Brings transcriptomics to the tidyverse!
The code is released under the version 3 of the GNU General Public License.
website: stemangiola.github.io/tidybulk/ Third party tutorials Please have a look also to
- tidySummarizedExperiment for bulk data tidy representation
- tidySingleCellExperiment for single-cell data tidy representation
- tidyseurat for single-cell data tidy representation
- tidyHeatmap for heatmaps produced with tidy principles analysis and manipulation
- tidygate for adding custom gate information to your tibble
| 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.
From Bioconductor
BiocManager::install("tidybulk")From Github
devtools::install_github("stemangiola/tidybulk")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"
First of all, you can cite all articles utilised within your workflow automatically from any tidybulk tibble
counts_SE |> get_bibliography()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$symbol,
function(df)
if(length(df[1,1])>0)
matrixStats:::colSums(as.matrix(df[,-1]))
)
dge_list.nr <- do.call("rbind", dge_list.nr)
colnames(dge_list.nr) <- colnames(dge_list)We may want to compensate for sequencing depth, scaling the transcript
abundance (e.g., with TMM algorithm, Robinson and Oshlack
doi.org/10.1186/gb-2010-11-3-r25). scale_abundance takes a tibble,
column names (as symbols; for sample, transcript and count) and a
method as arguments and returns a tibble with additional columns with
scaled data as <NAME OF COUNT COLUMN>_scaled.
TidyTranscriptomics
counts_SE.norm = counts_SE.aggr |> identify_abundant(factor_of_interest = condition) |> scale_abundance()## tidybulk says: the sample with largest library size SRR1740080 was chosen as reference for scaling
Standard procedure (comparative purpose)
library(edgeR)
dgList <- DGEList(count_m=x,group=group)
keep <- filterByExpr(dgList)
dgList <- dgList[keep,,keep.lib.sizes=FALSE]
[...]
dgList <- calcNormFactors(dgList, method="TMM")
norm_counts.table <- cpm(dgList)We can easily plot the scaled density to check the scaling outcome. On the x axis we have the log scaled counts, on the y axes we have the density, data is grouped by sample and coloured by cell type.
counts_SE.norm |>
ggplot(aes(count_scaled + 1, group=.sample, color=`Cell.type`)) +
geom_density() +
scale_x_log10() +
my_theme
We may want to identify and filter variable transcripts.
TidyTranscriptomics
counts_SE.norm.variable = counts_SE.norm |> keep_variable()## Getting the 500 most variable genes
Standard procedure (comparative purpose)
library(edgeR)
x = norm_counts.table
s <- rowMeans((x-rowMeans(x))^2)
o <- order(s,decreasing=TRUE)
x <- x[o[1L:top],,drop=FALSE]
norm_counts.table = norm_counts.table[rownames(x)]
norm_counts.table$cell_type = tibble_counts[
match(
tibble_counts$sample,
rownames(norm_counts.table)
),
"Cell.type"
]We may want to reduce the dimensions of our data, for example using PCA
or MDS algorithms. reduce_dimensions takes a tibble, column names (as
symbols; for sample, transcript and count) and a method (e.g., MDS
or PCA) as arguments and returns a tibble with additional columns for
the reduced dimensions.
MDS (Robinson et al., 10.1093/bioinformatics/btp616)
TidyTranscriptomics
counts_SE.norm.MDS =
counts_SE.norm |>
reduce_dimensions(method="MDS", .dims = 6)## Getting the 500 most variable genes
## tidybulk says: to access the raw results do `attr(..., "internals")$MDS`
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$sample, rownames(cmds)),
"Cell.type"
]On the x and y axes axis we have the reduced dimensions 1 to 3, data is coloured by cell type.
counts_SE.norm.MDS |> pivot_sample() |> select(contains("Dim"), everything())## # A tibble: 48 × 14
## Dim1 Dim2 Dim3 Dim4 Dim5 Dim6 .sample Cell.type time
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <fct> <fct>
## 1 -1.46 0.220 -1.68 0.0553 0.0658 -0.126 SRR1740034 b_cell 0 d
## 2 -1.46 0.226 -1.71 0.0300 0.0454 -0.137 SRR1740035 b_cell 1 d
## 3 -1.44 0.193 -1.60 0.0890 0.0503 -0.121 SRR1740036 b_cell 3 d
## 4 -1.44 0.198 -1.67 0.0891 0.0543 -0.110 SRR1740037 b_cell 7 d
## 5 0.243 -1.42 0.182 0.00642 -0.503 -0.131 SRR1740038 dendritic_mye… 0 d
## 6 0.191 -1.42 0.195 0.0180 -0.457 -0.130 SRR1740039 dendritic_mye… 1 d
## 7 0.257 -1.42 0.152 0.0130 -0.582 -0.0927 SRR1740040 dendritic_mye… 3 d
## 8 0.162 -1.43 0.189 0.0232 -0.452 -0.109 SRR1740041 dendritic_mye… 7 d
## 9 0.516 -1.47 0.240 -0.251 0.457 -0.119 SRR1740042 monocyte 0 d
## 10 0.514 -1.41 0.231 -0.219 0.458 -0.131 SRR1740043 monocyte 1 d
## # ℹ 38 more rows
## # ℹ 5 more variables: condition <lgl>, batch <fct>, factor_of_interest <lgl>,
## # TMM <dbl>, multiplier <dbl>
counts_SE.norm.MDS |>
pivot_sample() |>
GGally::ggpairs(columns = 6:(6+5), ggplot2::aes(colour=`Cell.type`))## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
PCA
TidyTranscriptomics
counts_SE.norm.PCA =
counts_SE.norm |>
reduce_dimensions(method="PCA", .dims = 6)Standard procedure (comparative purpose)
count_m_log = log(count_m + 1)
pc = count_m_log |> prcomp(scale = TRUE)
variance = pc$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`))
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$sample, rownames(tsne)),
"Cell.type"
]Plot
counts_SE.norm.tSNE |>
pivot_sample() |>
select(contains("tSNE"), everything()) ## # A tibble: 251 × 4
## tSNE1 tSNE2 .sample Call
## <dbl> <dbl> <chr> <fct>
## 1 -4.29 5.40 TCGA-A1-A0SD-01A-11R-A115-07 LumA
## 2 4.48 -2.82 TCGA-A1-A0SF-01A-11R-A144-07 LumA
## 3 -9.06 0.637 TCGA-A1-A0SG-01A-11R-A144-07 LumA
## 4 7.05 7.28 TCGA-A1-A0SH-01A-11R-A084-07 LumA
## 5 -2.38 2.77 TCGA-A1-A0SI-01A-11R-A144-07 LumB
## 6 -1.63 -5.67 TCGA-A1-A0SJ-01A-11R-A084-07 LumA
## 7 18.2 -18.3 TCGA-A1-A0SK-01A-12R-A084-07 Basal
## 8 -9.06 -11.7 TCGA-A1-A0SM-01A-11R-A084-07 LumA
## 9 -8.88 -10.3 TCGA-A1-A0SN-01A-11R-A144-07 LumB
## 10 -8.30 23.4 TCGA-A1-A0SQ-01A-21R-A144-07 LumA
## # ℹ 241 more rows
counts_SE.norm.tSNE |>
pivot_sample() |>
ggplot(aes(x = `tSNE1`, y = `tSNE2`, color=Call)) + geom_point() + my_theme
We may want to rotate the reduced dimensions (or any two numeric columns
really) of our data, of a set angle. rotate_dimensions takes a tibble,
column names (as symbols; for sample, transcript and count) and an
angle as arguments and returns a tibble with additional columns for the
rotated dimensions. The rotated dimensions will be added to the original
data set as <NAME OF DIMENSION> rotated <ANGLE> by default, or as
specified in the input arguments.
TidyTranscriptomics
counts_SE.norm.MDS.rotated =
counts_SE.norm.MDS |>
rotate_dimensions(`Dim1`, `Dim2`, rotation_degrees = 45, action="get")Standard procedure (comparative purpose)
rotation = function(m, d) {
r = d * pi / 180
((bind_rows(
c(`1` = cos(r), `2` = -sin(r)),
c(`1` = sin(r), `2` = cos(r))
) |> as_matrix()) %*% m)
}
mds_r = pca |> rotation(rotation_degrees)
mds_r$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
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
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.deStandard 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"
)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_unwanted = batch, .factor_of_interest = factor_of_interest)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$sample, rownames(count_m_log.sva)),
"Cell.type"
]We may want to infer the cell type composition of our samples (with the
algorithm Cibersort; Newman et al., 10.1038/nmeth.3337).
deconvolve_cellularity takes as arguments a tibble, column names (as
symbols; for sample, transcript and count) and returns a tibble
with additional columns for the adjusted cell type proportions.
TidyTranscriptomics
counts_SE.cibersort =
counts_SE |>
deconvolve_cellularity(action="get", cores=1, prefix = "cibersort__") Standard procedure (comparative purpose)
source(‘CIBERSORT.R’)
count_m |> write.table("mixture_file.txt")
results <- CIBERSORT(
"sig_matrix_file.txt",
"mixture_file.txt",
perm=100, QN=TRUE
)
results$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)## tidySummarizedExperiment says: A data frame is returned for independent data analysis.
We can also perform a statistical test on the differential cell-type abundance across conditions
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).
# 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)
# 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.
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
counts_stratified$plot[[1]]
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$sample, rownames(cluster)),
c("Cell.type", "Dim1", "Dim2")
]We can add cluster annotation to the MDS dimension reduced data set and plot.
counts_SE.norm.cluster |>
ggplot(aes(x=`Dim1`, y=`Dim2`, color=`cluster_kmeans`)) +
geom_point() +
my_theme
SNN
Matrix package (v1.3-3) causes an error with Seurat::FindNeighbors used in this method. We are trying to solve this issue. At the moment this option in unaviable.
TidyTranscriptomics
counts_SE.norm.SNN =
counts_SE.norm.tSNE |>
cluster_elements(method = "SNN")Standard procedure (comparative purpose)
library(Seurat)
snn = CreateSeuratObject(count_m)
snn = ScaleData(
snn, display.progress = TRUE,
num.cores=4, do.par = TRUE
)
snn = FindVariableFeatures(snn, selection.method = "vst")
snn = FindVariableFeatures(snn, selection.method = "vst")
snn = RunPCA(snn, npcs = 30)
snn = FindNeighbors(snn)
snn = FindClusters(snn, method = "igraph", ...)
snn = snn[["seurat_clusters"]]
snn$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"
)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" )## Getting the 8513 most variable genes
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
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
The above wrapper streamline the most common processing of bulk RNA sequencing data. Other useful wrappers are listed above.
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
)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>
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())## 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>