tidybulk - part of tidyTranscriptomics

Brings transcriptomics to the tidyverse! (SEO: tidy differential expression)

website: stemangiola.github.io/tidybulk/

Please have a look also to

tidyseurat for tidy high-level data analysis and manipulation
nanny for tidy high-level data analysis and manipulation
tidygate for adding custom gate information to your tibble
tidyHeatmap for heatmaps produced with tidy principles

IMPORTANT! From version 1.1.6, the detection of abundant/lowly abundant transcripts has to be done explicitly (identify_abundant(); you will be warned otherwise). This because, it is better to make each function do one thing only, without hidden/implicit procedures (as stated in the tidy data manifesto). This improves logical flow, and software modularity.

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

Minimal input data frame

sample	transcript	abundance	annotation
`chr` or `fctr`	`chr` or `fctr`	`integer`	…

Output data frame

sample	transcript	abundance	annotation	new information
`chr` or `fctr`	`chr` or `fctr`	`integer`	…	…

All functions are also directly compatible with SummarizedExperiment object.

Installation

From Bioconductor

BiocManager::install("tidybulk")

From Github

devtools::install_github("stemangiola/tidybulk")

Create `tidybulk` tibble.

It memorises key column names

tt = counts %>% tidybulk(sample, transcript, count)

Get the bibliography of your workflow

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

tt %>%
    # call analysis functions
    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

tt.aggr = tt %>% aggregate_duplicates()

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

tt.norm = tt.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.

tt.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.

TidyTranscriptomics

tt.norm.variable = tt.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$cell_type = tidybulk::counts[
    match(
        tidybulk::counts$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

tt.norm.MDS =
  tt.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$cell_type = tidybulk::counts[
    match(tidybulk::counts$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.

tt.norm.MDS %>% pivot_sample()  %>% select(contains("Dim"), everything())

## # A tibble: 48 x 15
##     Dim1  Dim2   Dim3    Dim4    Dim5   Dim6 sample `Cell type` time  condition
##    <dbl> <dbl>  <dbl>   <dbl>   <dbl>  <dbl> <chr>  <chr>       <chr> <chr>    
##  1  2.31  1.87 -2.73   0.108   0.0918 -0.515 SRR17… b_cell      0 d   TRUE     
##  2  2.30  1.88 -2.77   0.119   0.116  -0.490 SRR17… b_cell      1 d   TRUE     
##  3  2.27  1.83 -2.73   0.169   0.121  -0.481 SRR17… b_cell      3 d   TRUE     
##  4  2.29  1.87 -2.78   0.166   0.0763 -0.471 SRR17… b_cell      7 d   TRUE     
##  5 -1.50 -1.79 -1.01  -0.171  -1.25   -0.516 SRR17… dendritic_… 0 d   FALSE    
##  6 -1.45 -1.87 -0.925 -0.0722 -1.15   -0.534 SRR17… dendritic_… 1 d   FALSE    
##  7 -1.46 -1.79 -0.989 -0.230  -1.42   -0.503 SRR17… dendritic_… 3 d   FALSE    
##  8 -1.41 -1.88 -0.981 -0.123  -1.23   -0.511 SRR17… dendritic_… 7 d   FALSE    
##  9 -2.07 -1.71 -0.936 -0.445   1.16   -0.519 SRR17… monocyte    0 d   FALSE    
## 10 -1.88 -1.69 -0.788 -0.561   1.15   -0.512 SRR17… monocyte    1 d   FALSE    
## # … with 38 more rows, and 5 more variables: batch <dbl>,
## #   factor_of_interest <chr>, `merged transcripts` <dbl>, TMM <dbl>,
## #   multiplier <dbl>

tt.norm.MDS %>%
    pivot_sample() %>%
  GGally::ggpairs(columns = 10:15, ggplot2::aes(colour=`Cell type`))

PCA

TidyTranscriptomics

tt.norm.PCA =
  tt.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.

tt.norm.PCA %>% pivot_sample() %>% select(contains("PC"), everything())

## # A tibble: 48 x 15
##      PC1      PC2    PC3     PC4     PC5   PC6 sample `Cell type` time 
##    <dbl>    <dbl>  <dbl>   <dbl>   <dbl> <dbl> <chr>  <chr>       <chr>
##  1 -17.3 -20.4    -0.745 -0.205   0.0756 -3.05 SRR17… b_cell      0 d  
##  2 -17.4 -20.6    -0.580 -0.558  -0.139  -2.91 SRR17… b_cell      1 d  
##  3 -17.1 -20.0    -0.345 -1.08   -0.220  -3.02 SRR17… b_cell      3 d  
##  4 -17.4 -20.7    -0.484 -0.713  -0.0373 -3.01 SRR17… b_cell      7 d  
##  5  12.1  -0.778  10.8    0.537   5.09   -2.17 SRR17… dendritic_… 0 d  
##  6  11.8   0.0561 11.0    0.0632  4.77   -2.13 SRR17… dendritic_… 1 d  
##  7  11.8  -0.652  10.7    0.808   5.81   -1.94 SRR17… dendritic_… 3 d  
##  8  11.5  -0.565  11.1    0.0806  4.86   -2.22 SRR17… dendritic_… 7 d  
##  9  16.7  -1.20    8.18   2.38   -4.28   -2.85 SRR17… monocyte    0 d  
## 10  15.1  -0.247   8.00   2.68   -4.40   -3.17 SRR17… monocyte    1 d  
## # … with 38 more rows, and 6 more variables: condition <chr>, batch <dbl>,
## #   factor_of_interest <chr>, `merged transcripts` <dbl>, TMM <dbl>,
## #   multiplier <dbl>

tt.norm.PCA %>%
     pivot_sample() %>%
  GGally::ggpairs(columns = 10:12, ggplot2::aes(colour=`Cell type`))

tSNE

TidyTranscriptomics

tt.norm.tSNE =
    breast_tcga_mini %>%
    tidybulk(       sample, ens, count_scaled) %>%
    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 = tidybulk::counts[
    match(tidybulk::counts$sample, rownames(tsne)),
    "Cell type"
]

Plot

tt.norm.tSNE %>%
    pivot_sample() %>%
    select(contains("tSNE"), everything())

## # A tibble: 251 x 4
##      tSNE1  tSNE2 sample                       Call 
##      <dbl>  <dbl> <chr>                        <fct>
##  1   9.36    7.99 TCGA-A1-A0SD-01A-11R-A115-07 LumA 
##  2  -9.20    2.81 TCGA-A1-A0SF-01A-11R-A144-07 LumA 
##  3  14.6    16.0  TCGA-A1-A0SG-01A-11R-A144-07 LumA 
##  4   5.10   -9.53 TCGA-A1-A0SH-01A-11R-A084-07 LumA 
##  5   6.18    6.32 TCGA-A1-A0SI-01A-11R-A144-07 LumB 
##  6  -0.550  13.1  TCGA-A1-A0SJ-01A-11R-A084-07 LumA 
##  7 -37.6    -4.31 TCGA-A1-A0SK-01A-12R-A084-07 Basal
##  8  -2.72  -17.1  TCGA-A1-A0SM-01A-11R-A084-07 LumA 
##  9  -2.66  -15.1  TCGA-A1-A0SN-01A-11R-A144-07 LumB 
## 10  21.8    12.9  TCGA-A1-A0SQ-01A-21R-A144-07 LumA 
## # … with 241 more rows

tt.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.

TidyTranscriptomics

tt.norm.MDS.rotated =
  tt.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.

tt.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.

tt.norm.MDS.rotated %>%
    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).

TidyTranscriptomics

tt.de =
    tt %>%
    test_differential_abundance( ~ condition, action="get")
tt.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>)

tt.de =
    tt %>%
    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

tt.norm.adj =
    tt.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

tt.cibersort =
    tt %>%
    deconvolve_cellularity(action="get", cores=1)

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$cell_type = tidybulk::counts[
    match(tidybulk::counts$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).

tt.cibersort %>%
    select(contains("cibersort:"), everything()) %>%
    gather(`Cell type inferred`, `proportion`, 1:22) %>%
  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

    tt %>%
    test_differential_cellularity( ~ condition )

## # A tibble: 22 x 7
##    .cell_type cell_type_propo… `estimate_(Inte… estimate_condit…
##    <chr>      <list>                      <dbl>            <dbl>
##  1 B cells n… <tibble [48 × 9…            -3.38           3.17  
##  2 B cells m… <tibble [48 × 9…            -2.98           2.83  
##  3 Plasma ce… <tibble [48 × 9…            -7.09          -0.508 
##  4 T cells C… <tibble [48 × 9…            -4.05          -0.445 
##  5 T cells C… <tibble [48 × 9…            -1.93           0.0167
##  6 T cells C… <tibble [48 × 9…            -3.07          -0.533 
##  7 T cells C… <tibble [48 × 9…            -5.97           1.46  
##  8 T cells f… <tibble [48 × 9…            -5.17          -0.469 
##  9 T cells r… <tibble [48 × 9…            -5.62          -0.602 
## 10 T cells g… <tibble [48 × 9…            -5.53           1.02  
## # … with 12 more rows, and 3 more variables: std.error_conditionTRUE <dbl>,
## #   statistic_conditionTRUE <dbl>, p.value_conditionTRUE <dbl>

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

    tt %>%
    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

tt.norm.cluster = tt.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$cell_type = tidybulk::counts[
    match(tidybulk::counts$sample, rownames(cluster)),
    c("Cell type", "Dim1", "Dim2")
]

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

 tt.norm.cluster %>%
    ggplot(aes(x=`Dim1`, y=`Dim2`, color=`cluster kmeans`)) +
  geom_point() +
  my_theme

SNN

TidyTranscriptomics

tt.norm.SNN =
    tt.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$cell_type = tidybulk::counts[
    match(tidybulk::counts$sample, rownames(snn)),
    c("Cell type", "Dim1", "Dim2")
]

tt.norm.SNN %>%
    pivot_sample() %>%
    select(contains("tSNE"), everything())

## # A tibble: 251 x 5
##      tSNE1  tSNE2 sample                       Call  `cluster SNN`
##      <dbl>  <dbl> <chr>                        <fct> <fct>        
##  1   9.36    7.99 TCGA-A1-A0SD-01A-11R-A115-07 LumA  0            
##  2  -9.20    2.81 TCGA-A1-A0SF-01A-11R-A144-07 LumA  2            
##  3  14.6    16.0  TCGA-A1-A0SG-01A-11R-A144-07 LumA  1            
##  4   5.10   -9.53 TCGA-A1-A0SH-01A-11R-A084-07 LumA  0            
##  5   6.18    6.32 TCGA-A1-A0SI-01A-11R-A144-07 LumB  0            
##  6  -0.550  13.1  TCGA-A1-A0SJ-01A-11R-A084-07 LumA  1            
##  7 -37.6    -4.31 TCGA-A1-A0SK-01A-12R-A084-07 Basal 3            
##  8  -2.72  -17.1  TCGA-A1-A0SM-01A-11R-A084-07 LumA  2            
##  9  -2.66  -15.1  TCGA-A1-A0SN-01A-11R-A144-07 LumB  2            
## 10  21.8    12.9  TCGA-A1-A0SQ-01A-21R-A144-07 LumA  1            
## # … with 241 more rows

tt.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
tt.norm.SNN %>%
    mutate(factor_of_interest = `cluster SNN` == 3) %>%
    test_differential_abundance(
    ~ factor_of_interest,
    action="get"
   )

## # A tibble: 500 x 7
##    ens               logFC logCPM      F   PValue      FDR significant
##    <chr>             <dbl>  <dbl>  <dbl>    <dbl>    <dbl> <lgl>      
##  1 ENSG00000002834 -0.981   10.7  42.9   3.13e-10 8.86e-10 TRUE       
##  2 ENSG00000003989 -3.54     9.92 81.9   3.99e-17 2.10e-16 TRUE       
##  3 ENSG00000004478 -0.154   10.6   1.20  2.74e- 1 2.96e- 1 FALSE      
##  4 ENSG00000006125 -0.669   10.7  30.5   8.14e- 8 1.75e- 7 TRUE       
##  5 ENSG00000008988  0.826   11.3  78.4   1.49e-16 7.45e-16 TRUE       
##  6 ENSG00000009307  0.412   11.3  32.1   3.92e- 8 8.72e- 8 TRUE       
##  7 ENSG00000010278  0.0782  10.5   0.569 4.51e- 1 4.73e- 1 FALSE      
##  8 ENSG00000011465 -1.13    11.1  34.5   1.33e- 8 3.19e- 8 TRUE       
##  9 ENSG00000012223  0.238   11.8   0.256 6.13e- 1 6.31e- 1 FALSE      
## 10 ENSG00000012660 -1.68    10.5  92.0   8.66e-19 5.15e-18 TRUE       
## # … with 490 more rows

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

tt.norm.non_redundant =
    tt.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

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

Approach 2

tt.norm.non_redundant =
    tt.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.

tt.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).

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 x 8
##    ens   iso   `read count` sample cases_0_project… cases_0_samples… transcript
##    <chr> <chr>        <dbl> <chr>  <chr>            <chr>            <chr>     
##  1 ENSG… 13             144 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
##  2 ENSG… 13              72 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
##  3 ENSG… 13               0 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
##  4 ENSG… 13            1099 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
##  5 ENSG… 13              11 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
##  6 ENSG… 13               2 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
##  7 ENSG… 13               3 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
##  8 ENSG… 13            2678 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
##  9 ENSG… 13             751 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
## 10 ENSG… 13               1 TARGE… Acute Myeloid L… Primary Blood D… TSPAN6    
## # … with 109 more rows, and 1 more variable: 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.

tt %>% describe_transcript() %>% select(transcript, description, everything())

## # A tibble: 938,112 x 9
##    transcript description sample `Cell type` count time  condition batch
##    <chr>      <chr>       <fct>  <fct>       <dbl> <fct> <lgl>     <int>
##  1 DDX11L1    DEAD/H-box… SRR17… b_cell         17 0 d   TRUE          0
##  2 WASH7P     WASP famil… SRR17… b_cell       3568 0 d   TRUE          0
##  3 MIR6859-1  microRNA 6… SRR17… b_cell         57 0 d   TRUE          0
##  4 MIR1302-2  microRNA 1… SRR17… b_cell          1 0 d   TRUE          0
##  5 FAM138A    family wit… SRR17… b_cell          0 0 d   TRUE          0
##  6 OR4F5      olfactory … SRR17… b_cell          0 0 d   TRUE          0
##  7 LOC729737  uncharacte… SRR17… b_cell       1764 0 d   TRUE          0
##  8 LOC102725… DEAD/H-box… SRR17… b_cell         11 0 d   TRUE          0
##  9 MIR6859-2  microRNA 6… SRR17… b_cell         40 0 d   TRUE          0
## 10 OR4F29     olfactory … SRR17… b_cell          0 0 d   TRUE          0
## # … with 938,102 more rows, and 1 more variable: factor_of_interest <lgl>

ADD versus GET versus ONLY modes

Every function takes a tidytranscriptomics structured data as input, and (i) with action=“add” outputs the new information joint to the original input data frame (default), (ii) with action=“get” the new information with the sample or transcript relative informatin depending on what the analysis is about, or (iii) with action=“only” just the new information. For example, from this data set

  tt.norm

## # A tibble: 938,112 x 13
##    sample transcript `Cell type` count time  condition batch factor_of_inter…
##    <chr>  <chr>      <chr>       <dbl> <chr> <chr>     <dbl> <chr>           
##  1 SRR17… DDX11L1    b_cell         17 0 d   TRUE          0 TRUE            
##  2 SRR17… WASH7P     b_cell       3568 0 d   TRUE          0 TRUE            
##  3 SRR17… MIR6859-1  b_cell         57 0 d   TRUE          0 TRUE            
##  4 SRR17… MIR1302-2  b_cell          1 0 d   TRUE          0 TRUE            
##  5 SRR17… FAM138A    b_cell          0 0 d   TRUE          0 TRUE            
##  6 SRR17… OR4F5      b_cell          0 0 d   TRUE          0 TRUE            
##  7 SRR17… LOC729737  b_cell       1764 0 d   TRUE          0 TRUE            
##  8 SRR17… LOC102725… b_cell         11 0 d   TRUE          0 TRUE            
##  9 SRR17… MIR6859-2  b_cell         40 0 d   TRUE          0 TRUE            
## 10 SRR17… OR4F29     b_cell          0 0 d   TRUE          0 TRUE            
## # … with 938,102 more rows, and 5 more variables: `merged transcripts` <dbl>,
## #   .abundant <lgl>, TMM <dbl>, multiplier <dbl>, count_scaled <dbl>

action=“add” (Default) We can add the MDS dimensions to the original data set

  tt.norm %>%
    reduce_dimensions(
        .abundance = count_scaled,
        method="MDS" ,
        .element = sample,
        .feature = transcript,
        .dims = 3,
        action="add"
    )

## # A tibble: 938,112 x 16
##    sample transcript `Cell type` count time  condition batch factor_of_inter…
##    <chr>  <chr>      <chr>       <dbl> <chr> <chr>     <dbl> <chr>           
##  1 SRR17… DDX11L1    b_cell         17 0 d   TRUE          0 TRUE            
##  2 SRR17… WASH7P     b_cell       3568 0 d   TRUE          0 TRUE            
##  3 SRR17… MIR6859-1  b_cell         57 0 d   TRUE          0 TRUE            
##  4 SRR17… MIR1302-2  b_cell          1 0 d   TRUE          0 TRUE            
##  5 SRR17… FAM138A    b_cell          0 0 d   TRUE          0 TRUE            
##  6 SRR17… OR4F5      b_cell          0 0 d   TRUE          0 TRUE            
##  7 SRR17… LOC729737  b_cell       1764 0 d   TRUE          0 TRUE            
##  8 SRR17… LOC102725… b_cell         11 0 d   TRUE          0 TRUE            
##  9 SRR17… MIR6859-2  b_cell         40 0 d   TRUE          0 TRUE            
## 10 SRR17… OR4F29     b_cell          0 0 d   TRUE          0 TRUE            
## # … with 938,102 more rows, and 8 more variables: `merged transcripts` <dbl>,
## #   .abundant <lgl>, TMM <dbl>, multiplier <dbl>, count_scaled <dbl>,
## #   Dim1 <dbl>, Dim2 <dbl>, Dim3 <dbl>

action=“get” We can add the MDS dimensions to the original data set selecting just the sample-wise column

  tt.norm %>%
    reduce_dimensions(
        .abundance = count_scaled,
        method="MDS" ,
        .element = sample,
        .feature = transcript,
        .dims = 3,
        action="get"
    )

## # A tibble: 48 x 12
##    sample `Cell type` time  condition batch factor_of_inter… `merged transcr…
##    <chr>  <chr>       <chr> <chr>     <dbl> <chr>                       <dbl>
##  1 SRR17… b_cell      0 d   TRUE          0 TRUE                            1
##  2 SRR17… b_cell      1 d   TRUE          1 TRUE                            1
##  3 SRR17… b_cell      3 d   TRUE          1 TRUE                            1
##  4 SRR17… b_cell      7 d   TRUE          1 TRUE                            1
##  5 SRR17… dendritic_… 0 d   FALSE         0 FALSE                           1
##  6 SRR17… dendritic_… 1 d   FALSE         0 FALSE                           1
##  7 SRR17… dendritic_… 3 d   FALSE         1 FALSE                           1
##  8 SRR17… dendritic_… 7 d   FALSE         0 FALSE                           1
##  9 SRR17… monocyte    0 d   FALSE         1 FALSE                           1
## 10 SRR17… monocyte    1 d   FALSE         1 FALSE                           1
## # … with 38 more rows, and 5 more variables: TMM <dbl>, multiplier <dbl>,
## #   Dim1 <dbl>, Dim2 <dbl>, Dim3 <dbl>

action=“only” We can get just the MDS dimensions relative to each sample

  tt.norm %>%
    reduce_dimensions(
        .abundance = count_scaled,
        method="MDS" ,
        .element = sample,
        .feature = transcript,
        .dims = 3,
        action="only"
    )

## # A tibble: 48 x 4
##    sample      Dim1  Dim2   Dim3
##    <chr>      <dbl> <dbl>  <dbl>
##  1 SRR1740034  2.31  1.87 -2.73 
##  2 SRR1740035  2.30  1.88 -2.77 
##  3 SRR1740036  2.27  1.83 -2.73 
##  4 SRR1740037  2.29  1.87 -2.78 
##  5 SRR1740038 -1.50 -1.79 -1.01 
##  6 SRR1740039 -1.45 -1.87 -0.925
##  7 SRR1740040 -1.46 -1.79 -0.989
##  8 SRR1740041 -1.41 -1.88 -0.981
##  9 SRR1740042 -2.07 -1.71 -0.936
## 10 SRR1740043 -1.88 -1.69 -0.788
## # … with 38 more rows

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tidybulk's Introduction

tidybulk - part of tidyTranscriptomics

Functions/utilities available

Minimal input data frame

Output data frame

Installation

Create tidybulk tibble.

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)

ADD versus GET versus ONLY modes

tidybulk's People

Contributors

Recommend Projects

Recommend Topics

Recommend Org

Create `tidybulk` tibble.

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