Last updated: 2021-07-05

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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/fig3.structure.png ../output/figs/fig3.structure.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig3.structure_xSeuratClust0.1.png ../output/figs/fig3.structure_xSeuratClust0.1.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SuppFig.k6topicUMAPs.png ../output/figs/SuppFig.k6topicUMAPs.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig3.k6topic1.UMAP.png ../output/figs/Fig3.k6topic1.UMAP.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SuppFig_LoadingsxClust.k6.pdf ../output/figs/SuppFig_LoadingsxClust.k6.pdf
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig3.k6topic1.SeuratClusters0.1.loadingsplot.png ../output/figs/Fig3.k6topic1.SeuratClusters0.1.loadingsplot.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig3.k6topic1.SeuratClusters1.loadingsplot.png ../output/figs/Fig3.k6topic1.SeuratClusters1.loadingsplot.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SuppFig_k6volcanoplots.png ../output/figs/SuppFig_k6volcanoplots.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig3_k6.topic1volc.png ../output/figs/Fig3_k6.topic1volc.png
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TopicModelling_k6_top10drivergenes.byBeta.csv ../output/TopicModelling_k6_top10drivergenes.byBeta.csv
/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TopicModelling_k6_top15drivergenes.byZ.csv ../output/TopicModelling_k6_top15drivergenes.byZ.csv

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library(fastTopics)
library(Matrix)
library(ggplot2)
library(Seurat)
library(cowplot)
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
library(tibble)

load data

load("/project2/gilad/katie/Pilot_HumanEBs/fastTopics/prepared_data_YorubaOnly_genesExpressedInMoreThan10Cells.RData")
merged<-readRDS("/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/mergedObjects/Harmony.Batchindividual.rds")
load("/project2/gilad/katie/Pilot_HumanEBs/fastTopics/pathways/diff_count_res_scd-ex-k=6.RData")
fit<- readRDS("/project2/gilad/katie/Pilot_HumanEBs/fastTopics/fit-scd-ex-k=6.rds")$fit
summary(fit)
Model overview:
  Number of data rows, n: 42488
  Number of data cols, m: 17623
  Rank/Number of topics, k: 6
Evaluation of fit (1500 updates performed):
  Log-likelihood: -5.187201015829e+08
  Deviance: +5.212447676848e+08
  Max KKT residual: +2.760543e-03
Size factors:
      Min        1Q    Median        3Q       Max 
  3455.00   9001.00  16712.00  34599.25 162277.00 
Topic proportions:
    <0.1 0.1-0.5 0.5-0.9 >0.9
k1 40909    1295     250   34
k2 20001    7186   15285   16
k3 11692   30769      27    0
k4 35857    4590    2039    2
k5 20514    9243   12605  126
k6 15027   26566     895    0
Topic representatives:
                                 k1    k2    k3    k4    k5    k6
Batch1_Lane4_GCCAGCAAGGAACTCG 1.000 0.000 0.000 0.000 0.000 0.000
Batch1_Lane6_TATTTCGAGATGGTCG 0.000 0.925 0.000 0.021 0.003 0.050
Batch3_Lane3_CCCTGATTCCCTTGGT 0.000 0.366 0.592 0.000 0.042 0.000
Batch2_Lane2_TGTTACTTCAACACCA 0.000 0.003 0.034 0.960 0.003 0.000
Batch1_Lane8_TCACATTCACACAGAG 0.002 0.011 0.016 0.000 0.963 0.008
Batch2_Lane4_AAGACAAGTAAGGTCG 0.000 0.000 0.033 0.014 0.169 0.784
#structure plot
clrs2<- c("#8dd3c7", "#ffffb3", "#bebada", "#fb8072", "#80b1d3", "#fdb462", "#b3de69", "#fccde5", "#d9d9d9", "#bc80bd", "#ccebc5", "#ffed6f", "#a6cee3", "#1f78b4", "midnightblue", "#33a02c", "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00", "#cab2d6", "#6a3d9a", "#ffff99", "#b15928", "darkseagreen4", "darkorange3", "darkorchid4", "palevioletred2", "khaki3", "cornsilk3")
V<- structure_plot(fit,topics=c("k6","k1","k3","k4","k5","k2"), n=5000,num_threads=5, perplexity = 1000, colors = clrs2, verbose=F, font.size= 20)

V

#grouped according to the 1-d t-SNE embedding
#
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig3.structure.png", width=6, height=3, units= "in", res=1080)

V

dev.off()
#structure plot divided by seurat clusters at various resolutions

B<- structure_plot(fit,
               grouping=factor(merged@meta.data$SCT_snn_res.0.1, c("0", "1", "2", "3", "4", "5", "6")),
               topics =c("k6","k1","k3","k4","k5","k2"),
               gap=100, 
               perplexity=15,
               num_threads = 4, 
               n=10000, 
               font.size=20,
               verbose = F)

B

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/fig3.structure_xSeuratClust0.1.png", width=8, height=3, units= "in", res=1080)

B

dev.off()
R<-structure_plot(fit,
               grouping=factor(merged@meta.data$SCT_snn_res.0.5),
               topics = c("k6","k1","k3","k4","k5","k2"),
               gap=100, 
               perplexity=15,
               num_threads = 4, 
               n=10000, 
               font.size=20,
               verbose = F)

R

names<- paste0("k6.",colnames(fit$L))
merged<- AddMetaData(merged, poisson2multinom(fit)$L, col.name = names)
feat<- list()
for(i in 1:ncol(fit$F)){
  p<- FeaturePlot(merged, features = paste0("k6.k",i))
  p<- AugmentPlot(p)
  feat[[i]]<-p+ggtitle(paste0("Topic ", i))
}
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.
feat
[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SuppFig.k6topicUMAPs.png", width=6, height=4, units= "in", res=1080)

feat[[1]] + feat [[2]] + feat[[3]] + feat[[4]] + feat[[5]] + feat[[6]]


dev.off()
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig3.k6topic1.UMAP.png", width=6, height=6, units= "in", res=1080)

p<- FeaturePlot(merged, features = paste0("k6.k",1), pt.size = 1.5)
p<- AugmentPlot(p)
p+ggtitle("6 Topics: Topic 1 cell loadings")
  
dev.off()
clust<- c("SCT_snn_res.0.1", "SCT_snn_res.0.5", "SCT_snn_res.0.8", "SCT_snn_res.1")
cres<- list()
for(i in 1:length(clust)){
  cres[[i]]<- DimPlot(merged, group.by = clust[i])
}
cres
[[1]]


[[2]]


[[3]]


[[4]]

lps<- NULL
for ( i in 1:length(clust)){
lps[[i]]<- loadings_plot(poisson2multinom(fit), as.factor(merged@meta.data[,clust[i]]))
}
lps
[[1]]


[[2]]


[[3]]


[[4]]

pdf(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SuppFig_LoadingsxClust.k6.pdf", width= 11, height = 4)

lps[[1]]
lps[[2]]
lps[[3]]
lps[[4]]
  
dev.off()
p<- loadings_plot(poisson2multinom(fit), as.factor(merged@meta.data[,clust[1]]), k= "k1")
p<- AugmentPlot(p)

p<- p+ ggtitle("6 Topics: Topic 1 loading on seurat clusters") + xlab("Seurat Cluster (Res.0.1)") + geom_boxplot(size=.5, outlier.shape=NA)

p

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig3.k6topic1.SeuratClusters0.1.loadingsplot.png", width=4, height=4, units= "in", res=1080)

p
  
dev.off()
p<- loadings_plot(poisson2multinom(fit), as.factor(merged@meta.data[,clust[4]]), k= "k1")
p<- AugmentPlot(p)

p<- p+ ggtitle("6 Topics: Topic 1 loading on seurat clusters") + xlab("Seurat Cluster (Res. 1)") + geom_boxplot(size=.5, outlier.shape=NA)

p

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig3.k6topic1.SeuratClusters1.loadingsplot.png", width=4, height=4, units= "in", res=1080)

p
  
dev.off()

volcano plots for genes DE in each topic

plot.list<- list()
for (i in 1:ncol(fit$L)){
p<-volcano_plot(diff_count_res, k=i, labels=genes, label_above_quantile = 0.99945)
plot.list[[i]]<-p
}

plot.list
[[1]]


[[2]]


[[3]]


[[4]]


[[5]]


[[6]]

options(ggrepel.max.overlaps = Inf)
volc.all<- plot.list[[1]]+ plot.list[[2]] + plot.list[[3]]+ plot.list[[4]] + plot.list[[5]] + plot.list[[6]]

volc.all
Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/SuppFig_k6volcanoplots.png", width=11, height=7, units= "in", res=1080)

volc.all
  
dev.off()
png(file= "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/figs/Fig3_k6.topic1volc.png", width=5, height=4, units= "in", res=1080)

plot.list[[1]]
  
dev.off()

output tables of top 10 topic driver genes by beta

top10.byBeta<- NULL
for (i in 1:ncol(diff_count_res$beta)){
  topic<- diff_count_res$beta[,i]
  topic<- topic[order(topic, decreasing=T)]
  top10<- names(topic)[1:10]
  top10.byBeta<- cbind(top10.byBeta,top10)
}

colnames(top10.byBeta)<- colnames(diff_count_res$beta)
write.csv(top10.byBeta, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TopicModelling_k6_top10drivergenes.byBeta.csv")
top15.byZ<- NULL
for (i in 1:ncol(diff_count_res$beta)){
  topic<- diff_count_res$Z[,i]
  topic<- topic[order(topic, decreasing=T)]
  top15<- names(topic)[1:15]
  top15.byZ<- cbind(top15.byZ,top15)
}

colnames(top15.byZ)<- colnames(diff_count_res$beta)
write.csv(top15.byZ, "/project2/gilad/katie/Pilot_HumanEBs/Embryoid_Body_Pilot_Workflowr/output/TopicModelling_k6_top15drivergenes.byZ.csv")

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] tibble_3.0.4       dplyr_1.0.2        cowplot_1.1.1      Seurat_3.2.0      
[5] ggplot2_3.3.3      Matrix_1.2-18      fastTopics_0.3-145 workflowr_1.6.2   

loaded via a namespace (and not attached):
  [1] Rtsne_0.15            colorspace_2.0-0      deldir_0.1-28        
  [4] ellipsis_0.3.1        ggridges_0.5.2        rprojroot_2.0.2      
  [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] MatrixModels_0.4-1    ggrepel_0.9.0         codetools_0.2-16     
 [16] splines_3.6.1         lsei_1.2-0            knitr_1.29           
 [19] polyclip_1.10-0       jsonlite_1.7.2        mcmc_0.9-7           
 [22] ica_1.0-2             cluster_2.1.0         png_0.1-7            
 [25] uwot_0.1.10           sctransform_0.2.1     shiny_1.5.0          
 [28] compiler_3.6.1        httr_1.4.2            fastmap_1.0.1        
 [31] lazyeval_0.2.2        later_1.1.0.1         htmltools_0.5.0      
 [34] quantreg_5.61         prettyunits_1.1.1     tools_3.6.1          
 [37] rsvd_1.0.3            igraph_1.2.6          coda_0.19-3          
 [40] gtable_0.3.0          glue_1.4.2            reshape2_1.4.4       
 [43] RANN_2.6.1            rappdirs_0.3.3        spatstat_1.64-1      
 [46] Rcpp_1.0.6            vctrs_0.3.6           gdata_2.18.0         
 [49] ape_5.4-1             nlme_3.1-140          conquer_1.0.1        
 [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          spatstat.utils_1.17-0
 [67] hms_0.5.3             promises_1.1.1        parallel_3.6.1       
 [70] SparseM_1.78          RColorBrewer_1.1-2    yaml_2.2.1           
 [73] gridExtra_2.3         reticulate_1.20       pbapply_1.4-2        
 [76] rpart_4.1-15          stringi_1.5.3         highr_0.8            
 [79] caTools_1.18.0        rlang_0.4.10          pkgconfig_2.0.3      
 [82] matrixStats_0.57.0    bitops_1.0-6          evaluate_0.14        
 [85] lattice_0.20-38       tensor_1.5            ROCR_1.0-7           
 [88] purrr_0.3.4           labeling_0.4.2        patchwork_1.1.1      
 [91] htmlwidgets_1.5.1     tidyselect_1.1.0      RcppAnnoy_0.0.18     
 [94] plyr_1.8.6            magrittr_2.0.1        R6_2.5.0             
 [97] gplots_3.0.4          generics_0.1.0        mgcv_1.8-28          
[100] pillar_1.4.7          withr_2.4.2           fitdistrplus_1.0-14  
[103] abind_1.4-5           survival_3.2-3        future.apply_1.6.0   
[106] crayon_1.3.4          KernSmooth_2.23-15    plotly_4.9.2.1       
[109] rmarkdown_2.3         progress_1.2.2        grid_3.6.1           
[112] data.table_1.13.4     git2r_0.26.1          digest_0.6.27        
[115] xtable_1.8-4          tidyr_1.1.0           httpuv_1.5.4         
[118] MCMCpack_1.4-8        RcppParallel_5.0.2    munsell_0.5.0        
[121] viridisLite_0.3.0     quadprog_1.5-8