<!– badges: start –>
<!– badges: end –>
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
“{.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$cell_type = tibble_counts[
match(
tibble_counts$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 = tibble_counts[
match(tibble_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_SE %>%
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 = tibble_counts[
match(tibble_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 = tibble_counts[
match(tibble_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$cell_type = tibble_counts[
match(tibble_counts$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
”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$cell_type = tibble_counts[
match(tibble_counts$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
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 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 17763)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] tidySummarizedExperiment_1.3.0 SummarizedExperiment_1.23.0
## [3] Biobase_2.53.0 GenomicRanges_1.45.0
## [5] GenomeInfoDb_1.29.0 IRanges_2.27.0
## [7] S4Vectors_0.31.0 BiocGenerics_0.39.0
## [9] MatrixGenerics_1.5.0 matrixStats_0.59.0
## [11] tidybulk_1.5.0 ggrepel_0.9.1
## [13] ggplot2_3.3.3 magrittr_2.0.1
## [15] tibble_3.1.2 tidyr_1.1.3
## [17] dplyr_1.0.6 knitr_1.33
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-152 bitops_1.0-7 bit64_4.0.5
## [4] httr_1.4.2 SnowballC_0.7.0 backports_1.2.1
## [7] tools_4.1.0 utf8_1.2.1 R6_2.5.0
## [10] mgcv_1.8-36 DBI_1.1.1 lazyeval_0.2.2
## [13] colorspace_2.0-1 withr_2.4.2 tidyselect_1.1.1
## [16] bit_4.0.4 compiler_4.1.0 preprocessCore_1.55.0
## [19] cli_2.5.0 DelayedArray_0.19.0 plotly_4.9.3
## [22] scales_1.1.1 readr_1.4.0 genefilter_1.75.0
## [25] stringr_1.4.0 digest_0.6.27 XVector_0.33.0
## [28] pkgconfig_2.0.3 htmltools_0.5.1.1 fastmap_1.1.0
## [31] limma_3.49.0 htmlwidgets_1.5.3 rlang_0.4.11
## [34] rstudioapi_0.13 RSQLite_2.2.7 generics_0.1.0
## [37] jsonlite_1.7.2 BiocParallel_1.27.0 tokenizers_0.2.1
## [40] RCurl_1.98-1.3 GenomeInfoDbData_1.2.6 Matrix_1.3-4
## [43] Rcpp_1.0.6 munsell_0.5.0 fansi_0.5.0
## [46] lifecycle_1.0.0 stringi_1.6.2 edgeR_3.35.0
## [49] zlibbioc_1.39.0 plyr_1.8.6 Rtsne_0.15
## [52] grid_4.1.0 blob_1.2.1 crayon_1.4.1
## [55] lattice_0.20-44 Biostrings_2.61.0 splines_4.1.0
## [58] annotate_1.71.0 hms_1.1.0 KEGGREST_1.33.0
## [61] locfit_1.5-9.4 ps_1.6.0 pillar_1.6.1
## [64] widyr_0.1.3 reshape2_1.4.4 codetools_0.2-18
## [67] XML_3.99-0.6 glue_1.4.2 evaluate_0.14
## [70] tidytext_0.3.1 data.table_1.14.0 vctrs_0.3.8
## [73] png_0.1-7 gtable_0.3.0 purrr_0.3.4
## [76] assertthat_0.2.1 cachem_1.0.5 xfun_0.23
## [79] broom_0.7.6 xtable_1.8-4 janeaustenr_0.1.5
## [82] survival_3.2-11 viridisLite_0.4.0 AnnotationDbi_1.55.0
## [85] memoise_2.0.0 sva_3.41.0 ellipsis_0.3.2