Brings transcriptomics to the tidyverse

Functions/utilities available

Function	Description
`identify_abundant`	identify the abundant genes
`aggregate_duplicates`	Aggregate abundance and annotation of duplicated transcripts in a robust way
`scale_abundance`	Scale (normalise) abundance for RNA sequencing depth
`reduce_dimensions`	Perform dimensionality reduction (PCA, MDS, tSNE)
`cluster_elements`	Labels elements with cluster identity (kmeans, SNN)
`remove_redundancy`	Filter out elements with highly correlated features
`adjust_abundance`	Remove known unwanted variation (Combat)
`test_differential_abundance`	Differential transcript abundance testing (DE)
`deconvolve_cellularity`	Estimated tissue composition (Cibersort or llsr)
`test_differential_cellularity`	Differential cell-type abundance testing
`keep_variable`	Filter for top variable features
`keep_abundant`	Filter out lowly abundant transcripts
`test_gene_enrichment`	Gene enrichment analyses (EGSEA)
`test_gene_overrepresentation`	Gene enrichment on list of transcript names (no rank)

Utilities	Description
`get_bibliography`	Get the bibliography of your workflow
`tidybulk`	add tidybulk attributes to a tibble object
`tidybulk_SAM_BAM`	Convert SAM BAM files into tidybulk tibble
`pivot_sample`	Select sample-wise columns/information
`pivot_transcript`	Select transcript-wise columns/information
`rotate_dimensions`	Rotate two dimensions of a degree
`ensembl_to_symbol`	Add gene symbol from ensembl IDs
`symbol_to_entrez`	Add entrez ID from gene symbol
`describe_transcript`	Add gene description from gene symbol
`impute_missing_abundance`	Impute abundance for missing data points using sample groupings
`fill_missing_abundance`	Fill abundance for missing data points using an arbitrary value

All functions are directly compatibble with SummarizedExperiment object.

Installation

From Bioconductor

BiocManager::install("tidybulk")

From Github

devtools::install_github("stemangiola/tidybulk")

Data

We will use a SummarizedExperiment object

counts_SE

## # A SummarizedExperiment-tibble abstraction: 408,624 × 8
## # [90mFeatures=8513 | Samples=48 | Assays=count[0m
##    .feature .sample    count Cell.type time  condition batch factor_of_interest
##    <chr>    <chr>      <dbl> <fct>     <fct> <lgl>     <fct> <lgl>             
##  1 A1BG     SRR1740034   153 b_cell    0 d   TRUE      0     TRUE              
##  2 A1BG-AS1 SRR1740034    83 b_cell    0 d   TRUE      0     TRUE              
##  3 AAAS     SRR1740034   868 b_cell    0 d   TRUE      0     TRUE              
##  4 AACS     SRR1740034   222 b_cell    0 d   TRUE      0     TRUE              
##  5 AAGAB    SRR1740034   590 b_cell    0 d   TRUE      0     TRUE              
##  6 AAMDC    SRR1740034    48 b_cell    0 d   TRUE      0     TRUE              
##  7 AAMP     SRR1740034  1257 b_cell    0 d   TRUE      0     TRUE              
##  8 AANAT    SRR1740034   284 b_cell    0 d   TRUE      0     TRUE              
##  9 AAR2     SRR1740034   379 b_cell    0 d   TRUE      0     TRUE              
## 10 AARS2    SRR1740034   685 b_cell    0 d   TRUE      0     TRUE              
## # ℹ 40 more rows

Loading tidySummarizedExperiment will automatically abstract this object as tibble, so we can display it and manipulate it with tidy tools. Although it looks different, and more tools (tidyverse) are available to us, this object is in fact a SummarizedExperiment object.

class(counts_SE)

## [1] "SummarizedExperiment"
## attr(,"package")
## [1] "SummarizedExperiment"

Get the bibliography of your workflow

First of all, you can cite all articles utilised within your workflow automatically from any tidybulk tibble

counts_SE |>	get_bibliography()

Aggregate duplicated `transcripts`

tidybulk provide the aggregate_duplicates function to aggregate duplicated transcripts (e.g., isoforms, ensembl). For example, we often have to convert ensembl symbols to gene/transcript symbol, but in doing so we have to deal with duplicates. aggregate_duplicates takes a tibble and column names (as symbols; for sample, transcript and count) as arguments and returns a tibble with transcripts with the same name aggregated. All the rest of the columns are appended, and factors and boolean are appended as characters.

TidyTranscriptomics

rowData(counts_SE)$gene_name = rownames(counts_SE)
counts_SE.aggr = counts_SE |> aggregate_duplicates(.transcript = gene_name)

Standard procedure (comparative purpose)

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`

We may want to compensate for sequencing depth, scaling the transcript abundance (e.g., with TMM algorithm, Robinson and Oshlack doi.org/10.1186/gb-2010-11-3-r25). scale_abundance takes a tibble, column names (as symbols; for sample, transcript and count) and a method as arguments and returns a tibble with additional columns with scaled data as <NAME OF COUNT COLUMN>_scaled.

TidyTranscriptomics

counts_SE.norm = counts_SE.aggr |> identify_abundant(factor_of_interest = condition) |> scale_abundance()

Standard procedure (comparative purpose)

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)

We can easily plot the scaled density to check the scaling outcome. On the x axis we have the log scaled counts, on the y axes we have the density, data is grouped by sample and coloured by cell type.

counts_SE.norm |>
	ggplot(aes(count_scaled + 1, group=sample, color=`Cell.type`)) +
	geom_density() +
	scale_x_log10() +
	my_theme

plot of chunk plot_normalise

Filter `variable transcripts`

We may want to identify and filter variable transcripts.

TidyTranscriptomics

counts_SE.norm.variable = counts_SE.norm |> keep_variable()

Standard procedure (comparative purpose)

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`

We may want to reduce the dimensions of our data, for example using PCA or MDS algorithms. reduce_dimensions takes a tibble, column names (as symbols; for sample, transcript and count) and a method (e.g., MDS or PCA) as arguments and returns a tibble with additional columns for the reduced dimensions.

MDS (Robinson et al., 10.1093/bioinformatics/btp616)

TidyTranscriptomics

counts_SE.norm.MDS =
  counts_SE.norm |>
  reduce_dimensions(method="MDS", .dims = 6)

Standard procedure (comparative purpose)

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"
]

On the x and y axes axis we have the reduced dimensions 1 to 3, data is coloured by cell type.

counts_SE.norm.MDS |> pivot_sample()  |> select(contains("Dim"), everything())

## # A tibble: 48 × 14
##      Dim1   Dim2   Dim3     Dim4    Dim5    Dim6 .sample    Cell.type      time 
##     <dbl>  <dbl>  <dbl>    <dbl>   <dbl>   <dbl> <chr>      <fct>          <fct>
##  1 -1.46   0.220 -1.68   0.0553   0.0658 -0.126  SRR1740034 b_cell         0 d  
##  2 -1.46   0.226 -1.71   0.0300   0.0454 -0.137  SRR1740035 b_cell         1 d  
##  3 -1.44   0.193 -1.60   0.0890   0.0503 -0.121  SRR1740036 b_cell         3 d  
##  4 -1.44   0.198 -1.67   0.0891   0.0543 -0.110  SRR1740037 b_cell         7 d  
##  5  0.243 -1.42   0.182  0.00642 -0.503  -0.131  SRR1740038 dendritic_mye… 0 d  
##  6  0.191 -1.42   0.195  0.0180  -0.457  -0.130  SRR1740039 dendritic_mye… 1 d  
##  7  0.257 -1.42   0.152  0.0130  -0.582  -0.0927 SRR1740040 dendritic_mye… 3 d  
##  8  0.162 -1.43   0.189  0.0232  -0.452  -0.109  SRR1740041 dendritic_mye… 7 d  
##  9  0.516 -1.47   0.240 -0.251    0.457  -0.119  SRR1740042 monocyte       0 d  
## 10  0.514 -1.41   0.231 -0.219    0.458  -0.131  SRR1740043 monocyte       1 d  
## # ℹ 38 more rows
## # ℹ 5 more variables: condition <lgl>, batch <fct>, factor_of_interest <lgl>,
## #   TMM <dbl>, multiplier <dbl>

counts_SE.norm.MDS |>
	pivot_sample() |>
  GGally::ggpairs(columns = 6:(6+5), ggplot2::aes(colour=`Cell.type`))

plot of chunk plot_mds

PCA

TidyTranscriptomics

counts_SE.norm.PCA =
  counts_SE.norm |>
  reduce_dimensions(method="PCA", .dims = 6)

Standard procedure (comparative purpose)

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"
]

On the x and y axes axis we have the reduced dimensions 1 to 3, data is coloured by cell type.

counts_SE.norm.PCA |> pivot_sample() |> select(contains("PC"), everything())

## # A tibble: 48 × 14
##        PC1   PC2    PC3     PC4    PC5   PC6 .sample   Cell.type time  condition
##      <dbl> <dbl>  <dbl>   <dbl>  <dbl> <dbl> <chr>     <fct>     <fct> <lgl>    
##  1 -12.6   -2.52 -14.9  -0.424  -0.592 -1.22 SRR17400… b_cell    0 d   TRUE     
##  2 -12.6   -2.57 -15.2  -0.140  -0.388 -1.30 SRR17400… b_cell    1 d   TRUE     
##  3 -12.6   -2.41 -14.5  -0.714  -0.344 -1.10 SRR17400… b_cell    3 d   TRUE     
##  4 -12.5   -2.34 -14.9  -0.816  -0.427 -1.00 SRR17400… b_cell    7 d   TRUE     
##  5   0.189 13.0    1.66 -0.0269  4.64  -1.35 SRR17400… dendriti… 0 d   FALSE    
##  6  -0.293 12.9    1.76 -0.0727  4.21  -1.28 SRR17400… dendriti… 1 d   FALSE    
##  7   0.407 13.0    1.42 -0.0529  5.37  -1.01 SRR17400… dendriti… 3 d   FALSE    
##  8  -0.620 13.0    1.73 -0.201   4.17  -1.07 SRR17400… dendriti… 7 d   FALSE    
##  9   2.56  13.5    2.32  2.03   -4.32  -1.22 SRR17400… monocyte  0 d   FALSE    
## 10   2.65  13.1    2.21  1.80   -4.29  -1.30 SRR17400… monocyte  1 d   FALSE    
## # ℹ 38 more rows
## # ℹ 4 more variables: batch <fct>, factor_of_interest <lgl>, TMM <dbl>,
## #   multiplier <dbl>

counts_SE.norm.PCA |>
	 pivot_sample() |>
  GGally::ggpairs(columns = 11:13, ggplot2::aes(colour=`Cell.type`))

plot of chunk plot_pca

tSNE

TidyTranscriptomics

counts_SE.norm.tSNE =
	breast_tcga_mini_SE |>
	identify_abundant() |>
	reduce_dimensions(
		method = "tSNE",
		perplexity=10,
		pca_scale =TRUE
	)

Standard procedure (comparative purpose)

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"
]

Plot

counts_SE.norm.tSNE |>
	pivot_sample() |>
	select(contains("tSNE"), everything())

## # A tibble: 251 × 4
##     tSNE1   tSNE2 .sample                      Call 
##     <dbl>   <dbl> <chr>                        <fct>
##  1   8.36  -4.98  TCGA-A1-A0SD-01A-11R-A115-07 LumA 
##  2  -6.33   2.25  TCGA-A1-A0SF-01A-11R-A144-07 LumA 
##  3  11.3  -13.8   TCGA-A1-A0SG-01A-11R-A144-07 LumA 
##  4   5.80   0.304 TCGA-A1-A0SH-01A-11R-A084-07 LumA 
##  5   5.46  -4.30  TCGA-A1-A0SI-01A-11R-A144-07 LumB 
##  6  -1.36   5.50  TCGA-A1-A0SJ-01A-11R-A084-07 LumA 
##  7 -16.6   26.7   TCGA-A1-A0SK-01A-12R-A084-07 Basal
##  8   8.55   8.29  TCGA-A1-A0SM-01A-11R-A084-07 LumA 
##  9   7.13   7.71  TCGA-A1-A0SN-01A-11R-A144-07 LumB 
## 10  18.6  -12.5   TCGA-A1-A0SQ-01A-21R-A144-07 LumA 
## # ℹ 241 more rows

counts_SE.norm.tSNE |>
	pivot_sample() |>
	ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=Call)) + geom_point() + my_theme

plot of chunk unnamed-chunk-12

Rotate `dimensions`

We may want to rotate the reduced dimensions (or any two numeric columns really) of our data, of a set angle. rotate_dimensions takes a tibble, column names (as symbols; for sample, transcript and count) and an angle as arguments and returns a tibble with additional columns for the rotated dimensions. The rotated dimensions will be added to the original data set as <NAME OF DIMENSION> rotated <ANGLE> by default, or as specified in the input arguments.

TidyTranscriptomics

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

Standard procedure (comparative purpose)

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"
]

Original On the x and y axes axis we have the first two reduced dimensions, data is coloured by cell type.

counts_SE.norm.MDS.rotated |>
	ggplot(aes(x=`Dim1`, y=`Dim2`, color=`Cell.type` )) +
  geom_point() +
  my_theme

plot of chunk plot_rotate_1

Rotated On the x and y axes axis we have the first two reduced dimensions rotated of 45 degrees, data is coloured by cell type.

counts_SE.norm.MDS.rotated |>
	pivot_sample() |>
	ggplot(aes(x=`Dim1_rotated_45`, y=`Dim2_rotated_45`, color=`Cell.type` )) +
  geom_point() +
  my_theme

plot of chunk plot_rotate_2

Test `differential abundance`

We may want to test for differential transcription between sample-wise factors of interest (e.g., with edgeR). test_differential_abundance takes a tibble, column names (as symbols; for sample, transcript and count) and a formula representing the desired linear model as arguments and returns a tibble with additional columns for the statistics from the hypothesis test (e.g., log fold change, p-value and false discovery rate).

TidyTranscriptomics

counts_SE.de =
	counts_SE |>
	test_differential_abundance( ~ condition, action="get")
counts_SE.de

Standard procedure (comparative purpose)

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)

The functon test_differential_abundance operated with contrasts too. The constrasts hve the name of the design matrix (generally <NAME_COLUMN_COVARIATE><VALUES_OF_COVARIATE>)

counts_SE.de =
	counts_SE |>
	identify_abundant(factor_of_interest = condition) |>
	test_differential_abundance(
		~ 0 + condition,                  
		.contrasts = c( "conditionTRUE - conditionFALSE"),
		action="get"
	)

Adjust `counts`

We may want to adjust counts for (known) unwanted variation. adjust_abundance takes as arguments a tibble, column names (as symbols; for sample, transcript and count) and a formula representing the desired linear model where the first covariate is the factor of interest and the second covariate is the unwanted variation, and returns a tibble with additional columns for the adjusted counts as <COUNT COLUMN>_adjusted. At the moment just an unwanted covariated is allowed at a time.

TidyTranscriptomics

counts_SE.norm.adj =
	counts_SE.norm |> adjust_abundance(	~ factor_of_interest + batch)

Standard procedure (comparative purpose)

library(sva)

count_m_log = log(count_m + 1)

design =
		model.matrix(
			object = ~ factor_of_interest + batch,
			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`

We may want to infer the cell type composition of our samples (with the algorithm Cibersort; Newman et al., 10.1038/nmeth.3337). deconvolve_cellularity takes as arguments a tibble, column names (as symbols; for sample, transcript and count) and returns a tibble with additional columns for the adjusted cell type proportions.

TidyTranscriptomics

counts_SE.cibersort =
	counts_SE |>
	deconvolve_cellularity(action="get", cores=1, prefix = "cibersort__")

Standard procedure (comparative purpose)

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"
]

With the new annotated data frame, we can plot the distributions of cell types across samples, and compare them with the nominal cell type labels to check for the purity of isolation. On the x axis we have the cell types inferred by Cibersort, on the y axis we have the inferred proportions. The data is facetted and coloured by nominal cell types (annotation given by the researcher after FACS sorting).

counts_SE.cibersort |>
	pivot_longer(
		names_to= "Cell_type_inferred", 
		values_to = "proportion", 
		names_prefix ="cibersort__", 
		cols=contains("cibersort__")
	) |>
  ggplot(aes(x=Cell_type_inferred, y=proportion, fill=`Cell.type`)) +
  geom_boxplot() +
  facet_wrap(~`Cell.type`) +
  my_theme +
  theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5), aspect.ratio=1/5)

Test differential cell-type abundance

We can also perform a statistical test on the differential cell-type abundance across conditions

	counts_SE |>
	test_differential_cellularity(. ~ condition )

We can also perform regression analysis with censored data (coxph).

	counts_SE |>
	test_differential_cellularity(survival::Surv(time, dead) ~ .)

Cluster `samples`

We may want to cluster our data (e.g., using k-means sample-wise). cluster_elements takes as arguments a tibble, column names (as symbols; for sample, transcript and count) and returns a tibble with additional columns for the cluster annotation. At the moment only k-means clustering is supported, the plan is to introduce more clustering methods.

k-means

TidyTranscriptomics

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

Standard procedure (comparative purpose)

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")
]

We can add cluster annotation to the MDS dimension reduced data set and plot.

 counts_SE.norm.cluster |>
	ggplot(aes(x=`Dim1`, y=`Dim2`, color=`cluster_kmeans`)) +
  geom_point() +
  my_theme

plot of chunk plot_cluster

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.

TidyTranscriptomics

counts_SE.norm.SNN =
	counts_SE.norm.tSNE |>
	cluster_elements(method = "SNN")

Standard procedure (comparative purpose)

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")
]

counts_SE.norm.SNN |>
	pivot_sample() |>
	select(contains("tSNE"), everything()) 

counts_SE.norm.SNN |>
	pivot_sample() |>
	gather(source, Call, c("cluster_SNN", "Call")) |>
	distinct() |>
	ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=Call)) + geom_point() + facet_grid(~source) + my_theme


# Do differential transcription between clusters
counts_SE.norm.SNN |>
	mutate(factor_of_interest = `cluster_SNN` == 3) |>
	test_differential_abundance(
    ~ factor_of_interest,
    action="get"
   )

Drop `redundant` transcripts

We may want to remove redundant elements from the original data set (e.g., samples or transcripts), for example if we want to define cell-type specific signatures with low sample redundancy. remove_redundancy takes as arguments a tibble, column names (as symbols; for sample, transcript and count) and returns a tibble with redundant elements removed (e.g., samples). Two redundancy estimation approaches are supported:

removal of highly correlated clusters of elements (keeping a representative) with method=“correlation”
removal of most proximal element pairs in a reduced dimensional space.

Approach 1

TidyTranscriptomics

counts_SE.norm.non_redundant =
	counts_SE.norm.MDS |>
  remove_redundancy(	method = "correlation" )

Standard procedure (comparative purpose)

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 redudant data frame
counts |> anti_join(.data.correlated) |>
	spread(sample, rc, - transcript) |>
	left_join(annotation)

We can visualise how the reduced redundancy with the reduced dimentions look like

counts_SE.norm.non_redundant |>
	pivot_sample() |>
	ggplot(aes(x=`Dim1`, y=`Dim2`, color=`Cell.type`)) +
  geom_point() +
  my_theme

plot of chunk plot_drop

Approach 2

counts_SE.norm.non_redundant =
	counts_SE.norm.MDS |>
  remove_redundancy(
  	method = "reduced_dimensions",
  	Dim_a_column = `Dim1`,
  	Dim_b_column = `Dim2`
  )

We can visualise MDS reduced dimensions of the samples with the closest pair removed.

counts_SE.norm.non_redundant |>
	pivot_sample() |>
	ggplot(aes(x=`Dim1`, y=`Dim2`, color=`Cell.type`)) +
  geom_point() +
  my_theme

plot of chunk plot_drop2

Other useful wrappers

The above wrapper streamline the most common processing of bulk RNA sequencing data. Other useful wrappers are listed above.

From BAM/SAM to tibble of gene counts

We can calculate gene counts (using FeatureCounts; Liao Y et al., 10.1093/nar/gkz114) from a list of BAM/SAM files and format them into a tidy structure (similar to counts).

counts = tidybulk_SAM_BAM(
	file_names,
	genome = "hg38",
	isPairedEnd = TRUE,
	requireBothEndsMapped = TRUE,
	checkFragLength = FALSE,
	useMetaFeatures = TRUE
)

From ensembl IDs to gene symbol IDs

We can add gene symbols from ensembl identifiers. This is useful since different resources use ensembl IDs while others use gene symbol IDs. This currently works for human and mouse.

counts_ensembl |> ensembl_to_symbol(ens)

## # A tibble: 119 × 8
##    ens   iso   `read count` sample cases_0_project_dise…¹ cases_0_samples_0_sa…²
##    <chr> <chr>        <dbl> <chr>  <chr>                  <chr>                 
##  1 ENSG… 13             144 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
##  2 ENSG… 13              72 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
##  3 ENSG… 13               0 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
##  4 ENSG… 13            1099 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
##  5 ENSG… 13              11 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
##  6 ENSG… 13               2 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
##  7 ENSG… 13               3 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
##  8 ENSG… 13            2678 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
##  9 ENSG… 13             751 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## 10 ENSG… 13               1 TARGE… Acute Myeloid Leukemia Primary Blood Derived…
## # ℹ 109 more rows
## # ℹ abbreviated names: ¹cases_0_project_disease_type,
## #   ²cases_0_samples_0_sample_type
## # ℹ 2 more variables: transcript <chr>, ref_genome <chr>

From gene symbol to gene description (gene name in full)

We can add gene full name (and in future description) from symbol identifiers. This currently works for human and mouse.

counts_SE |> 
	describe_transcript() |> 
	select(feature, description, everything())

## # A SummarizedExperiment-tibble abstraction: 408,624 × 10
## # [90mFeatures=8513 | Samples=48 | Assays=count[0m
##    feature  sample     count Cell.type time  condition batch factor_of_interest
##    <chr>    <chr>      <dbl> <fct>     <fct> <lgl>     <fct> <lgl>             
##  1 A1BG     SRR1740034   153 b_cell    0 d   TRUE      0     TRUE              
##  2 A1BG-AS1 SRR1740034    83 b_cell    0 d   TRUE      0     TRUE              
##  3 AAAS     SRR1740034   868 b_cell    0 d   TRUE      0     TRUE              
##  4 AACS     SRR1740034   222 b_cell    0 d   TRUE      0     TRUE              
##  5 AAGAB    SRR1740034   590 b_cell    0 d   TRUE      0     TRUE              
##  6 AAMDC    SRR1740034    48 b_cell    0 d   TRUE      0     TRUE              
##  7 AAMP     SRR1740034  1257 b_cell    0 d   TRUE      0     TRUE              
##  8 AANAT    SRR1740034   284 b_cell    0 d   TRUE      0     TRUE              
##  9 AAR2     SRR1740034   379 b_cell    0 d   TRUE      0     TRUE              
## 10 AARS2    SRR1740034   685 b_cell    0 d   TRUE      0     TRUE              
## # ℹ 40 more rows
## # ℹ 2 more variables: description <chr>, gene_name <chr>

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          e1071_1.7-13           
##   [7] compiler_4.3.0          RSQLite_2.3.1           mgcv_1.8-42            
##  [10] reshape2_1.4.4          png_0.1-8               vctrs_0.6.2            
##  [13] sva_3.48.0              stringr_1.5.0           pkgconfig_2.0.3        
##  [16] crayon_1.5.2            fastmap_1.1.1           backports_1.4.1        
##  [19] XVector_0.40.0          ellipsis_0.3.2          labeling_0.4.2         
##  [22] utf8_1.2.3              markdown_1.6            tzdb_0.3.0             
##  [25] preprocessCore_1.62.0   purrr_1.0.1             bit_4.0.5              
##  [28] xfun_0.39               zlibbioc_1.46.0         cachem_1.0.7           
##  [31] jsonlite_1.8.4          blob_1.2.4              highr_0.10             
##  [34] SnowballC_0.7.0         reshape_0.8.9           DelayedArray_0.26.0    
##  [37] BiocParallel_1.34.0     broom_1.0.4             parallel_4.3.0         
##  [40] R6_2.5.1                RColorBrewer_1.1-3      stringi_1.7.12         
##  [43] GGally_2.1.2            limma_3.56.0            genefilter_1.82.0      
##  [46] Rcpp_1.0.10             org.Mm.eg.db_3.17.0     readr_2.1.4            
##  [49] Matrix_1.5-4            splines_4.3.0           tidyselect_1.2.0       
##  [52] yaml_2.3.7              codetools_0.2-19        plyr_1.8.8             
##  [55] lattice_0.21-8          withr_2.5.0             KEGGREST_1.40.0        
##  [58] evaluate_0.20           Rtsne_0.16              survival_3.5-5         
##  [61] proxy_0.4-27            Biostrings_2.68.0       pillar_1.9.0           
##  [64] janeaustenr_1.0.0       plotly_4.10.1           generics_0.1.3         
##  [67] RCurl_1.98-1.12         hms_1.1.3               commonmark_1.9.0       
##  [70] munsell_0.5.0           scales_1.2.1            xtable_1.8-4           
##  [73] class_7.3-21            glue_1.6.2              lazyeval_0.2.2         
##  [76] tools_4.3.0             tokenizers_0.3.0        data.table_1.14.8      
##  [79] annotate_1.78.0         locfit_1.5-9.7          XML_3.99-0.14          
##  [82] grid_4.3.0              AnnotationDbi_1.62.0    edgeR_3.42.0           
##  [85] colorspace_2.1-0        nlme_3.1-162            GenomeInfoDbData_1.2.10
##  [88] cli_3.6.1               fansi_1.0.4             viridisLite_0.4.1      
##  [91] gtable_0.3.3            digest_0.6.31           org.Hs.eg.db_3.17.0    
##  [94] farver_2.1.1            htmlwidgets_1.6.2       memoise_2.0.1          
##  [97] htmltools_0.5.5         lifecycle_1.0.3         httr_1.4.5             
## [100] bit64_4.0.5

Overview of the tidybulk package

Stefano Mangiola

2023-04-25

Functions/utilities available

Installation

Data

Get the bibliography of your workflow

Aggregate duplicated `transcripts`

Scale `counts`

Filter `variable transcripts`

Reduce `dimensions`

Rotate `dimensions`

Test `differential abundance`

Adjust `counts`

Deconvolve `Cell type composition`

Test differential cell-type abundance

Cluster `samples`

Drop `redundant` transcripts

Other useful wrappers

From BAM/SAM to tibble of gene counts

From ensembl IDs to gene symbol IDs

From gene symbol to gene description (gene name in full)

Appendix

Overview of the tidybulk package

Stefano Mangiola

2023-04-25

Functions/utilities available

Installation

Data

Get the bibliography of your workflow

Aggregate duplicated transcripts

Scale counts

Filter variable transcripts

Reduce dimensions

Rotate dimensions

Test differential abundance

Adjust counts

Deconvolve Cell type composition

Test differential cell-type abundance

Cluster samples

Drop redundant transcripts

Other useful wrappers

From BAM/SAM to tibble of gene counts

From ensembl IDs to gene symbol IDs

From gene symbol to gene description (gene name in full)

Appendix

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