Last updated: 2015-10-19

Code version: 6b16d0b7ff64ea109c14ebf7c3bfbb5cebd4dec8

We analyzed our single cell data with BASiCS developed by Vallejos et al., 2015. The results shown here are from a model fit with 40,000 iterations.

BASiCS and its dependency, RcppArmadillo, were able to be installed on the cluster using a new version of gcc. Since this took a long time to run, it was submitted via the following:

echo "Rscript -e 'library(rmarkdown); render(\"basics.Rmd\")'" | \
  qsub -l h_vmem=32g -cwd -V -j y -o ~/log/ -N basics
theme_set(theme_bw(base_size = 16))
Loading required package: limma


Below is the description of the data from the BASiCS vignette, interspersed with my code to load the data.

The input dataset for BASiCS must contain the following 3 elements:

  • Counts: a matrix of raw expression counts with dimensions q times n. First q0 rows must correspond to biological genes. Last q − q0 rows must correspond to technical spike-in genes.

Input annotation.

anno_filter <- read.table("../data/annotation-filter.txt", header = TRUE,
                          stringsAsFactors = FALSE)

Input molecule counts.

molecules_filter <- read.table("../data/molecules-filter.txt", header = TRUE,
                               stringsAsFactors = FALSE)
stopifnot(nrow(anno_filter) == ncol(molecules_filter),
          colnames(molecules_filter) == anno_filter$sample_id,
          anno_filter$well != "bulk")

Remove outlier batch NA19098.r2.

molecules_filter <- molecules_filter[, anno_filter$batch != "NA19098.r2"]
anno_filter <- anno_filter[anno_filter$batch != "NA19098.r2", ]
stopifnot(nrow(anno_filter) == ncol(molecules_filter),
          colnames(molecules_filter) == anno_filter$sample_id,
          anno_filter$well != "bulk")
  • Tech: a vector of TRUE/FALSE elements with length q. If Tech[i] = FALSE the gene i is biological; otherwise the gene is spike-in.
tech <- grepl("ERCC", rownames(molecules_filter))
  • SpikeInput: a vector of length q − q0 whose elements contain the input number of molecules for the spike-in genes (amount per cell).
spike <- read.table("../data/expected-ercc-molecules.txt", header = TRUE,
                    sep = "\t", stringsAsFactors = FALSE)

Only keep the spike-ins that were kept after applying the expression cutoff.

spike_input <- spike$ercc_molecules_well[spike$id %in% rownames(molecules_filter)]
names(spike_input) <- spike$id[spike$id %in% rownames(molecules_filter)]
spike_input <- spike_input[order(names(spike_input))]
stopifnot(sum(tech) == length(spike_input),
          rownames(molecules_filter)[tech] == names(spike_input))

30 of the ERCC spike-ins were observed in the single cell data.

These elements must be stored into an object of class BASiCS_Data.

basics_data <- newBASiCS_Data(as.matrix(molecules_filter), tech, spike_input)
An object of class BASiCS_Data
 Dataset contains 10513 genes (10483 biological and 30 technical) and 563 cells.
 Elements (slots): Counts, Tech, SpikeInput and BatchInfo.
 The data contains 1 batch.

NOTICE: BASiCS requires a pre-filtered dataset 
    - You must remove poor quality cells before creating the BASiCS data object 
    - We recommend to pre-filter very lowly expressed transcripts before creating the object. 
      Inclusion criteria may vary for each data. For example, remove transcripts 
          - with very low total counts across of all samples 
          - that are only expressed in few cells 
            (by default genes expressed in only 1 cell are not accepted) 
          - with very low total counts across the samples where the transcript is expressed 

 BASiCS_Filter can be used for this purpose 

Fit the model

store_dir <- "../data"
run_name <- "new"
if (file.exists(paste0(store_dir, "/chain_phi_", run_name, ".txt"))) {
  chain_mu = as.matrix(fread(paste0(store_dir, "/chain_mu_", run_name, ".txt")))
  chain_delta = as.matrix(fread(paste0(store_dir, "/chain_delta_", run_name, ".txt")))
  chain_phi = as.matrix(fread(paste0(store_dir, "/chain_phi_", run_name, ".txt")))
  chain_s = as.matrix(fread(paste0(store_dir, "/chain_s_", run_name, ".txt")))
  chain_nu = as.matrix(fread(paste0(store_dir, "/chain_nu_", run_name, ".txt")))
  chain_theta = as.matrix(fread(paste0(store_dir, "/chain_theta_", run_name, ".txt")))

  mcmc_output <- newBASiCS_Chain(mu = chain_mu, delta = chain_delta,
                                 phi = chain_phi, s = chain_s,
                                 nu = chain_nu, theta = chain_theta)

  time_total <- readRDS(paste0(store_dir, "/time_total_", run_name, ".rds"))
} else {
  time_start <- Sys.time()
  mcmc_output <- BASiCS_MCMC(basics_data, N = 40000, Thin = 10, Burn = 20000,
                             PrintProgress = TRUE, StoreChains = TRUE,
                             StoreDir = store_dir, RunName = run_name)
  time_end <- Sys.time()
  time_total <- difftime(time_end, time_start, units = "hours")
  saveRDS(time_total, paste0(store_dir, "/time_total_", run_name, ".rds"))

Read 0.0% of 2000 rows
Read 2000 rows and 10513 (of 10513) columns from 0.331 GB file in 00:00:08

Read 0.0% of 2000 rows
Read 2000 rows and 10483 (of 10483) columns from 0.348 GB file in 00:00:09
An object of class BASiCS_Chain
 2000 MCMC samples.
 Dataset contains 10513 genes (10483 biological and 30 technical) and 563 cells (1 batch). 
 Elements (slots): mu, delta, phi, s, nu and theta.

Fitting the model took 81.06 hours.

Summarize the results.

mcmc_summary <- Summary(mcmc_output)

Cell-specific normalizing constants

BASiCS models two cell-specific parameters. Phi models the differences in gene molecules. S models the differences in ERCC molecules.

plot(mcmc_summary, Param = "phi")

plot(mcmc_summary, Param = "s")

phi <- displaySummaryBASiCS(mcmc_summary, Param = "phi")
s <- displaySummaryBASiCS(mcmc_summary, Param = "s")
parameters <- cbind(phi, s, anno_filter)
parameters$gene_count <- colSums(counts(basics_data, type = "biological"))
parameters$ercc_count <- colSums(counts(basics_data, type = "technical"))

Phi versus gene molecule count

phi_gene_count <- ggplot(parameters, aes(x = gene_count, y = Phi)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(x = "Gene molecule count",
       title = paste0("Phi measures gene molecule count differences\nr = ",
                      round(cor(parameters$gene_count, parameters$Phi), 2)))

Phi versus ERCC molecule count

phi_ercc_count <- phi_gene_count %+% aes(x = ercc_count) +
  labs(x = "ERCC molecule count",
       title = paste0("Phi does not measure ERCC molecule count differences\nr = ",
                      round(cor(parameters$ercc_count, parameters$Phi), 2)))

S versus ERCC molecule count

s_ercc_count <- phi_ercc_count %+% aes(y = S) +
  labs(y = "S",
       title = paste0("S measures ERCC molecule count differences\nr = ",
                      round(cor(parameters$ercc_count, parameters$S), 2)))

S versus gene molecule count

s_gene_count <- phi_gene_count %+% aes(y = S) +
  labs(y = "S",
       title = paste0("S does not measure gene molecule count differences\nr = ",
                      round(cor(parameters$gene_count, parameters$S), 2)))

Phi versus S

phi_s <- ggplot(parameters, aes(x = S, y = Phi)) +
  geom_point() +
  geom_smooth(method = "lm") +
  labs(       title = paste0("Phi versus S\nr = ",
                      round(cor(parameters$S, parameters$Phi), 2)))

Denoised data

Remove technical noise (i.e. normalize using the ERCC spike-ins).

denoised = BASiCS_DenoisedCounts(Data = basics_data, Chain = mcmc_output)

PCA - BASiCS Denoised

Both the raw and the cpm versions of the BASiCS denoised data appear similar to the result with the non-normalized cpm data. This does not change substantially when increasing the iterations from a few thousands to a few tens of thousands.

pca_basics <- run_pca(denoised)
plot_pca(pca_basics$PCs, explained = pca_basics$explained,
         metadata = anno_filter, color = "individual",
         shape = "replicate")

PCA - BASiCS Denoised cpm

denoised_cpm <- cpm(denoised, log = TRUE,
                    lib.size = colSums(denoised) *
                               calcNormFactors(denoised, method = "TMM"))
pca_basics_cpm <- run_pca(denoised_cpm)
plot_pca(pca_basics_cpm$PCs, explained = pca_basics_cpm$explained,
         metadata = anno_filter, color = "individual",
         shape = "replicate")

PCA - non-normalized

pca_non <- run_pca(counts(basics_data))
plot_pca(pca_non$PCs, explained = pca_non$explained,
         metadata = anno_filter, color = "individual",
         shape = "replicate")

PCA - non-normalized cpm

non_cpm <- cpm(counts(basics_data), log = TRUE,
               lib.size = colSums(counts(basics_data)) *
                          calcNormFactors(counts(basics_data), method = "TMM"))
pca_non_cpm <- run_pca(non_cpm)
plot_pca(pca_non_cpm$PCs, explained = pca_non_cpm$explained,
         metadata = anno_filter, color = "individual",
         shape = "replicate")

Session information

R version 3.2.0 (2015-04-16)
Platform: x86_64-unknown-linux-gnu (64-bit)

 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            

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

other attached packages:
[1] testit_0.4       edgeR_3.10.2     limma_3.24.9     tidyr_0.2.0     
[5] ggplot2_1.0.1    data.table_1.9.4 BASiCS_0.3.0     knitr_1.10.5    

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.0         magrittr_1.5        MASS_7.3-40        
 [4] BiocGenerics_0.14.0 munsell_0.4.2       colorspace_1.2-6   
 [7] lattice_0.20-31     stringr_1.0.0       httr_0.6.1         
[10] plyr_1.8.3          tools_3.2.0         parallel_3.2.0     
[13] grid_3.2.0          gtable_0.1.2        coda_0.17-1        
[16] htmltools_0.2.6     yaml_2.1.13         digest_0.6.8       
[19] reshape2_1.4.1      formatR_1.2         bitops_1.0-6       
[22] RCurl_1.95-4.6      evaluate_0.7        rmarkdown_0.6.1    
[25] labeling_0.3        stringi_0.4-1       scales_0.2.4       
[28] chron_2.3-45        proto_0.3-10