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 4.2.2 (2022-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
##
## 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
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] tidySummarizedExperiment_1.8.1 SummarizedExperiment_1.28.0
## [3] Biobase_2.58.0 GenomicRanges_1.50.2
## [5] GenomeInfoDb_1.34.9 IRanges_2.32.0
## [7] S4Vectors_0.36.2 BiocGenerics_0.44.0
## [9] MatrixGenerics_1.10.0 matrixStats_0.63.0
## [11] tidybulk_1.10.1 ggrepel_0.9.3
## [13] ggplot2_3.4.1 magrittr_2.0.3
## [15] tibble_3.2.0 tidyr_1.3.0
## [17] dplyr_1.1.0 knitr_1.42
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-162 bitops_1.0-7 bit64_4.0.5
## [4] httr_1.4.5 SnowballC_0.7.0 tools_4.2.2
## [7] backports_1.4.1 utf8_1.2.3 R6_2.5.1
## [10] DBI_1.1.3 lazyeval_0.2.2 mgcv_1.8-42
## [13] colorspace_2.1-0 withr_2.5.0 tidyselect_1.2.0
## [16] bit_4.0.5 compiler_4.2.2 preprocessCore_1.60.2
## [19] cli_3.6.0 DelayedArray_0.24.0 plotly_4.10.1
## [22] scales_1.2.1 readr_2.1.4 genefilter_1.80.3
## [25] stringr_1.5.0 digest_0.6.31 XVector_0.38.0
## [28] pkgconfig_2.0.3 htmltools_0.5.4 fastmap_1.1.1
## [31] limma_3.54.2 htmlwidgets_1.6.1 rlang_1.1.0
## [34] RSQLite_2.3.0 generics_0.1.3 jsonlite_1.8.4
## [37] BiocParallel_1.32.5 tokenizers_0.3.0 RCurl_1.98-1.10
## [40] GenomeInfoDbData_1.2.9 Matrix_1.5-3 Rcpp_1.0.10
## [43] munsell_0.5.0 fansi_1.0.4 lifecycle_1.0.3
## [46] stringi_1.7.12 edgeR_3.40.2 zlibbioc_1.44.0
## [49] plyr_1.8.8 Rtsne_0.16 grid_4.2.2
## [52] blob_1.2.3 parallel_4.2.2 crayon_1.5.2
## [55] lattice_0.20-45 Biostrings_2.66.0 splines_4.2.2
## [58] annotate_1.76.0 hms_1.1.2 KEGGREST_1.38.0
## [61] locfit_1.5-9.7 pillar_1.8.1 widyr_0.1.5
## [64] reshape2_1.4.4 codetools_0.2-19 XML_3.99-0.13
## [67] glue_1.6.2 evaluate_0.20 tidytext_0.4.1
## [70] data.table_1.14.8 vctrs_0.5.2 png_0.1-8
## [73] tzdb_0.3.0 gtable_0.3.1 purrr_1.0.1
## [76] cachem_1.0.7 xfun_0.37 xtable_1.8-4
## [79] broom_1.0.4 janeaustenr_1.0.0 survival_3.5-5
## [82] viridisLite_0.4.1 AnnotationDbi_1.60.2 memoise_2.0.1
## [85] sva_3.46.0 ellipsis_0.3.2