnPlot(samp.obj, features = "nCount_RNA",group.by = "individual", pt.size = 0)

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18858 often has higher count/higher feature count. Based on downstream analyses, lots of 18858 remained in a pluripotent state and iPSCs tend to have higher counts than other cells types.

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

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head(samp.obj)
An object of class Seurat 
1 features across 1112 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. 
   1563    2621    3378    4036    5532    9419 
#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 1112 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")

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

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

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

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

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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))
}

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#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")

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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")

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

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

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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))
}

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

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above shows the expression of marker genes in UMAP space. POU5F1- pluripotent marker SOX17- endoderm marker HAND1- mesoderm marker PAX6- early ectoderm marker

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

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

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

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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))
}

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samp<- samp.clust@meta.data$SampleID[1]
path<- here::here("output/sampleQCrds/")
saveRDS(samp.clust, file=paste0(path,samp,'.seurat.rds'))
FeaturePlot(samp.clust, features = "nFeature_RNA")

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

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

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

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#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                     8                  205                    5
  1                    38                   24                   78
  2                     1                  123                    0
  3                    78                    4                   33
  4                     7                    0                   88
  5                    23                    0                   70
  6                     9                    3                   70
  7                    27                    3                   45
  8                    74                    0                    0
  9                    24                    0                   38
  10                   22                    5                    7

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_1.0.2                
 [3] ggplot2_3.3.3               DropletUtils_1.6.1         
 [5] SingleCellExperiment_1.8.0  SummarizedExperiment_1.16.1
 [7] DelayedArray_0.12.3         BiocParallel_1.20.1        
 [9] matrixStats_0.57.0          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] plyr_1.8.6             igraph_1.2.6           lazyeval_0.2.2        
  [4] splines_3.6.1          listenv_0.8.0          digest_0.6.27         
  [7] htmltools_0.5.0        gdata_2.18.0           magrittr_2.0.1        
 [10] tensor_1.5             cluster_2.1.0          ROCR_1.0-7            
 [13] limma_3.42.2           globals_0.12.5         R.utils_2.9.2         
 [16] colorspace_2.0-0       rappdirs_0.3.3         ggrepel_0.9.0         
 [19] xfun_0.16              crayon_1.3.4           RCurl_1.98-1.2        
 [22] jsonlite_1.7.2         spatstat_1.64-1        spatstat.data_1.4-3   
 [25] survival_3.2-3         zoo_1.8-8              ape_5.4-1             
 [28] glue_1.4.2             polyclip_1.10-0        gtable_0.3.0          
 [31] zlibbioc_1.32.0        XVector_0.26.0         leiden_0.3.3          
 [34] Rhdf5lib_1.8.0         future.apply_1.6.0     HDF5Array_1.14.4      
 [37] abind_1.4-5            scales_1.1.1           edgeR_3.28.1          
 [40] miniUI_0.1.1.1         Rcpp_1.0.6             viridisLite_0.3.0     
 [43] xtable_1.8-4           reticulate_1.20        dqrng_0.2.1           
 [46] rsvd_1.0.3             htmlwidgets_1.5.1      httr_1.4.2            
 [49] gplots_3.0.4           RColorBrewer_1.1-2     ellipsis_0.3.1        
 [52] ica_1.0-2              farver_2.0.3           pkgconfig_2.0.3       
 [55] R.methodsS3_1.8.0      uwot_0.1.10            deldir_0.1-28         
 [58] locfit_1.5-9.4         labeling_0.4.2         tidyselect_1.1.0      
 [61] rlang_0.4.10           reshape2_1.4.4         later_1.1.0.1         
 [64] munsell_0.5.0          tools_3.6.1            generics_0.1.0        
 [67] ggridges_0.5.2         evaluate_0.14          stringr_1.4.0         
 [70] fastmap_1.0.1          yaml_2.2.1             goftest_1.2-2         
 [73] npsurv_0.4-0           fs_1.4.2               fitdistrplus_1.0-14   
 [76] caTools_1.18.0         purrr_0.3.4            RANN_2.6.1            
 [79] pbapply_1.4-2          future_1.18.0          nlme_3.1-140          
 [82] whisker_0.4            mime_0.9               R.oo_1.23.0           
 [85] compiler_3.6.1         plotly_4.9.2.1         png_0.1-7             
 [88] lsei_1.2-0             spatstat.utils_1.17-0  tibble_3.0.4          
 [91] stringi_1.5.3          RSpectra_0.16-0        lattice_0.20-38       
 [94] vctrs_0.3.6            pillar_1.4.7           lifecycle_0.2.0       
 [97] lmtest_0.9-37          RcppAnnoy_0.0.18       data.table_1.13.4     
[100] cowplot_1.1.1          bitops_1.0-6           irlba_2.3.3           
[103] httpuv_1.5.4           patchwork_1.1.1        R6_2.5.0              
[106] promises_1.1.1         KernSmooth_2.23-15     gridExtra_2.3         
[109] codetools_0.2-16       MASS_7.3-51.4          gtools_3.8.2          
[112] rhdf5_2.30.1           rprojroot_2.0.2        withr_2.4.2           
[115] sctransform_0.2.1      GenomeInfoDbData_1.2.2 mgcv_1.8-28           
[118] grid_3.6.1             rpart_4.1-15           tidyr_1.1.0           
[121] rmarkdown_2.3          Rtsne_0.15             git2r_0.26.1          
[124] shiny_1.5.0