Comparison with base R

In this article we show some examples of the differences in coding between tidybulk/tidyverse and base R. We noted a decrease > 10x of assignments and a decrease of > 2x of line numbers.

Create `tidybulk` tibble.

tt = se_mini

Aggregate duplicated `transcripts`

Tidy transcriptomics

rowData(tt)$gene_name = rownames(tt)
tt.aggr = tt %>% aggregate_duplicates(.transcript = gene_name)

Base R

temp = data.frame(
	symbol = dge_list$genes$symbol,
	dge_list$counts
)
dge_list.nr <- by(temp,	temp$symbol,
	function(df)
		if(length(df[1,1])>0)
			matrixStats:::colSums(as.matrix(df[,-1]))
)
dge_list.nr <- do.call("rbind", dge_list.nr)
colnames(dge_list.nr) <- colnames(dge_list)

Scale `counts`

Tidy transcriptomics

tt.norm = tt.aggr %>% identify_abundant(factor_of_interest = condition) %>% scale_abundance()

Base R

library(edgeR)

dgList <- DGEList(count_m=x,group=group)
keep <- filterByExpr(dgList)
dgList <- dgList[keep,,keep.lib.sizes=FALSE]
[...]
dgList <- calcNormFactors(dgList, method="TMM")
norm_counts.table <- cpm(dgList)

Filter `variable transcripts`

We may want to identify and filter variable transcripts.

Tidy transcriptomics

tt.norm.variable = tt.norm %>% keep_variable()

Base R

library(edgeR)

x = norm_counts.table

s <- rowMeans((x-rowMeans(x))^2)
o <- order(s,decreasing=TRUE)
x <- x[o[1L:top],,drop=FALSE]

norm_counts.table = norm_counts.table[rownames(x)]

norm_counts.table$cell_type = tibble_counts[
	match(
		tibble_counts$sample,
		rownames(norm_counts.table)
	),
	"Cell type"
]

Reduce `dimensions`

Tidy transcriptomics

tt.norm.MDS =
  tt.norm %>%
  reduce_dimensions(method="MDS", .dims = 2)

Base R

library(limma)

count_m_log = log(count_m + 1)
cmds = limma::plotMDS(ndim = .dims, plot = FALSE)

cmds = cmds %$%	
	cmdscale.out %>%
	setNames(sprintf("Dim%s", 1:6))

cmds$cell_type = tibble_counts[
	match(tibble_counts$sample, rownames(cmds)),
	"Cell type"
]

PCA

Tidy transcriptomics

tt.norm.PCA =
  tt.norm %>%
  reduce_dimensions(method="PCA", .dims = 2)

Base R

count_m_log = log(count_m + 1)
pc = count_m_log %>% prcomp(scale = TRUE)
variance = pc$sdev^2
variance = (variance / sum(variance))[1:6]
pc$cell_type = counts[
	match(counts$sample, rownames(pc)),
	"Cell type"
]

tSNE

Tidy transcriptomics

tt.norm.tSNE =
	breast_tcga_mini_SE %>%
	tidybulk(		sample, ens, count_scaled) %>%
	identify_abundant() %>%
	reduce_dimensions(
		method = "tSNE",
		perplexity=10,
		pca_scale =TRUE
	)

Base R

count_m_log = log(count_m + 1)

tsne = Rtsne::Rtsne(
	t(count_m_log),
	perplexity=10,
		pca_scale =TRUE
)$Y
tsne$cell_type = tibble_counts[
	match(tibble_counts$sample, rownames(tsne)),
	"Cell type"
]

Rotate `dimensions`

Tidy transcriptomics

tt.norm.MDS.rotated =
  tt.norm.MDS %>%
	rotate_dimensions(`Dim1`, `Dim2`, rotation_degrees = 45, action="get")

Base R

rotation = function(m, d) {
	r = d * pi / 180
	((bind_rows(
		c(`1` = cos(r), `2` = -sin(r)),
		c(`1` = sin(r), `2` = cos(r))
	) %>% as_matrix) %*% m)
}
mds_r = pca %>% rotation(rotation_degrees)
mds_r$cell_type = counts[
	match(counts$sample, rownames(mds_r)),
	"Cell type"
]

Test `differential abundance`

Tidy transcriptomics

tt.de =
	tt %>%
	test_differential_abundance( ~ condition, action="get")
tt.de

Base R

library(edgeR)

dgList <- DGEList(counts=counts_m,group=group)
keep <- filterByExpr(dgList)
dgList <- dgList[keep,,keep.lib.sizes=FALSE]
dgList <- calcNormFactors(dgList)
design <- model.matrix(~group)
dgList <- estimateDisp(dgList,design)
fit <- glmQLFit(dgList,design)
qlf <- glmQLFTest(fit,coef=2)
topTags(qlf, n=Inf)

Adjust `counts`

Tidy transcriptomics

tt.norm.adj =
	tt.norm %>% adjust_abundance(	~ condition + time)

Base R

library(sva)

count_m_log = log(count_m + 1)

design =
		model.matrix(
			object = ~ condition + time,
			data = annotation
		)

count_m_log.sva =
	ComBat(
			batch =	design[,2],
			mod = design,
			...
		)

count_m_log.sva = ceiling(exp(count_m_log.sva) -1)
count_m_log.sva$cell_type = counts[
	match(counts$sample, rownames(count_m_log.sva)),
	"Cell type"
]

Deconvolve `Cell type composition`

Tidy transcriptomics

tt.cibersort =
	tt %>%
	deconvolve_cellularity(action="get", cores=1)

Base R

source(‘CIBERSORT.R’)
count_m %>% write.table("mixture_file.txt")
results <- CIBERSORT(
	"sig_matrix_file.txt",
	"mixture_file.txt",
	perm=100, QN=TRUE
)
results$cell_type = tibble_counts[
	match(tibble_counts$sample, rownames(results)),
	"Cell type"
]

Cluster `samples`

k-means

Tidy transcriptomics

tt.norm.cluster = tt.norm.MDS %>%
  cluster_elements(method="kmeans",	centers = 2, action="get" )

Base R

count_m_log = log(count_m + 1)

k = kmeans(count_m_log, iter.max = 1000, ...)
cluster = k$cluster

cluster$cell_type = tibble_counts[
	match(tibble_counts$sample, rownames(cluster)),
	c("Cell type", "Dim1", "Dim2")
]

SNN

Matrix package (v1.3-3) causes an error with Seurat::FindNeighbors used in this method. We are trying to solve this issue. At the moment this option in unaviable.

Tidy transcriptomics

tt.norm.SNN =
	tt.norm.tSNE %>%
	cluster_elements(method = "SNN")

Base R

library(Seurat)

snn = CreateSeuratObject(count_m)
snn = ScaleData(
	snn, display.progress = TRUE,
	num.cores=4, do.par = TRUE
)
snn = FindVariableFeatures(snn, selection.method = "vst")
snn = FindVariableFeatures(snn, selection.method = "vst")
snn = RunPCA(snn, npcs = 30)
snn = FindNeighbors(snn)
snn = FindClusters(snn, method = "igraph", ...)
snn = snn[["seurat_clusters"]]

snn$cell_type = tibble_counts[
	match(tibble_counts$sample, rownames(snn)),
	c("Cell type", "Dim1", "Dim2")
]

Drop `redundant` transcripts

Tidy transcriptomics

tt.norm.non_redundant =
	tt.norm.MDS %>%
  remove_redundancy(	method = "correlation" )

Base R

library(widyr)

.data.correlated =
	pairwise_cor(
		counts,
		sample,
		transcript,
		rc,
		sort = TRUE,
		diag = FALSE,
		upper = FALSE
	) %>%
	filter(correlation > correlation_threshold) %>%
	distinct(item1) %>%
	rename(!!.element := item1)

# Return non redundant data frame
counts %>% anti_join(.data.correlated) %>%
	spread(sample, rc, - transcript) %>%
	left_join(annotation)

Draw `heatmap`

tidytranscriptomics

tt.norm.MDS %>%

  # filter lowly abundant
  keep_abundant() %>%

  # extract 500 most variable genes
  keep_variable( .abundance = count_scaled, top = 500) %>%

  # create heatmap
  heatmap(sample, transcript, count_scaled, transform = log1p) %>%
	add_tile(`Cell type`)

Base R

# Example taken from airway dataset from BioC2020 workshop. 
dgList <- SE2DGEList(airway)
group <- factor(dgList$samples$`Cell type`)
keep.exprs <- filterByExpr(dgList, group=group)
dgList <- dgList[keep.exprs,, keep.lib.sizes=FALSE]
dgList <- calcNormFactors(dgList)
logcounts <- cpm(dgList, log=TRUE)
var_genes <- apply(logcounts, 1, var)
select_var <- names(sort(var_genes, decreasing=TRUE))[1:500]
highly_variable_lcpm <- logcounts[select_var,]
colours <- c("#440154FF", "#21908CFF", "#fefada" )
col.group <- c("red","grey")[group]
gplots::heatmap.2(highly_variable_lcpm, col=colours, trace="none", ColSideColors=col.group, scale="row")

Draw `density plot`

tidytranscriptomics

# Example taken from airway dataset from BioC2020 workshop. 
airway %>%
    tidybulk() %>%
	  identify_abundant() %>%
    scale_abundance() %>%
    pivot_longer(cols = starts_with("counts"), names_to = "source", values_to = "abundance") %>%
    filter(!lowly_abundant) %>%
    ggplot(aes(x=abundance + 1, color=sample)) +
    geom_density() +
    facet_wrap(~source) +
    scale_x_log10()

Base R

# Example taken from airway dataset from BioC2020 workshop. 
dgList <- SE2DGEList(airway)
group <- factor(dgList$samples$dex)
keep.exprs <- filterByExpr(dgList, group=group)
dgList <- dgList[keep.exprs,, keep.lib.sizes=FALSE]
dgList <- calcNormFactors(dgList)
logcounts <- cpm(dgList, log=TRUE)
var_genes <- apply(logcounts, 1, var)
select_var <- names(sort(var_genes, decreasing=TRUE))[1:500]
highly_variable_lcpm <- logcounts[select_var,]
colours <- c("#440154FF", "#21908CFF", "#fefada" )
col.group <- c("red","grey")[group]
gplots::heatmap.2(highly_variable_lcpm, col=colours, trace="none", ColSideColors=col.group, scale="row")

Appendix

sessionInfo()

## R version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [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       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] tidySummarizedExperiment_1.10.0 SummarizedExperiment_1.30.0    
##  [3] Biobase_2.60.0                  GenomicRanges_1.52.0           
##  [5] GenomeInfoDb_1.36.0             IRanges_2.34.0                 
##  [7] S4Vectors_0.38.0                BiocGenerics_0.46.0            
##  [9] MatrixGenerics_1.12.0           matrixStats_0.63.0             
## [11] tidybulk_1.12.0                 ggrepel_0.9.3                  
## [13] ggplot2_3.4.2                   magrittr_2.0.3                 
## [15] tibble_3.2.1                    tidyr_1.3.0                    
## [17] dplyr_1.1.2                     knitr_1.42                     
## 
## loaded via a namespace (and not attached):
##  [1] DBI_1.1.3               bitops_1.0-7            widyr_0.1.5            
##  [4] rlang_1.1.0             tidytext_0.4.1          compiler_4.3.0         
##  [7] RSQLite_2.3.1           mgcv_1.8-42             reshape2_1.4.4         
## [10] png_0.1-8               vctrs_0.6.2             sva_3.48.0             
## [13] stringr_1.5.0           pkgconfig_2.0.3         crayon_1.5.2           
## [16] fastmap_1.1.1           backports_1.4.1         XVector_0.40.0         
## [19] ellipsis_0.3.2          utf8_1.2.3              tzdb_0.3.0             
## [22] preprocessCore_1.62.0   purrr_1.0.1             bit_4.0.5              
## [25] xfun_0.39               zlibbioc_1.46.0         cachem_1.0.7           
## [28] jsonlite_1.8.4          blob_1.2.4              SnowballC_0.7.0        
## [31] DelayedArray_0.26.0     BiocParallel_1.34.0     broom_1.0.4            
## [34] parallel_4.3.0          R6_2.5.1                stringi_1.7.12         
## [37] limma_3.56.0            genefilter_1.82.0       Rcpp_1.0.10            
## [40] readr_2.1.4             Matrix_1.5-4            splines_4.3.0          
## [43] tidyselect_1.2.0        codetools_0.2-19        plyr_1.8.8             
## [46] lattice_0.21-8          withr_2.5.0             KEGGREST_1.40.0        
## [49] evaluate_0.20           Rtsne_0.16              survival_3.5-5         
## [52] Biostrings_2.68.0       pillar_1.9.0            janeaustenr_1.0.0      
## [55] plotly_4.10.1           generics_0.1.3          RCurl_1.98-1.12        
## [58] hms_1.1.3               munsell_0.5.0           scales_1.2.1           
## [61] xtable_1.8-4            glue_1.6.2              lazyeval_0.2.2         
## [64] tools_4.3.0             tokenizers_0.3.0        data.table_1.14.8      
## [67] annotate_1.78.0         locfit_1.5-9.7          XML_3.99-0.14          
## [70] grid_4.3.0              AnnotationDbi_1.62.0    edgeR_3.42.0           
## [73] colorspace_2.1-0        nlme_3.1-162            GenomeInfoDbData_1.2.10
## [76] cli_3.6.1               fansi_1.0.4             viridisLite_0.4.1      
## [79] gtable_0.3.3            digest_0.6.31           htmlwidgets_1.6.2      
## [82] memoise_2.0.1           htmltools_0.5.5         lifecycle_1.0.3        
## [85] httr_1.4.5              bit64_4.0.5

Comparison with base R

Stefano Mangiola

2023-04-25

Create `tidybulk` tibble.

Aggregate duplicated `transcripts`

Scale `counts`

Filter `variable transcripts`

Reduce `dimensions`

PCA

tSNE

Rotate `dimensions`

Test `differential abundance`

Adjust `counts`

Deconvolve `Cell type composition`

Cluster `samples`

k-means

SNN

Drop `redundant` transcripts

Draw `heatmap`

Draw `density plot`

Appendix

Comparison with base R

Stefano Mangiola

2023-04-25

Create tidybulk tibble.

Aggregate duplicated transcripts

Scale counts

Filter variable transcripts

Reduce dimensions

PCA

tSNE

Rotate dimensions

Test differential abundance

Adjust counts

Deconvolve Cell type composition

Cluster samples

k-means

SNN

Drop redundant transcripts

Draw heatmap

Draw density plot

Appendix

Create `tidybulk` tibble.

Aggregate duplicated `transcripts`

Scale `counts`

Filter `variable transcripts`

Reduce `dimensions`

Rotate `dimensions`

Test `differential abundance`

Adjust `counts`

Deconvolve `Cell type composition`

Cluster `samples`

Drop `redundant` transcripts

Draw `heatmap`

Draw `density plot`