Applying CSIDE to Spatial Transcriptomics Data

Data Preprocessing and running RCTD

First, we run RCTD on the data to annotated cell types. Please note that this follows exactly the content of the spatial transcriptomics RCTD vignette (doublet mode). Please refer to the spatial transcriptomics vignette for more explanation on the RCTD algorithm.

### Load in/preprocess your data, this might vary based on your file type
if(!file.exists(file.path(savedir,'myRCTD.rds'))) {
  counts <- read.csv(file.path(datadir,"MappedDGEForR.csv")) # load in counts matrix
  coords <- read.csv(file.path(datadir,"BeadLocationsForR.csv"))
  rownames(counts) <- counts[,1]; counts[,1] <- NULL # Move first column to rownames
  rownames(coords) <- coords$barcodes; coords$barcodes <- NULL # Move barcodes to rownames
  nUMI <- colSums(counts) # In this case, total counts per pixel is nUMI
  puck <- SpatialRNA(coords, counts, nUMI)
  barcodes <- colnames(puck@counts) # pixels to be used (a list of barcode names). 
  plot_puck_continuous(puck, barcodes, puck@nUMI, ylimit = c(0,round(quantile(puck@nUMI,0.9))), 
                       title ='plot of nUMI') 
  refdir <- system.file("extdata",'Reference/Vignette',package = 'spacexr') # directory for the reference
  counts <- read.csv(file.path(refdir,"dge.csv")) # load in counts matrix
  rownames(counts) <- counts[,1]; counts[,1] <- NULL # Move first column to rownames
  meta_data <- read.csv(file.path(refdir,"meta_data.csv")) # load in meta_data (barcodes, clusters, and nUMI)
  cell_types <- meta_data$cluster; names(cell_types) <- meta_data$barcode # create cell_types named list
  cell_types <- as.factor(cell_types) # convert to factor data type
  nUMI <- meta_data$nUMI; names(nUMI) <- meta_data$barcode # create nUMI named list
  reference <- Reference(counts, cell_types, nUMI)
  myRCTD <- create.RCTD(puck, reference, max_cores = 2)
  myRCTD <- run.RCTD(myRCTD, doublet_mode = 'doublet')
  saveRDS(myRCTD,file.path(savedir,'myRCTD.rds'))
}

Create explanatory variable / covariate

Now that we have successfully run RCTD, we can create a explanatory variable (i.e. covariate) used for predicting differential expression in CSIDE. In this case, the variable, explanatory.variable, will be randomly generated, but in general one should set the explanatory variable to biologically relevant predictors of gene expression such as spatial position. The explanatory variable itself is a vector of values, constrained between 0 and 1, with names matching the pixel names of the myRCTD object.

Here, we also artifically upregulate the expression of the Lsamp gene (in regions of high explanatory variable) to see whether CSIDE can detect this differentially expressed gene.

### Create SpatialRNA object
myRCTD <- readRDS(file.path(savedir,'myRCTD.rds'))
set.seed(12345)
explanatory.variable <- runif(length(myRCTD@spatialRNA@nUMI))
names(explanatory.variable) <- names(myRCTD@spatialRNA@nUMI) # currently random explanatory variable 
print(head(explanatory.variable))

#Differentially upregulate one gene
change_gene <- 'Lsamp'
high_barc <- names(explanatory.variable[explanatory.variable > 0.5])
low_barc <- names(explanatory.variable[explanatory.variable < 0.5])
myRCTD@originalSpatialRNA@counts[change_gene, high_barc] <- myRCTD@spatialRNA@counts[change_gene, high_barc] * 3 

plot_puck_continuous(myRCTD@spatialRNA, names(explanatory.variable), explanatory.variable, ylimit = c(0,1), title ='plot of explanatory variable')

Running CSIDE

After creating the explanatory variable, we are now ready to run CSIDE using the run.CSIDE.single function. We will use two cores, and a false discovery rate of 0.25. Next, we will set a gene threshold (i.e. minimum gene expression) of 0.01, and we will set a cell_type_threshold (minimum instances per cell type) of 10.

Warning: On this toy dataset, we have made several choices of parameters that are not recommended for regular use. On real datasets, we recommend first consulting the CSIDE default parameters. This includes gene_threshold (default 5e-5), cell_type_threshold (default 125), and fdr (default 0.01). Please see ?run.CSIDE.single for more information on these parameters.

#de
myRCTD@config$max_cores <- 2
myRCTD <- run.CSIDE.single(myRCTD, explanatory.variable, gene_threshold = .01, 
                        cell_type_threshold = 10, fdr = 0.25) 
#> Warning in run.CSIDE.general(myRCTD, X1, X2, barcodes, cell_types,
#> cell_type_threshold = cell_type_threshold, : run.CSIDE.general: some parameters
#> are set to the CSIDE vignette values, which are intended for testing but
#> not proper execution. For more accurate results, consider using the default
#> parameters to this function.
#> run.CSIDE.general: running CSIDE with cell types 10, 18
#> run.CSIDE.general: configure params_to_test = 2,
#> filter_genes: filtering genes based on threshold = 0.01

saveRDS(myRCTD,file.path(savedir,'myRCTDde.rds'))

CSIDE results

After running CSIDE, it is time to examine CSIDE results. For each cell type, a results dataframe for significant genes is stored in myRCTD@de_results$sig_gene_list. In particular, notice the columns Z_score (Z-score), log_fc (estimated DE loge-fold-change), and p_val (p-value). We also have the mean and standard errors of loge expression in each of the two regions (mean_0, mean_1, sd_0, and sd_1). We will focus on cell type 10. Furthermore, we will examine the original Lsamp gene, which was detected to be significantly differentially expressed in cell type 10. CSIDE model fits for all genes are stored in myRCTD@de_results$gene_fits, and we demonstrate below how to access the point estimates, standard errors, and convergence.

#print results for cell type '18'
cell_type <- '10'
results_de <- myRCTD@de_results$sig_gene_list[[cell_type]]
print(results_de)
#>        Z_score    log_fc        se paramindex_best conv       p_val    mean_0
#> Rora  2.741861 -2.853199 1.0406068               2 TRUE 0.006109228 -4.306653
#> Kcnd2 2.399268  1.280853 0.5338516               2 TRUE 0.016427889 -4.670806
#> Lsamp 1.760034  1.644275 0.9342294               2 TRUE 0.078402081 -5.228156
#> Calm2 1.676232  1.037308 0.6188334               2 TRUE 0.093692839 -5.485009
#>          mean_1      sd_0      sd_1
#> Rora  -7.159851 0.4153836 0.9541063
#> Kcnd2 -3.389953 0.3124028 0.4328995
#> Lsamp -3.583881 0.5354939 0.7655265
#> Calm2 -4.447701 0.3611977 0.5024849
sig_gene <- change_gene
print(paste("following results hold for", sig_gene))
#> [1] "following results hold for Lsamp"
print("check for covergence of each cell type")
#> [1] "check for covergence of each cell type"
print(myRCTD@de_results$gene_fits$con_mat[sig_gene, ]) 
#>   10   18 
#> TRUE TRUE
print('estimated DE')
#> [1] "estimated DE"
print(myRCTD@de_results$gene_fits$mean_val[sig_gene, ]) 
#>       10       18 
#> 1.644275 2.691020
print('standard errors for non-intercept terms')
#> [1] "standard errors for non-intercept terms"
print(myRCTD@de_results$gene_fits$s_mat[sig_gene, c(2,4)]) 
#>    2_2_10    2_2_18 
#> 0.9342294 2.7821257

Finally, we will plot CSIDE results in the savedir directory!

The following plot shows a spatial visualization of the Lsamp gene, which was determined to be differentially expressed.

The function make_all_de_plots will automatically generate several types of plots displaying the CSIDE results across all genes and cell types.

myRCTD <- readRDS(file.path(savedir,'myRCTDde.rds'))
plot_gene_two_regions(myRCTD, sig_gene, cell_type, min_UMI = 10)

make_all_de_plots(myRCTD, savedir)

Applying CSIDE to Spatial Transcriptomics Data

Dylan Cable

December 15th, 2021

Introduction

Data Preprocessing and running RCTD

Create explanatory variable / covariate

Running CSIDE

CSIDE results