Last updated: 2020-01-27

Checks: 6 0

Knit directory: 10x-adipocyte-analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20181026)

The command set.seed(20181026) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: fcdc606

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    code/.Rhistory
    Ignored:    figures/
    Ignored:    output/bulk_analysis/
    Ignored:    output/demuxlet/
    Ignored:    output/harmony/
    Ignored:    output/markergenes/
    Ignored:    output/monocle/
    Ignored:    output/seurat_objects/
    Ignored:    output/velocyto/
    Ignored:    output/wgcna/
    Ignored:    tables/

Untracked files:
    Untracked:  .rstudio_old10/
    Untracked:  10x-adipocyte-analysis-copy.Rproj
    Untracked:  analysis/.ipynb_checkpoints/10x-180831_harmony_palantir-checkpoint.ipynb
    Untracked:  analysis/.ipynb_checkpoints/velocyto_notebook_180831-checkpoint.ipynb
    Untracked:  code/BEAM-heatmaps.R
    Untracked:  code/BEAM_gsea.R
    Untracked:  code/__pycache__/
    Untracked:  code/colors.R
    Untracked:  code/convert_raw_data_to_csv_harmony.R
    Untracked:  code/harmony.py
    Untracked:  code/test.csv

Unstaged changes:
    Deleted:    10x-adipocyte-analysis.Rproj
    Modified:   analysis/10x-180831-figures.Rmd
    Deleted:    analysis/velocyto_notebook_180504.ipynb
    Deleted:    analysis/velocyto_notebook_180831.ipynb
    Deleted:    code/REMOVE/find-brown-sample-markers-180504-REMOVE.R
    Deleted:    code/REMOVE/find-white-sample-markers-180504-REMOVE.R
    Deleted:    code/REMOVE/get-genes-monocle-180831-REMOVE.R
    Modified:   code/compute-genelists-monocle-depots.R
    Modified:   code/find-depot-markers-180504.R
    Modified:   code/find-markers.R
    Modified:   code/preprocess-data.R
    Modified:   code/run-alignment.R
    Modified:   code/run-monocle.R
    Modified:   code/velocyto_preprocess.py

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 R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd fcdc606 Pytrik Folkertsma 2020-01-27 vijay analysis notebooks
html 20cfe73 Pytrik Folkertsma 2020-01-25 Build site.
Rmd adae7b7 Pytrik Folkertsma 2020-01-23 vijay analysis branch markers 
library(Seurat)
library(dplyr)
library(tibble)
library(cowplot)
library(DT)

Data loading and processing

# file.data <- "/nfsdata/projects/timshel/sc-scheele_lab_adipose_fluidigm_c1/data-vijay/GSE129363_SVF_Normalised_Data.txt.gz"
# df.data <- read_tsv(file.data) %>% rename(gene=X1)# WORKS and parsed correctly. Warning message: Missing column names filled in: 'X1' [1] 
# df.data <- data.table::fread(file.data, nThread=24, showProgress=T)
# df.data <- df.data %>% column_to_rownames(var="V1")
# 
# ### Meta-data
# file.metadata <- "/nfsdata/projects/timshel/sc-scheele_lab_adipose_fluidigm_c1/data-vijay/GSE129363_cell_metadata.txt.gz"
# df.metadata <- read_tsv(file.metadata)
# df.metadata <- df.metadata %>% rename(cell_barcode=X1, diabetes=Condition, depot=Tissue, celltype=ClsID)
# # *TODO*: rename IA-->visc; SC-->subq
# df.metadata <- df.metadata %>% column_to_rownames(var="cell_barcode")
# 
# ### Create object
# vijay <- CreateSeuratObject(counts=df.data, meta.data=df.metadata, project="vijay") # Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-')
# vijay[["RNA"]]@data <- vijay[["RNA"]]@counts # copy slot because data is already normalized
# # NB: do not perform further (log)normalization as this has already been done
# 
# vijay <- ScaleData(vijay)
# vijay <- FindVariableFeatures(vijay)
# vijay <- RunPCA(vijay)
# #ElbowPlot(vijay, reduction='pca')
# vijay <- RunTSNE(vijay, dims=1:11)
# vijay <- RunUMAP(vijay, dims=1:11)
# saveRDS(vijay, 'output/seurat_objects/vijay/vijay.rds')
vijay <- readRDS('output/seurat_objects/vijay/vijay.rds')

Metadata plots

#plot cell types + overlayed with meta data
plot_grid(
 UMAPPlot(vijay, group.by='celltype', label=T), 
 UMAPPlot(vijay, group.by='depot'),
 UMAPPlot(vijay, group.by='diabetes')
)

#plot cell types + overlayed with meta data
plot_grid(
 FeaturePlot(vijay, features='nFeature_RNA'), 
 FeaturePlot(vijay, features='nCount_RNA')
)

U/L branch marker genes

Plot the U and L branch marker genes.

data_180831 <- readRDS('output/seurat_objects/180831/10x-180831-S3')
#DE genes between T1T2T3 and T4T5 in the 10x-180831 data.
markers_u_l <- read.table('output/markergenes/180831/markers_10x-180831_upperbranch_lowerbranch_negbinom', sep='\t', header=T)
markers_u <- markers_u_l[order(-markers_u_l$avg_logFC),]
markers_l <- markers_u_l[order(markers_u_l$avg_logFC),]

How do these genes look in the 180831 data?

plots <- FeaturePlot(data_180831, features=c(as.vector(markers_u$gene)[1:10], as.vector(markers_l$gene)[1:10]), pt.size=1, combine=F)
plot_grid(plotlist=plots, ncol=4)

And how are they expressed in the Vijay data?

UMAPPlot(vijay, group.by='celltype', label=T)

plots <- FeaturePlot(vijay, features=c(as.vector(markers_u$gene)[1:10], as.vector(markers_l$gene)[1:10]), pt.size=1, combine=F)
Warning in FetchData(object = object, vars = c(dims, "ident", features), :
The following requested variables were not found: RP11-572C15.6
plot_grid(plotlist=plots, ncol=2)

Some U branch markers are not really expressed (SCD, PLIN4, GDP1, RBP4). Interestingly most U branch markers are also expressed in I1 and I3 and E1-E3.

Seurat integration with 10x-180831 data

#anchors <- FindIntegrationAnchors(object.list = list(vijay, data_180831), dims = 1:20)
#integrated <- IntegrateData(anchorset = anchors, dims = 1:20)
#integrated <- ScaleData(integrated, verbose = FALSE)
#integrated <- RunPCA(integrated, npcs = 30, verbose = FALSE)
#integrated <- RunUMAP(integrated, reduction = "pca", dims = 1:10)
#integrated <- RunTSNE(integrated, reduction = "pca", dims = 1:10)
#saveRDS(integrated, '/projects/pytrik/sc_adipose/analyze_10x_fluidigm/10x-adipocyte-analysis/output/seurat_objects/vijay/vijay.180831.integrated.rds')

integrated <- readRDS('output/seurat_objects/vijay/vijay.180831.integrated.rds')
integrated@meta.data['dataset'] <- '10x-180831'
integrated@meta.data[which(is.na(integrated@meta.data$branch)), 'dataset'] <- 'Vijay'

plot_grid(
  UMAPPlot(integrated, group.by='dataset'),
  UMAPPlot(integrated, group.by='celltype', label=T),
  UMAPPlot(integrated, group.by='branch'), ncol=2
)

Structural cells overlap with clusters P4, P5, P6, P2, P7. Metabolic cells overlap partly with P2 and P7 and also I3.

Predict cell types from 10x-180831 data

#find anchors
anchors <- FindTransferAnchors(reference = data_180831, query = vijay, dims = 1:20)
Performing PCA on the provided reference using 583 features as input.
Projecting PCA
Finding neighborhoods
Finding anchors
    Found 4975 anchors
Filtering anchors
    Retained 903 anchors
Extracting within-dataset neighbors
anchors_cca <- FindTransferAnchors(reference = data_180831, query = vijay, dims = 1:20, reduction = 'cca')
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
    Found 50710 anchors
Filtering anchors
    Retained 3050 anchors
Extracting within-dataset neighbors
#transfer labels
predictions_pca_project <- TransferData(anchors, data_180831$branch, dims = 1:20, weight.reduction='pcaproject')
Finding integration vectors
Finding integration vector weights
Predicting cell labels
predictions_pca <- TransferData(anchors, data_180831$branch, dims=1:20, weight.reduction='pca')
Running PCA on query dataset
Finding integration vectors
Finding integration vector weights
Predicting cell labels
predictions_cca <- TransferData(anchors_cca, data_180831$branch, dims=1:20, weight.reduction='cca')
Finding integration vectors
Finding integration vector weights
Predicting cell labels
#rename colnames
names(predictions_pca_project) <- unlist(lapply(names(predictions_pca_project), function(x){return(paste('pca_project.', x, sep=''))}))
names(predictions_pca) <- unlist(lapply(names(predictions_pca), function(x){return(paste('pca.', x, sep=''))}))
names(predictions_cca) <- unlist(lapply(names(predictions_cca), function(x){return(paste('cca.', x, sep=''))}))

vijay <- AddMetaData(vijay, metadata = predictions_pca_project)
vijay <- AddMetaData(vijay, metadata = predictions_pca)
vijay <- AddMetaData(vijay, metadata = predictions_cca)

Scores

plot_grid(
  UMAPPlot(vijay, group.by='celltype', label=T),
  FeaturePlot(vijay, features='pca.prediction.score.Progenitor'),
  FeaturePlot(vijay, features='pca_project.prediction.score.Progenitor'),
  FeaturePlot(vijay, features='cca.prediction.score.Progenitor'),
  FeaturePlot(vijay, features='pca.prediction.score.Metabolic'),
  FeaturePlot(vijay, features='pca_project.prediction.score.Metabolic'),
  FeaturePlot(vijay, features='cca.prediction.score.Metabolic'),
  FeaturePlot(vijay, features='pca.prediction.score.ECM'),
  FeaturePlot(vijay, features='pca_project.prediction.score.ECM'),
  FeaturePlot(vijay, features='cca.prediction.score.ECM'),
  UMAPPlot(vijay, group.by='pca.predicted.id'),
  UMAPPlot(vijay, group.by='pca_project.predicted.id'),
  UMAPPlot(vijay, group.by='cca.predicted.id'),
  ncol=2
)

Version Author Date
20cfe73 Pytrik Folkertsma 2020-01-25

Assign the labels if threshold is above 0.7 (same threshold as the wolfrum label transfer).

assign_labels <- function(colname, threshold=0.7){
  pred_ids <- unlist(as.vector(apply(vijay@meta.data[,c(paste(colname,'.prediction.score.max', sep=''), paste(colname, '.predicted.id', sep=''))], 1, function(x){
    if (x[[1]] < threshold){
      return(NA)
    } else{
      return(x[[2]])
    }
  })))
  return(pred_ids)
}

for (col in c('pca_project', 'pca', 'cca')){
  for (t in c(0.5, 0.7, 0.9, 0.95, 0.99)){
    preds <- assign_labels(col, t)
    vijay <- AddMetaData(vijay, preds, col.name=paste(col, 'predicted_label', t, sep='.'))
  }
}

Threshold for prediction = 0.5

plot_grid(
  UMAPPlot(vijay, group.by='pca.predicted_label.0.5'),
  UMAPPlot(vijay, group.by='pca_project.predicted_label.0.5'),
  UMAPPlot(vijay, group.by='cca.predicted_label.0.5'), ncol=2
)

Version Author Date
20cfe73 Pytrik Folkertsma 2020-01-25

Threshold for prediction = 0.7

plot_grid(
  UMAPPlot(vijay, group.by='pca.predicted_label.0.7'),
  UMAPPlot(vijay, group.by='pca_project.predicted_label.0.7'),
  UMAPPlot(vijay, group.by='cca.predicted_label.0.7'), ncol=2
)

Version Author Date
20cfe73 Pytrik Folkertsma 2020-01-25

Threshold for prediction = 0.9

plot_grid(
  UMAPPlot(vijay, group.by='pca.predicted_label.0.9'),
  UMAPPlot(vijay, group.by='pca_project.predicted_label.0.9'),
  UMAPPlot(vijay, group.by='cca.predicted_label.0.9'), ncol=2
)

Version Author Date
20cfe73 Pytrik Folkertsma 2020-01-25

I3 = adipose tissue macrophages (Vijay paper). Perhaps no metabolic preadipocytes because of maturation?

UMAPPlot(vijay, group.by='celltype', label=T)

Version Author Date
20cfe73 Pytrik Folkertsma 2020-01-25 


sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Storage

Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
[1] DT_0.9        cowplot_0.9.4 ggplot2_3.1.0 tibble_2.1.1  dplyr_0.8.0.1
[6] Seurat_3.1.1 

loaded via a namespace (and not attached):
 [1] tsne_0.1-3          nlme_3.1-140        bitops_1.0-6       
 [4] fs_1.2.7            RcppAnnoy_0.0.13    RColorBrewer_1.1-2 
 [7] httr_1.4.0          rprojroot_1.3-2     sctransform_0.2.0  
[10] tools_3.5.3         backports_1.1.3     R6_2.4.0           
[13] irlba_2.3.3         KernSmooth_2.23-15  uwot_0.1.4         
[16] lazyeval_0.2.2      colorspace_1.4-1    withr_2.1.2        
[19] npsurv_0.4-0        gridExtra_2.3       tidyselect_0.2.5   
[22] compiler_3.5.3      git2r_0.25.2        plotly_4.8.0       
[25] labeling_0.3        caTools_1.17.1.2    scales_1.0.0       
[28] lmtest_0.9-36       ggridges_0.5.1      pbapply_1.4-0      
[31] stringr_1.4.0       digest_0.6.18       rmarkdown_1.12     
[34] R.utils_2.8.0       pkgconfig_2.0.2     htmltools_0.3.6    
[37] bibtex_0.4.2        htmlwidgets_1.3     rlang_0.3.2        
[40] zoo_1.8-5           jsonlite_1.6        ica_1.0-2          
[43] gtools_3.8.1        R.oo_1.22.0         magrittr_1.5       
[46] Matrix_1.2-17       Rcpp_1.0.1          munsell_0.5.0      
[49] ape_5.3             reticulate_1.11.1   R.methodsS3_1.7.1  
[52] stringi_1.4.3       whisker_0.3-2       yaml_2.2.0         
[55] gbRd_0.4-11         MASS_7.3-51.4       gplots_3.0.1.1     
[58] Rtsne_0.15          plyr_1.8.4          grid_3.5.3         
[61] parallel_3.5.3      gdata_2.18.0        listenv_0.7.0      
[64] ggrepel_0.8.0       crayon_1.3.4        lattice_0.20-38    
[67] splines_3.5.3       SDMTools_1.1-221    knitr_1.22         
[70] pillar_1.3.1        igraph_1.2.4        reshape2_1.4.3     
[73] future.apply_1.3.0  codetools_0.2-16    leiden_0.3.1       
[76] glue_1.3.1          evaluate_0.13       lsei_1.2-0         
[79] metap_1.1           RcppParallel_4.4.4  data.table_1.12.0  
[82] png_0.1-7           Rdpack_0.10-1       gtable_0.3.0       
[85] RANN_2.6.1          purrr_0.3.2         tidyr_0.8.3        
[88] future_1.15.0       assertthat_0.2.1    xfun_0.5           
[91] rsvd_1.0.2          survival_2.43-3     viridisLite_0.3.0  
[94] workflowr_1.2.0     cluster_2.1.0       globals_0.12.4     
[97] fitdistrplus_1.0-14 ROCR_1.0-7