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

workflowr

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Last updated: 2020-08-10

Checks: 7 0

Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/IntegrateAnalysis.afterFilter.NOHARMONYjustmerge.Rmd) and HTML (docs/IntegrateAnalysis.afterFilter.NOHARMONYjustmerge.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
html	421a225	KLRhodes	2020-08-10	Build site.
Rmd	bc8ec6f	KLRhodes	2020-08-10	cleaning various versions of merging/intCurrent working directory

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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)

#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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#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.

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

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Now Running UMAP and identifying clusters, etc

merged<- RunUMAP(merged, reduction = "pca", dims = 1:100, verbose = F)

Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session

merged<- FindNeighbors(merged, reduction="pca", dims = 1:100, verbose = F)

SAVING merged/aligned/reclustered object

path<- here::here("output/mergedObjects/")
saveRDS(merged, file=paste0(path,'merged.noIntegration.rds'))

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

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

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

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

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

individuals <- table(merged$individual)
individuals <- individuals[individuals>50]
individuals <- names(individuals)
for (i in individuals)
{
  print(DimPlot(merged, reduction = "pca", group.by = c("Batch"), pt.size = 0.01,
                cells = WhichCells(merged, expression = individual == i)) +
          xlim(xlim) + ylim(ylim) + ggtitle(i))
}

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DimPlot(merged, reduction = "umap")

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

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

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

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#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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FeaturePlot(merged, features = "nFeature_RNA")

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

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

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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] RColorBrewer_1.1-2 here_0.1-11        DataCombine_0.2.21 ggplot2_3.3.2     
[5] harmony_1.0        Rcpp_1.0.5         Seurat_3.2.0       workflowr_1.6.2   

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_1.4-1      deldir_0.1-28        
  [4] ellipsis_0.3.1        ggridges_0.5.2        rprojroot_1.3-2      
  [7] fs_1.4.2              spatstat.data_1.4-3   farver_2.0.3         
 [10] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [13] ggrepel_0.8.2         RSpectra_0.16-0       codetools_0.2-16     
 [16] splines_3.6.1         lsei_1.2-0            knitr_1.29           
 [19] polyclip_1.10-0       jsonlite_1.7.0        ica_1.0-2            
 [22] cluster_2.1.0         png_0.1-7             uwot_0.1.8           
 [25] shiny_1.5.0           sctransform_0.2.1     compiler_3.6.1       
 [28] httr_1.4.2            backports_1.1.8       Matrix_1.2-18        
 [31] fastmap_1.0.1         lazyeval_0.2.2        later_1.1.0.1        
 [34] htmltools_0.5.0       tools_3.6.1           rsvd_1.0.3           
 [37] igraph_1.2.5          gtable_0.3.0          glue_1.4.1           
 [40] RANN_2.6.1            reshape2_1.4.4        dplyr_1.0.0          
 [43] rappdirs_0.3.1        spatstat_1.64-1       vctrs_0.3.2          
 [46] gdata_2.18.0          ape_5.3               nlme_3.1-140         
 [49] lmtest_0.9-37         xfun_0.16             stringr_1.4.0        
 [52] globals_0.12.5        mime_0.9              miniUI_0.1.1.1       
 [55] lifecycle_0.2.0       irlba_2.3.3           gtools_3.8.2         
 [58] goftest_1.2-2         future_1.18.0         MASS_7.3-51.4        
 [61] zoo_1.8-8             scales_1.1.1          promises_1.1.1       
 [64] spatstat.utils_1.17-0 parallel_3.6.1        yaml_2.2.1           
 [67] reticulate_1.16       pbapply_1.4-2         gridExtra_2.3        
 [70] rpart_4.1-15          stringi_1.4.6         caTools_1.18.0       
 [73] rlang_0.4.7           pkgconfig_2.0.3       bitops_1.0-6         
 [76] evaluate_0.14         lattice_0.20-38       ROCR_1.0-7           
 [79] purrr_0.3.4           tensor_1.5            labeling_0.3         
 [82] patchwork_1.0.1       htmlwidgets_1.5.1     cowplot_1.0.0        
 [85] tidyselect_1.1.0      RcppAnnoy_0.0.16      plyr_1.8.6           
 [88] magrittr_1.5          R6_2.4.1              gplots_3.0.4         
 [91] generics_0.0.2        withr_2.2.0           pillar_1.4.6         
 [94] whisker_0.4           mgcv_1.8-28           fitdistrplus_1.0-14  
 [97] survival_3.2-3        abind_1.4-5           tibble_3.0.3         
[100] future.apply_1.6.0    crayon_1.3.4          KernSmooth_2.23-15   
[103] plotly_4.9.2.1        rmarkdown_2.3         grid_3.6.1           
[106] data.table_1.13.0     git2r_0.26.1          digest_0.6.25        
[109] xtable_1.8-4          tidyr_1.1.0           httpuv_1.5.4         
[112] munsell_0.5.0         viridisLite_0.3.0