tidybulk - part of tidyTranscriptomics
================
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**Brings transcriptomics to the tidyverse!**
The code is released under the version 3 of the GNU General Public
License.
<img src="man/figures/logo.png" width="120px" height="139px" />
website:
[stemangiola.github.io/tidybulk/](http://stemangiola.github.io/tidybulk/)
[Third party
tutorials](https://rstudio-pubs-static.s3.amazonaws.com/792462_f948e766b15d4ee5be5c860493bda0b3.html)
Please have a look also to
- [tidySummarizedExperiment](https://github.com/stemangiola/tidySummarizedExperiment)
for bulk data tidy representation
- [tidySingleCellExperiment](https://github.com/stemangiola/tidySingleCellExperiment)
for single-cell data tidy representation
- [tidyseurat](https://github.com/stemangiola/tidyseurat) for
single-cell data tidy representation
- [tidyHeatmap](https://github.com/stemangiola/tidyHeatmap) for heatmaps
produced with tidy principles analysis and manipulation
- [tidygate](https://github.com/stemangiola/tidygate) for adding custom
gate information to your tibble
<!---
[](https://travis-ci.org/stemangiola/tidybulk) [](https://coveralls.io/github/stemangiola/tidybulk?branch=master)
-->
<img src="man/figures/new_SE_usage-01.png" width="800px" />
## Functions/utilities available
| Function | Description |
|-------------------------------------|------------------------------------------------------------------------------------|
| `aggregate_duplicates` | Aggregate abundance and annotation of duplicated transcripts in a robust way |
| `identify_abundant` `keep_abundant` | identify or keep the abundant genes |
| `keep_variable` | Filter for top variable features |
| `scale_abundance` | Scale (normalise) abundance for RNA sequencing depth |
| `reduce_dimensions` | Perform dimensionality reduction (PCA, MDS, tSNE, UMAP) |
| `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 (DESeq2, edgeR, voom) |
| `deconvolve_cellularity` | Estimated tissue composition (Cibersort, llsr, epic, xCell, mcp_counter, quantiseq |
| `test_differential_cellularity` | Differential cell-type abundance testing |
| `test_stratification_cellularity` | Estimate Kaplan-Meier survival differences |
| `test_gene_enrichment` | Gene enrichment analyses (EGSEA) |
| `test_gene_overrepresentation` | Gene enrichment on list of transcript names (no rank) |
| `test_gene_rank` | Gene enrichment on list of transcript (GSEA) |
| `impute_missing_abundance` | Impute abundance for missing data points using sample groupings |
| 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 |
All functions are directly compatibble with `SummarizedExperiment`
object.
## Installation
From Bioconductor
``` r
BiocManager::install("tidybulk")
```
From Github
``` r
devtools::install_github("stemangiola/tidybulk")
```
# Data
We will use a `SummarizedExperiment` object
``` r
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.
``` r
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
``` r
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.
<div class="column-left">
TidyTranscriptomics
``` r
rowData(counts_SE)$gene_name = rownames(counts_SE)
counts_SE.aggr = counts_SE |> aggregate_duplicates(.transcript = gene_name)
```
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` r
temp = data.frame(
symbol = dge_list$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)
```
</div>
<div style="clear:both;">
</div>
## 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`.
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm = counts_SE.aggr |> identify_abundant(factor_of_interest = condition) |> scale_abundance()
```
## tidybulk says: the sample with largest library size SRR1740080 was chosen as reference for scaling
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` 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)
```
</div>
<div style="clear:both;">
</div>
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.
``` r
counts_SE.norm |>
ggplot(aes(count_scaled + 1, group=.sample, color=`Cell.type`)) +
geom_density() +
scale_x_log10() +
my_theme
```
<!-- -->
## Filter `variable transcripts`
We may want to identify and filter variable transcripts.
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm.variable = counts_SE.norm |> keep_variable()
```
## Getting the 500 most variable genes
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` 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$sample,
rownames(norm_counts.table)
),
"Cell.type"
]
```
</div>
<div style="clear:both;">
</div>
## Reduce `dimensions`
We may want to reduce the dimensions of our data, for example using PCA
or MDS algorithms. `reduce_dimensions` takes a tibble, column names (as
symbols; for `sample`, `transcript` and `count`) and a method (e.g., MDS
or PCA) as arguments and returns a tibble with additional columns for
the reduced dimensions.
**MDS** (Robinson et al., 10.1093/bioinformatics/btp616)
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm.MDS =
counts_SE.norm |>
reduce_dimensions(method="MDS", .dims = 6)
```
## Getting the 500 most variable genes
## tidybulk says: to access the raw results do `attr(..., "internals")$MDS`
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` 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"
]
```
</div>
<div style="clear:both;">
</div>
On the x and y axes axis we have the reduced dimensions 1 to 3, data is
coloured by cell type.
``` r
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>
``` r
counts_SE.norm.MDS |>
pivot_sample() |>
GGally::ggpairs(columns = 6:(6+5), ggplot2::aes(colour=`Cell.type`))
```
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
<!-- -->
**PCA**
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm.PCA =
counts_SE.norm |>
reduce_dimensions(method="PCA", .dims = 6)
```
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` 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"
]
```
</div>
<div style="clear:both;">
</div>
On the x and y axes axis we have the reduced dimensions 1 to 3, data is
coloured by cell type.
``` r
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>
``` r
counts_SE.norm.PCA |>
pivot_sample() |>
GGally::ggpairs(columns = 11:13, ggplot2::aes(colour=`Cell.type`))
```
<!-- -->
**tSNE**
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm.tSNE =
breast_tcga_mini_SE |>
identify_abundant() |>
reduce_dimensions(
method = "tSNE",
perplexity=10,
pca_scale =TRUE
)
```
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` 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$sample, rownames(tsne)),
"Cell.type"
]
```
</div>
<div style="clear:both;">
</div>
Plot
``` r
counts_SE.norm.tSNE |>
pivot_sample() |>
select(contains("tSNE"), everything())
```
## # A tibble: 251 × 4
## tSNE1 tSNE2 .sample Call
## <dbl> <dbl> <chr> <fct>
## 1 -4.29 5.40 TCGA-A1-A0SD-01A-11R-A115-07 LumA
## 2 4.48 -2.82 TCGA-A1-A0SF-01A-11R-A144-07 LumA
## 3 -9.06 0.637 TCGA-A1-A0SG-01A-11R-A144-07 LumA
## 4 7.05 7.28 TCGA-A1-A0SH-01A-11R-A084-07 LumA
## 5 -2.38 2.77 TCGA-A1-A0SI-01A-11R-A144-07 LumB
## 6 -1.63 -5.67 TCGA-A1-A0SJ-01A-11R-A084-07 LumA
## 7 18.2 -18.3 TCGA-A1-A0SK-01A-12R-A084-07 Basal
## 8 -9.06 -11.7 TCGA-A1-A0SM-01A-11R-A084-07 LumA
## 9 -8.88 -10.3 TCGA-A1-A0SN-01A-11R-A144-07 LumB
## 10 -8.30 23.4 TCGA-A1-A0SQ-01A-21R-A144-07 LumA
## # ℹ 241 more rows
``` r
counts_SE.norm.tSNE |>
pivot_sample() |>
ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=Call)) + geom_point() + my_theme
```
<!-- -->
## Rotate `dimensions`
We may want to rotate the reduced dimensions (or any two numeric columns
really) of our data, of a set angle. `rotate_dimensions` takes a tibble,
column names (as symbols; for `sample`, `transcript` and `count`) and an
angle as arguments and returns a tibble with additional columns for the
rotated dimensions. The rotated dimensions will be added to the original
data set as `<NAME OF DIMENSION> rotated <ANGLE>` by default, or as
specified in the input arguments.
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm.MDS.rotated =
counts_SE.norm.MDS |>
rotate_dimensions(`Dim1`, `Dim2`, rotation_degrees = 45, action="get")
```
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` r
rotation = function(m, d) {
r = d * pi / 180
((bind_rows(
c(`1` = cos(r), `2` = -sin(r)),
c(`1` = sin(r), `2` = cos(r))
) |> as_matrix()) %*% m)
}
mds_r = pca |> rotation(rotation_degrees)
mds_r$cell_type = counts[
match(counts$sample, rownames(mds_r)),
"Cell.type"
]
```
</div>
<div style="clear:both;">
</div>
**Original** On the x and y axes axis we have the first two reduced
dimensions, data is coloured by cell type.
``` r
counts_SE.norm.MDS.rotated |>
ggplot(aes(x=`Dim1`, y=`Dim2`, color=`Cell.type` )) +
geom_point() +
my_theme
```
<!-- -->
**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.
``` r
counts_SE.norm.MDS.rotated |>
pivot_sample() |>
ggplot(aes(x=`Dim1_rotated_45`, y=`Dim2_rotated_45`, color=`Cell.type` )) +
geom_point() +
my_theme
```
<!-- -->
## 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).
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.de =
counts_SE |>
test_differential_abundance( ~ condition, action="get")
counts_SE.de
```
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` 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)
```
</div>
<div style="clear:both;">
</div>
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>)
``` r
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.
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm.adj =
counts_SE.norm |> adjust_abundance( .factor_unwanted = batch, .factor_of_interest = factor_of_interest)
```
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` r
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$sample, rownames(count_m_log.sva)),
"Cell.type"
]
```
</div>
<div style="clear:both;">
</div>
## Deconvolve `Cell type composition`
We may want to infer the cell type composition of our samples (with the
algorithm Cibersort; Newman et al., 10.1038/nmeth.3337).
`deconvolve_cellularity` takes as arguments a tibble, column names (as
symbols; for `sample`, `transcript` and `count`) and returns a tibble
with additional columns for the adjusted cell type proportions.
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.cibersort =
counts_SE |>
deconvolve_cellularity(action="get", cores=1, prefix = "cibersort__")
```
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` r
source(‘CIBERSORT.R’)
count_m |> write.table("mixture_file.txt")
results <- CIBERSORT(
"sig_matrix_file.txt",
"mixture_file.txt",
perm=100, QN=TRUE
)
results$cell_type = tibble_counts[
match(tibble_counts$sample, rownames(results)),
"Cell.type"
]
```
</div>
<div style="clear:both;">
</div>
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).
``` r
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)
```
## tidySummarizedExperiment says: A data frame is returned for independent data analysis.
<!-- -->
## Test differential cell-type abundance
We can also perform a statistical test on the differential cell-type
abundance across conditions
``` r
counts_SE |>
test_differential_cellularity(. ~ condition )
```
## # A tibble: 22 × 7
## .cell_type cell_type_proportions `estimate_(Intercept)`
## <chr> <list> <dbl>
## 1 cibersort.B.cells.naive <tibble [48 × 8]> -2.94
## 2 cibersort.B.cells.memory <tibble [48 × 8]> -4.86
## 3 cibersort.Plasma.cells <tibble [48 × 8]> -5.33
## 4 cibersort.T.cells.CD8 <tibble [48 × 8]> -2.33
## 5 cibersort.T.cells.CD4.naive <tibble [48 × 8]> -2.83
## 6 cibersort.T.cells.CD4.memory.re… <tibble [48 × 8]> -2.46
## 7 cibersort.T.cells.CD4.memory.ac… <tibble [48 × 8]> -3.67
## 8 cibersort.T.cells.follicular.he… <tibble [48 × 8]> -5.68
## 9 cibersort.T.cells.regulatory..T… <tibble [48 × 8]> -5.04
## 10 cibersort.T.cells.gamma.delta <tibble [48 × 8]> -4.78
## # ℹ 12 more rows
## # ℹ 4 more variables: estimate_conditionTRUE <dbl>,
## # std.error_conditionTRUE <dbl>, statistic_conditionTRUE <dbl>,
## # p.value_conditionTRUE <dbl>
We can also perform regression analysis with censored data (coxph).
``` r
# Add survival data
counts_SE_survival =
counts_SE |>
nest(data = -sample) |>
mutate(
days = sample(1:1000, size = n()),
dead = sample(c(0,1), size = n(), replace = TRUE)
) |>
unnest(data)
```
## Warning in is_sample_feature_deprecated_used(.data, .cols):
## tidySummarizedExperiment says: from version 1.3.1, the special columns
## including sample/feature id (colnames(se), rownames(se)) has changed to
## ".sample" and ".feature". This dataset is returned with the old-style
## vocabulary (feature and sample), however we suggest to update your workflow to
## reflect the new vocabulary (.feature, .sample)
## Warning in is_sample_feature_deprecated_used(.data, .cols):
## tidySummarizedExperiment says: from version 1.3.1, the special columns
## including sample/feature id (colnames(se), rownames(se)) has changed to
## ".sample" and ".feature". This dataset is returned with the old-style
## vocabulary (feature and sample), however we suggest to update your workflow to
## reflect the new vocabulary (.feature, .sample)
``` r
# Test
counts_SE_survival |>
test_differential_cellularity(survival::Surv(days, dead) ~ .)
```
## # A tibble: 22 × 6
## .cell_type cell_type_proportions estimate std.error statistic p.value
## <chr> <list> <dbl> <dbl> <dbl> <dbl>
## 1 cibersort.B.cells… <tibble [48 × 9]> 5.15 1.58 3.27 0.00108
## 2 cibersort.B.cells… <tibble [48 × 9]> 2.12 1.48 1.43 0.153
## 3 cibersort.Plasma.… <tibble [48 × 9]> 2.96 1.35 2.20 0.0279
## 4 cibersort.T.cells… <tibble [48 × 9]> 3.94 1.71 2.30 0.0215
## 5 cibersort.T.cells… <tibble [48 × 9]> 3.34 1.75 1.91 0.0560
## 6 cibersort.T.cells… <tibble [48 × 9]> -0.785 0.868 -0.904 0.366
## 7 cibersort.T.cells… <tibble [48 × 9]> -3.15 1.65 -1.91 0.0568
## 8 cibersort.T.cells… <tibble [48 × 9]> -0.435 0.421 -1.03 0.301
## 9 cibersort.T.cells… <tibble [48 × 9]> 0.795 0.757 1.05 0.294
## 10 cibersort.T.cells… <tibble [48 × 9]> -0.0292 0.641 -0.0456 0.964
## # ℹ 12 more rows
We can also perform test of Kaplan-Meier curves.
``` r
counts_stratified =
counts_SE_survival |>
# Test
test_stratification_cellularity(
survival::Surv(days, dead) ~ .,
sample, transcript, count
)
counts_stratified
```
## # A tibble: 22 × 6
## .cell_type cell_type_proportions .low_cellularity_exp…¹
## <chr> <list> <dbl>
## 1 cibersort.B.cells.naive <tibble [48 × 9]> 9.41
## 2 cibersort.B.cells.memory <tibble [48 × 9]> 10.5
## 3 cibersort.Plasma.cells <tibble [48 × 9]> 12.5
## 4 cibersort.T.cells.CD8 <tibble [48 × 9]> 11.0
## 5 cibersort.T.cells.CD4.naive <tibble [48 × 9]> 8.40
## 6 cibersort.T.cells.CD4.memory.re… <tibble [48 × 9]> 9.09
## 7 cibersort.T.cells.CD4.memory.ac… <tibble [48 × 9]> 11.2
## 8 cibersort.T.cells.follicular.he… <tibble [48 × 9]> 13.7
## 9 cibersort.T.cells.regulatory..T… <tibble [48 × 9]> 8.16
## 10 cibersort.T.cells.gamma.delta <tibble [48 × 9]> 14.8
## # ℹ 12 more rows
## # ℹ abbreviated name: ¹.low_cellularity_expected
## # ℹ 3 more variables: .high_cellularity_expected <dbl>, pvalue <dbl>,
## # plot <list>
Plot Kaplan-Meier curves
``` r
counts_stratified$plot[[1]]
```
<!-- -->
## 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**
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm.cluster = counts_SE.norm.MDS |>
cluster_elements(method="kmeans", centers = 2, action="get" )
```
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` 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$sample, rownames(cluster)),
c("Cell.type", "Dim1", "Dim2")
]
```
</div>
<div style="clear:both;">
</div>
We can add cluster annotation to the MDS dimension reduced data set and
plot.
``` r
counts_SE.norm.cluster |>
ggplot(aes(x=`Dim1`, y=`Dim2`, color=`cluster_kmeans`)) +
geom_point() +
my_theme
```
<!-- -->
**SNN**
Matrix package (v1.3-3) causes an error with Seurat::FindNeighbors used
in this method. We are trying to solve this issue. At the moment this
option in unaviable.
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm.SNN =
counts_SE.norm.tSNE |>
cluster_elements(method = "SNN")
```
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` r
library(Seurat)
snn = CreateSeuratObject(count_m)
snn = ScaleData(
snn, display.progress = TRUE,
num.cores=4, do.par = TRUE
)
snn = FindVariableFeatures(snn, selection.method = "vst")
snn = FindVariableFeatures(snn, selection.method = "vst")
snn = RunPCA(snn, npcs = 30)
snn = FindNeighbors(snn)
snn = FindClusters(snn, method = "igraph", ...)
snn = snn[["seurat_clusters"]]
snn$cell_type = tibble_counts[
match(tibble_counts$sample, rownames(snn)),
c("Cell.type", "Dim1", "Dim2")
]
```
</div>
<div style="clear:both;">
</div>
``` r
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**
<div class="column-left">
TidyTranscriptomics
``` r
counts_SE.norm.non_redundant =
counts_SE.norm.MDS |>
remove_redundancy( method = "correlation" )
```
## Getting the 8513 most variable genes
</div>
<div class="column-right">
Standard procedure (comparative purpose)
``` 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 redudant data frame
counts |> anti_join(.data.correlated) |>
spread(sample, rc, - transcript) |>
left_join(annotation)
```
</div>
<div style="clear:both;">
</div>
We can visualise how the reduced redundancy with the reduced dimentions
look like
``` r
counts_SE.norm.non_redundant |>
pivot_sample() |>
ggplot(aes(x=`Dim1`, y=`Dim2`, color=`Cell.type`)) +
geom_point() +
my_theme
```
<!-- -->
**Approach 2**
``` r
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.
``` r
counts_SE.norm.non_redundant |>
pivot_sample() |>
ggplot(aes(x=`Dim1`, y=`Dim2`, color=`Cell.type`)) +
geom_point() +
my_theme
```
<!-- -->
## 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).
``` r
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.
``` r
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.
``` r
counts_SE |>
describe_transcript() |>
select(feature, description, everything())
```
## Warning in is_sample_feature_deprecated_used(.data, .cols):
## tidySummarizedExperiment says: from version 1.3.1, the special columns
## including sample/feature id (colnames(se), rownames(se)) has changed to
## ".sample" and ".feature". This dataset is returned with the old-style
## vocabulary (feature and sample), however we suggest to update your workflow to
## reflect the new vocabulary (.feature, .sample)
## # 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>
