Last updated: 2021-07-04

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Knit directory: Embryoid_Body_Pilot_Workflowr/analysis/

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/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds ../output/mergedObjects/Harmony.Batchindividual.rds
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.1.pdf ../output/figs/fig1.1.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.2.pdf ../output/figs/fig1.2.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.1.png ../output/figs/fig1.1.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.2.png ../output/figs/fig1.2.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_ClusterPropHeat.png ../output/figs/Supp_ClusterPropHeat.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_ClusterProp_JUSTLEGEND.png ../output/figs/Supp_ClusterProp_JUSTLEGEND.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_nCountnFeat_RNA.png ../output/figs/Supp_nCountnFeat_RNA.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_nCountnFeat_SCT.png ../output/figs/Supp_nCountnFeat_SCT.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SUPP_DimPlot_res0.5and0.8.png ../output/figs/SUPP_DimPlot_res0.5and0.8.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SUPP_clust22_primitiveStreakVlnPlot.png ../output/figs/SUPP_clust22_primitiveStreakVlnPlot.png

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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Fig1.Rmd) and HTML (docs/Fig1.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
Rmd 068f5cb KLRhodes 2021-07-04 wflow_publish(c("analysis/CompiledFits_BatchvInd.Rmd", "analysis/DownSamp_NoiseRatio.Rmd", 
library(Seurat)
library(patchwork)
library(reshape2)
library(ggplot2)
library(tidyr)

Attaching package: 'tidyr'
The following object is masked from 'package:reshape2':

    smiths
library(ComplexHeatmap)
Loading required package: grid
========================================
ComplexHeatmap version 2.7.4
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================
library(circlize)
========================================
circlize version 0.4.11
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================
library(ggplotify)
library(dplyr)

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

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
merged<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds")
p0<- FeaturePlot(merged, features = c("POU5F1", "SOX17", "HAND1", "PAX6"), cols= c("grey","blue"), combine=T, slot= "data", ncol=4)
p1<- DimPlot(merged, group.by= "SCT_snn_res.1")
p2<- DimPlot(merged, group.by= "SCT_snn_res.0.5")
p4<- DimPlot(merged, group.by= "SCT_snn_res.0.8")
p3<- DimPlot(merged, group.by= "SCT_snn_res.0.1")

make at able of number of cells per group in each cluster at resolution 0.1

test<- merged@meta.data %>% unite(group, c("individual", "Batch"))
gr<- gsub("SNG-NA", "", test$group)
merged<- AddMetaData(merged, metadata = gr, col.name= "group")
tabi<- table(merged@meta.data$group, merged@meta.data$SCT_snn_res.0.1)

manipulate table to prepare for ggplot

tot<- rowSums(tabi)
tabprop<- tabi/tot
dat<- melt(tabprop)

dat$Var2<- factor(dat$Var2, levels = c("6", "5", "4", "3", "2", "1", "0"))
colnames(dat)<- c("Var1", "Cluster_Res0.1", "value")

make stacked barplot of cell type / cluster composition of each line

samps<- c("18511_Rep1","18511_Rep2","18511_Rep3","19160_Rep1","19160_Rep2","19160_Rep3","18858_Rep1","18858_Rep2","18858_Rep3")
clrs2<- c("#8dd3c7", "#ffffb3", "#bebada", "#fb8072", "#80b1d3", "#fdb462", "#b3de69", "#fccde5", "#bc80bd", "#ccebc5", "#ffed6f", "#a6cee3", "#1f78b4", "midnightblue", "#33a02c", "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00", "#cab2d6", "#6a3d9a", "#ffff99", "#b15928", "darkseagreen4", "darkorange3", "darkorchid4", "palevioletred2", "khaki3", "cornsilk3")
V<- ggplot(dat, aes(x= factor(Var1, levels = c("18511_Batch1","18511_Batch2","18511_Batch3","19160_Batch1","19160_Batch2","19160_Batch3","18858_Batch1","18858_Batch2","18858_Batch3")), y=value))+
  geom_col(aes(fill=Cluster_Res0.1), width = 0.8)+
  xlab("sample")+
  ylab("cell type proportion")+
  scale_fill_manual(values = clrs2)+
  theme(axis.text.x = element_text(angle=45, hjust=1))+
  scale_x_discrete(labels=samps)+
  labs(fill = "Cluster (Res. 0.1)")


V

fig1<- p0+p3 +p1
  

fig1

pdf(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.1.pdf", width= 16, height=8)

fig1

dev.off()
png 
  2 
pdf(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.2.pdf", width= 7, height=4)

V

dev.off()
png 
  2 
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.1.png", width= 16, height=8, units= "in", res= 1080)

fig1

dev.off()
png 
  2 
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig1.2.png", width=7, height=4, units= "in", res=1080)

V

dev.off()
png 
  2

Supp Figure showing hierarchical clustering of samples based on cluster proportions

New metadata w/ clean names

#add clean names to samples df
replicate<- merged@meta.data$Batch
replicate[replicate == "Batch1"]<- "Rep1"
replicate[replicate == "Batch2"]<- "Rep2"
replicate[replicate == "Batch3"]<- "Rep3"

ind<- merged@meta.data$individual
ind[ind == "SNG-NA18511"]<- "18511"
ind[ind == "SNG-NA19160"]<- "19160"
ind[ind == "SNG-NA18858"]<- "18858"

merged<- AddMetaData(merged, replicate, col.name = "replicate")
merged<- AddMetaData(merged, ind, col.name = "ind")
merged@meta.data<- merged@meta.data %>% unite(SampGroup, c("ind", "replicate"))
col_fun<- colorRamp2(c(0,0.3, 1), c("white", '#117733', "black"))
hp<- list()
res<- c("SCT_snn_res.0.1", "SCT_snn_res.0.5", "SCT_snn_res.0.8", "SCT_snn_res.1")
fortitle<- c("0.1", "0.5", "0.8", "1")

for (i in 1:length(res)){
v<- res[i]
tabi<- table(merged@meta.data$SampGroup, merged@meta.data[,v])
tot<- rowSums(tabi)
tabprop<- tabi/tot

m<- t(as.data.frame.matrix(tabprop))

hp[[i]]<-Heatmap(m, col= col_fun, show_heatmap_legend = FALSE, row_names_gp = gpar(fontsize = 8), column_title = paste0("Resolution ", fortitle[i]), cluster_rows= FALSE)
}

hp
[[1]]


[[2]]


[[3]]


[[4]]

all<-(as.ggplot(hp[[1]]) + as.ggplot(hp[[2]])+ as.ggplot(hp[[3]]) + as.ggplot(hp[[4]]))
all

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_ClusterPropHeat.png", width=5, height=8.5, units= "in", res=1080)

all

dev.off()
png 
  2 
lgd<- Legend(col_fun = col_fun, title="Proportion \nof cells")

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_ClusterProp_JUSTLEGEND.png", width=1, height=2, units= "in", res=1080)

draw(lgd)

dev.off()
png 
  2 
Idents(merged)<- 'SampGroup'
a<-VlnPlot(merged, features = 'nCount_RNA', pt.size=0)+NoLegend()
a

b<-VlnPlot(merged, features = 'nFeature_RNA', pt.size=0)+NoLegend()
b

c<-VlnPlot(merged, features = 'nCount_SCT', pt.size=0)+NoLegend()
c

d<-VlnPlot(merged, features = 'nFeature_SCT', pt.size=0)+NoLegend()
d

a|b

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_nCountnFeat_RNA.png", width= 7.5, height=3, units= "in", res= 1080)

a|b

dev.off()
png 
  2 
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Supp_nCountnFeat_SCT.png", width= 7.5, height=3, units= "in", res= 1080)

c|d

dev.off()
png 
  2 
G<- DimPlot(merged, group.by = "SCT_snn_res.0.5", pt.size = 0.005)
H<- DimPlot(merged, group.by = "SCT_snn_res.0.8", pt.size = 0.005)

G|H

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SUPP_DimPlot_res0.5and0.8.png", width=9, height=5, units= "in", res=1080)

G|H

dev.off()
png 
  2 
w<- VlnPlot(merged, features= c("EOMES", "MIXL1"), group.by= "SCT_snn_res.1", pt.size=0)
w

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SUPP_clust22_primitiveStreakVlnPlot.png", width=11, height=4, units= "in", res=1080)

w

dev.off()
png 
  2 
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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] dplyr_1.0.2          ggplotify_0.0.5      circlize_0.4.11     
 [4] ComplexHeatmap_2.7.4 tidyr_1.1.0          ggplot2_3.3.3       
 [7] reshape2_1.4.4       patchwork_1.1.1      Seurat_3.2.0        
[10] workflowr_1.6.2     

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-0      rjson_0.2.20         
  [4] deldir_0.1-28         ellipsis_0.3.1        ggridges_0.5.2       
  [7] rprojroot_2.0.2       GlobalOptions_0.1.2   fs_1.4.2             
 [10] clue_0.3-58           spatstat.data_1.4-3   farver_2.0.3         
 [13] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [16] ggrepel_0.9.0         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        Cairo_1.5-12.2        ica_1.0-2            
 [25] cluster_2.1.0         png_0.1-7             uwot_0.1.10          
 [28] shiny_1.5.0           sctransform_0.2.1     BiocManager_1.30.10  
 [31] compiler_3.6.1        httr_1.4.2            rvcheck_0.1.8        
 [34] Matrix_1.2-18         fastmap_1.0.1         lazyeval_0.2.2       
 [37] later_1.1.0.1         htmltools_0.5.0       tools_3.6.1          
 [40] rsvd_1.0.3            igraph_1.2.6          gtable_0.3.0         
 [43] glue_1.4.2            RANN_2.6.1            rappdirs_0.3.3       
 [46] Rcpp_1.0.6            spatstat_1.64-1       vctrs_0.3.6          
 [49] gdata_2.18.0          ape_5.4-1             nlme_3.1-140         
 [52] lmtest_0.9-37         xfun_0.16             stringr_1.4.0        
 [55] globals_0.12.5        mime_0.9              miniUI_0.1.1.1       
 [58] lifecycle_0.2.0       irlba_2.3.3           gtools_3.8.2         
 [61] goftest_1.2-2         future_1.18.0         MASS_7.3-51.4        
 [64] zoo_1.8-8             scales_1.1.1          promises_1.1.1       
 [67] spatstat.utils_1.17-0 parallel_3.6.1        RColorBrewer_1.1-2   
 [70] yaml_2.2.1            reticulate_1.20       pbapply_1.4-2        
 [73] gridExtra_2.3         rpart_4.1-15          stringi_1.5.3        
 [76] highr_0.8             S4Vectors_0.24.4      BiocGenerics_0.32.0  
 [79] caTools_1.18.0        shape_1.4.5           matrixStats_0.57.0   
 [82] rlang_0.4.10          pkgconfig_2.0.3       bitops_1.0-6         
 [85] evaluate_0.14         lattice_0.20-38       ROCR_1.0-7           
 [88] purrr_0.3.4           tensor_1.5            labeling_0.4.2       
 [91] htmlwidgets_1.5.1     cowplot_1.1.1         tidyselect_1.1.0     
 [94] RcppAnnoy_0.0.18      plyr_1.8.6            magrittr_2.0.1       
 [97] R6_2.5.0              magick_2.4.0          IRanges_2.20.2       
[100] gplots_3.0.4          generics_0.1.0        withr_2.4.2          
[103] pillar_1.4.7          whisker_0.4           mgcv_1.8-28          
[106] fitdistrplus_1.0-14   survival_3.2-3        abind_1.4-5          
[109] tibble_3.0.4          future.apply_1.6.0    crayon_1.3.4         
[112] KernSmooth_2.23-15    plotly_4.9.2.1        rmarkdown_2.3        
[115] GetoptLong_1.0.5      data.table_1.13.4     git2r_0.26.1         
[118] digest_0.6.27         xtable_1.8-4          httpuv_1.5.4         
[121] gridGraphics_0.5-0    stats4_3.6.1          munsell_0.5.0        
[124] viridisLite_0.3.0    

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] C

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

other attached packages:
 [1] dplyr_1.0.2          ggplotify_0.0.5      circlize_0.4.11     
 [4] ComplexHeatmap_2.7.4 tidyr_1.1.0          ggplot2_3.3.3       
 [7] reshape2_1.4.4       patchwork_1.1.1      Seurat_3.2.0        
[10] workflowr_1.6.2     

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-0      rjson_0.2.20         
  [4] deldir_0.1-28         ellipsis_0.3.1        ggridges_0.5.2       
  [7] rprojroot_2.0.2       GlobalOptions_0.1.2   fs_1.4.2             
 [10] clue_0.3-58           spatstat.data_1.4-3   farver_2.0.3         
 [13] leiden_0.3.3          listenv_0.8.0         npsurv_0.4-0         
 [16] ggrepel_0.9.0         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        Cairo_1.5-12.2        ica_1.0-2            
 [25] cluster_2.1.0         png_0.1-7             uwot_0.1.10          
 [28] shiny_1.5.0           sctransform_0.2.1     BiocManager_1.30.10  
 [31] compiler_3.6.1        httr_1.4.2            rvcheck_0.1.8        
 [34] Matrix_1.2-18         fastmap_1.0.1         lazyeval_0.2.2       
 [37] later_1.1.0.1         htmltools_0.5.0       tools_3.6.1          
 [40] rsvd_1.0.3            igraph_1.2.6          gtable_0.3.0         
 [43] glue_1.4.2            RANN_2.6.1            rappdirs_0.3.3       
 [46] Rcpp_1.0.6            spatstat_1.64-1       vctrs_0.3.6          
 [49] gdata_2.18.0          ape_5.4-1             nlme_3.1-140         
 [52] lmtest_0.9-37         xfun_0.16             stringr_1.4.0        
 [55] globals_0.12.5        mime_0.9              miniUI_0.1.1.1       
 [58] lifecycle_0.2.0       irlba_2.3.3           gtools_3.8.2         
 [61] goftest_1.2-2         future_1.18.0         MASS_7.3-51.4        
 [64] zoo_1.8-8             scales_1.1.1          promises_1.1.1       
 [67] spatstat.utils_1.17-0 parallel_3.6.1        RColorBrewer_1.1-2   
 [70] yaml_2.2.1            reticulate_1.20       pbapply_1.4-2        
 [73] gridExtra_2.3         rpart_4.1-15          stringi_1.5.3        
 [76] highr_0.8             S4Vectors_0.24.4      BiocGenerics_0.32.0  
 [79] caTools_1.18.0        shape_1.4.5           matrixStats_0.57.0   
 [82] rlang_0.4.10          pkgconfig_2.0.3       bitops_1.0-6         
 [85] evaluate_0.14         lattice_0.20-38       ROCR_1.0-7           
 [88] purrr_0.3.4           tensor_1.5            labeling_0.4.2       
 [91] htmlwidgets_1.5.1     cowplot_1.1.1         tidyselect_1.1.0     
 [94] RcppAnnoy_0.0.18      plyr_1.8.6            magrittr_2.0.1       
 [97] R6_2.5.0              magick_2.4.0          IRanges_2.20.2       
[100] gplots_3.0.4          generics_0.1.0        withr_2.4.2          
[103] pillar_1.4.7          whisker_0.4           mgcv_1.8-28          
[106] fitdistrplus_1.0-14   survival_3.2-3        abind_1.4-5          
[109] tibble_3.0.4          future.apply_1.6.0    crayon_1.3.4         
[112] KernSmooth_2.23-15    plotly_4.9.2.1        rmarkdown_2.3        
[115] GetoptLong_1.0.5      data.table_1.13.4     git2r_0.26.1         
[118] digest_0.6.27         xtable_1.8-4          httpuv_1.5.4         
[121] gridGraphics_0.5-0    stats4_3.6.1          munsell_0.5.0        
[124] viridisLite_0.3.0