Last updated: 2025-08-13

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File Version Author Date Message
Rmd b99041c Peter Carbonetto 2025-08-13 Added info about the yeast genes.
Rmd f83850c Peter Carbonetto 2025-08-13 Added some descriptions to the budding_yeast analysis.
Rmd 7a67df4 Peter Carbonetto 2025-08-13 Added UMAP to budding_yeast analysis.
Rmd c36cb94 Peter Carbonetto 2025-08-13 Added steps to budding_yeast analysis to filter out cells and genes.
Rmd 83ce75d Peter Carbonetto 2025-08-12 Started initial steps for processing of budding yeast data (GSE125162).

The paper is Jackson et al eLife 2020 (doi:10.7554/eLife.51254). First, I downloaded the file GSE125162_ALL-fastqTomat0-Counts.tsv.gz from GEO, accession GSE125162. (To read the data correctly using read_tsv, I first added “id” to the beginning of the header.)

First, load the packages needed for this analysis:

library(Matrix)
library(tools)
library(readr)
library(rsvd)
library(uwot)
library(ggplot2)
library(cowplot)

Load the count data:

counts <- read_tsv("../data/GSE125162_ALL-fastqTomat0-Counts.tsv.gz",
                   col_names = TRUE)
counts <- as.data.frame(counts)
ids    <- counts$id
counts <- counts[-1]
sample_info <- cbind(data.frame(id = ids),counts[6830:6834])
counts <- counts[1:6829]
counts <- as.matrix(counts)
counts <- as(counts,"CsparseMatrix")
rownames(counts) <- ids
sample_info <- transform(sample_info,
                         Genotype       = factor(Genotype),
                         Genotype_Group = factor(Genotype_Group),
                         Replicate      = factor(Replicate),
                         Condition      = factor(Condition))

Load the gene data:

genes <- read_tsv("../data/Saccharomyces_cerevisiae.gene_info.gz",
                  col_names = TRUE,comment = "#")
genes <- as.data.frame(genes)
genes <- genes[c("tax_id","GeneID","Symbol","LocusTag","Synonyms",
                 "dbXrefs","chromosome","type_of_gene")]
genes <- transform(genes,
                   chromosome   = factor(chromosome),
                   type_of_gene = factor(type_of_gene))

This is the number of cells and genes before any filtering:

nrow(counts)
ncol(counts)
# [1] 38225
# [1] 6829

Many cells have much smaller sequencing depth so, out of caution, I filter out the cells with the smaller sequencing depths:

par(mar = c(4,4,1,1))
x <- rowSums(counts)
i <- which(x >= 1000)
sample_info <- sample_info[i,]
counts <- counts[i,]
hist(log10(x),n = 64,xlab = "",ylab = "",main = "")

Further, a handful of genes are expressed in only a very small number of cells. I filter out genes that are expressed in fewer than 10 cells:

par(mar = c(4,4,1,1))
x <- colSums(counts > 0)
j <- which(x >= 10)
counts <- counts[,j]
hist(log10(x),n = 64,xlab = "",ylab = "",main = "")

This gives the number of cells, the number of genes, and the proportion of counts that are nonzero after these filtering steps:

nrow(counts)
ncol(counts)
mean(counts > 0)
# [1] 33834
# [1] 6128
# [1] 0.1394832

And here is the number of cells in each of the 11 growth conditions:

table(sample_info$Condition)
# 
# AmmoniumSulfate         CStarve       Glutamine     MinimalEtOH  MinimalGlucose 
#             738             492            1644             449            1894 
#         Proline            Urea             YPD      YPDDiauxic         YPDRapa 
#             401             615           11037            3332           11805 
#          YPEtOH 
#            1427

To get an initial sense for the structure underlying the data, I generate a 2-d nonlinear embedding of the cells using UMAP. First, I transform the counts into “shifted log counts”:

a <- 1
s <- rowSums(counts)
s <- s/mean(s)
shifted_log_counts <- MatrixExtra::mapSparse(counts/(a*s),log1p)

Next, I project the cells onto the top 50 PCs:

set.seed(1)
U <- rsvd(shifted_log_counts,k = 50)$u

Then I run UMAP on the 50 PCs:

Y <- umap(U,n_neighbors = 20,metric = "cosine",min_dist = 0.3,
          n_threads = 8,verbose = FALSE)
sample_info$umap1 <- Y[,1]
sample_info$umap2 <- Y[,2]

UMAP with the cells colored by growth condition:

umap_colors <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99","#e31a1c",
                 "#fdbf6f","#ff7f00","#cab2d6","#6a3d9a","#ffff99")
p <- ggplot(sample_info,aes(x = umap1,y = umap2,color = Condition)) +
  geom_point(size = 1.5) +
  scale_color_manual(values = umap_colors) +
  labs(x = "UMAP 1",y = "UMAP 2") + 
  theme_cowplot(font_size = 10)
print(p)

From this result, it is quite clear that the predominant structure corresponds to the different growth conditions. (This UMAP plot closely replicates Fig. 2B of the eLife paper.)

Finally, save the data and UMAP results to an .Rdata file for more convenient analysis in R:

save(list = c("sample_info","genes","counts"),file = "yeast.RData")
resaveRdaFiles("yeast.RData")

sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS 15.5
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
# LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# time zone: America/Chicago
# tzcode source: internal
# 
# attached base packages:
# [1] tools     stats     graphics  grDevices utils     datasets  methods  
# [8] base     
# 
# other attached packages:
# [1] cowplot_1.1.3 ggplot2_3.5.2 uwot_0.2.3    rsvd_1.0.5    readr_2.1.5  
# [6] Matrix_1.6-5 
# 
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#  [7] magrittr_2.0.3      evaluate_1.0.4      grid_4.3.3         
# [10] RColorBrewer_1.1-3  float_0.3-2         fastmap_1.2.0      
# [13] rprojroot_2.0.4     workflowr_1.7.1     jsonlite_2.0.0     
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# [31] tzdb_0.4.0          dplyr_1.1.4         httpuv_1.6.14      
# [34] vctrs_0.6.5         R6_2.6.1            lifecycle_1.0.4    
# [37] git2r_0.33.0        stringr_1.5.1       fs_1.6.6           
# [40] bit_4.0.5           vroom_1.6.5         irlba_2.3.5.1      
# [43] MatrixExtra_0.1.15  pkgconfig_2.0.3     pillar_1.11.0      
# [46] bslib_0.9.0         later_1.4.2         gtable_0.3.6       
# [49] glue_1.8.0          Rcpp_1.1.0          xfun_0.52          
# [52] tibble_3.3.0        tidyselect_1.2.1    knitr_1.50         
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