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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 = counts_mini %>% tidybulk(sample, transcript, count)

## Aggregate duplicated `transcripts`

Tidy transcriptomics “{.r .yellow} tt.aggr = tt %>% aggregate_duplicates() ”
Base R “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 ”r tt.norm = tt.aggr %>% identify_abundant(factor_of_interest = condition) %>% scale_abundance() “
Base R ”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 “r tt.norm.variable = tt.norm %>% keep_variable() ”
Base R “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$cell_type = tidybulk::counts[ match( tidybulk::counts$sample, rownames(norm_counts.table) ), "Cell type" ] ```

## Reduce `dimensions`

Tidy transcriptomics ”r tt.norm.MDS = tt.norm %>% reduce_dimensions(method=“MDS”, .dims = 2) “
Base R ”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 = tidybulk::counts[ match(tidybulk::counts$sample, rownames(cmds)), “Cell type” ] “

### PCA

Tidy transcriptomics ”r tt.norm.PCA = tt.norm %>% reduce_dimensions(method=“PCA”, .dims = 2) “
Base R ”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 ”r tt.norm.tSNE = breast_tcga_mini %>% tidybulk( sample, ens, count_scaled) %>% identify_abundant() %>% reduce_dimensions( method = “tSNE”, perplexity=10, pca_scale =TRUE ) “
Base R ”r count_m_log = log(count_m + 1) tsne = Rtsne::Rtsne( t(count_m_log), perplexity=10, pca_scale =TRUE )$Y tsne$cell_type = tidybulk::counts[ match(tidybulk::counts$sample, rownames(tsne)), “Cell type” ] “

## Rotate `dimensions`

Tidy transcriptomics ”r tt.norm.MDS.rotated = tt.norm.MDS %>% rotate_dimensions(Dim1, Dim2, rotation_degrees = 45, action=“get”) “
Base R ”r rotation = function(m, d) { r = d * pi / 180 ((bind_rows( c(1 = cos®, 2 = -sin®), c(1 = sin®, 2 = cos®) ) %>% 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 ”r tt.de = tt %>% test_differential_abundance( ~ condition, action=“get”) tt.de “
Base R ”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 “r tt.norm.adj = tt.norm %>% adjust_abundance( ~ condition + time) ”
Base R “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 ”r tt.cibersort = tt %>% deconvolve_cellularity(action=“get”, cores=1) “
Base R ”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$cell_type = tidybulk::counts[ match(tidybulk::counts$sample, rownames(results)), "Cell type" ] ```

## Cluster `samples`

### k-means

Tidy transcriptomics “r tt.norm.cluster = tt.norm.MDS %>% cluster_elements(method="kmeans”, centers = 2, action=“get” ) “
Base R ”r count_m_log = log(count_m + 1) k = kmeans(count_m_log, iter.max = 1000, …) cluster = k$cluster cluster$cell_type = tidybulk::counts[ match(tidybulk::counts$sample, rownames(cluster)), c(“Cell type”, “Dim1”, “Dim2”) ] “

### SNN

Tidy transcriptomics ”r tt.norm.SNN = tt.norm.tSNE %>% cluster_elements(method = “SNN”) “
Base R ”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$cell_type = tidybulk::counts[ match(tidybulk::counts$sample, rownames(snn)), c(“Cell type”, “Dim1”, “Dim2”) ] “

## Drop `redundant` transcripts

Tidy transcriptomics ”r tt.norm.non_redundant = tt.norm.MDS %>% remove_redundancy( method = “correlation” ) “
Base R ”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 ”r tt.norm.MDS %>% # filter lowly abundant keep_abundant() %>% # extract 500 most variable genes keep_variable( .abundance = count_scaled, top = 500) %>% # create heatmap as_tibble() %>% heatmap(sample, transcript, count_scaled, transform = log1p) %>% add_tile(Cell type) “
Base R ”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 “r # 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 ”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 4.0.4 (2021-02-15)
    ## Platform: x86_64-apple-darwin17.0 (64-bit)
    ## Running under: macOS Mojave 10.14.6
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
    ##
    ## locale:
    ## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] tidybulk_1.2.1 ggrepel_0.9.1  ggplot2_3.3.3  magrittr_2.0.1 tibble_3.1.0
    ## [6] tidyr_1.1.3    dplyr_1.0.5    knitr_1.31
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] backports_1.2.1             tidytext_0.3.0
    ##   [3] plyr_1.8.6                  igraph_1.2.6
    ##   [5] lazyeval_0.2.2              splines_4.0.4
    ##   [7] BiocParallel_1.24.1         listenv_0.8.0
    ##   [9] SnowballC_0.7.0             scattermore_0.7
    ##  [11] GenomeInfoDb_1.26.6         sva_3.38.0
    ##  [13] digest_0.6.27               htmltools_0.5.1.1
    ##  [15] fansi_0.4.2                 memoise_2.0.0
    ##  [17] tensor_1.5                  cluster_2.1.1
    ##  [19] ROCR_1.0-11                 limma_3.46.0
    ##  [21] globals_0.14.0              readr_1.4.0
    ##  [23] annotate_1.68.0             matrixStats_0.58.0
    ##  [25] spatstat.sparse_2.0-0       colorspace_2.0-0
    ##  [27] blob_1.2.1                  xfun_0.22
    ##  [29] crayon_1.4.1                RCurl_1.98-1.3
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    ## [127] IRanges_2.24.1              parallelly_1.24.0
    ## [129] codetools_0.2-18            MASS_7.3-53.1
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    ## [135] sctransform_0.3.2           S4Vectors_0.28.1
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