In stemangiola/rladiestunis2021_tidytranscriptomics: Introduction to Tidy Transcriptomics

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Workshop introduction

tidybulk, tidySummarizedExperiment and tidySingleCellExperiment are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data representation and analysis. The roadmap for the development of the tidytranscriptomics packages is shown in the figure below.

knitr::include_graphics("../inst/vignettes/roadmap.png")

Part 1 Bulk RNA-seq with tidySummarizedExperiment and tidybulk

knitr::include_graphics("../inst/vignettes/tidybulk_logo.png")

Acknowledgements

Some of the material in Part 1 was adapted from an R for RNA sequencing workshop first run here. Use of the airway dataset was inspired by the DESeq2 vignette.

Introduction

Measuring gene expression on a genome-wide scale has become common practice over the last two decades or so, with microarrays predominantly used pre-2008. With the advent of next generation sequencing technology in 2008, an increasing number of scientists use this technology to measure and understand changes in gene expression in often complex systems. As sequencing costs have decreased, using RNA sequencing to simultaneously measure the expression of tens of thousands of genes for multiple samples has never been easier. The cost of these experiments has now moved from generating the data to storing and analysing it.

There are many steps involved in analysing an RNA sequencing dataset. Sequenced reads are aligned to a reference genome, then the number of reads mapped to each gene can be counted. This results in a table of counts, which is what we perform statistical analyses on in R. While mapping and counting are important and necessary tasks, today we will be starting from the count data and showing how differential expression analysis can be performed in a friendly way using the Bioconductor package, tidybulk.

First, let’s load all the packages we will need to analyse the data.

Note: you should load the tidybulk and tidySummarizedExperiment libraries after the tidyverse core packages for best integration.

# dataset
library(airway)

# tidyverse core packages
library(tibble)
library(dplyr)
library(tidyr)
library(readr)
library(stringr)
library(ggplot2)
library(purrr)

# tidyverse-friendly packages
library(plotly)
library(ggrepel)
library(GGally)
library(tidyHeatmap)
library(tidybulk)
# library(tidySummarizedExperiment) # we'll load this below to show what it can do

Plot settings. Set the colours and theme we will use for our plots.

# Use colourblind-friendly colours
friendly_cols <- dittoSeq::dittoColors()

# Set theme
custom_theme <-
  list(
    scale_fill_manual(values = friendly_cols),
    scale_color_manual(values = friendly_cols),
    theme_bw() +
      theme(
        panel.border = element_blank(),
        axis.line = element_line(),
        panel.grid.major = element_line(size = 0.2),
        panel.grid.minor = element_line(size = 0.1),
        text = element_text(size = 12),
        legend.position = "bottom",
        strip.background = element_blank(),
        axis.title.x = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10)),
        axis.title.y = element_text(margin = margin(t = 10, r = 10, b = 10, l = 10)),
        axis.text.x = element_text(angle = 30, hjust = 1, vjust = 1)
      )
  )

tidySummarizedExperiment

Here we will perform our analysis using the data from the airway package. The airway data comes from the paper by [@himes2014rna]; and it includes 8 samples from human airway smooth muscle cells, from 4 cell lines. For each cell line treated (with dexamethasone) and untreated (negative control) a sample has undergone RNA sequencing and gene counts have been generated.

# load airway RNA sequencing data
data(airway)

# take a look
airway

The data in the airway package is a Bioconductor SummarizedExperiment object. For more information see here.

The tidySummarizedExperiment package enables any SummarizedExperiment object to be displayed and manipulated according to tidy data principles, without affecting any SummarizedExperiment behaviour.

If we load the tidySummarizedExperiment package and then view the airway data it now displays as a tibble. A tibble is the tidyverse table format.

# load tidySummarizedExperiment package
library(tidySummarizedExperiment)

# take a look
airway

Now we can more easily see the data. The airway object contains information about genes and samples, the first column has the Ensembl gene identifier, the second column has the sample identifier and the third column has the gene transcription abundance. The abundance is the number of reads aligning to the gene in each experimental sample. The remaining columns include sample information. The dex column tells us whether the samples are treated or untreated and the cell column tells us what cell line they are from.

We can still interact with the tidy SummarizedExperiment object using commands for SummarizedExperiment objects.

assays(airway)

Tidyverse commands

And now we can also use tidyverse commands, such as filter, select and mutate to explore the tidy SummarizedExperiment object. Some examples are shown below and more can be seen at the tidySummarizedExperiment website here. We can also use the tidyverse pipe %>%. This 'pipes' the output from the command on the left into the command on the right/below. Using the pipe is not essential but it reduces the amount of code we need to write when we have multiple steps. It also can make the steps clearer and easier to see. For more details on the pipe see here.

We can use filter to choose rows, for example, to see just the rows for the treated samples.

airway %>% filter(dex == "trt")

We can use select to choose columns, for example, to see the sample, cell line and treatment columns.

airway %>% select(sample, cell, dex)

We can use mutate to create a column. For example, we could create a new sample_name column that contains shorter sample names. We can remove the SRR1039 prefix that's present in all of the samples, as shorter names can fit better in some of the plots we will create. We can use mutate together with str_replace to remove the SRR1039 string from the sample column.

airway %>%
  mutate(sample_name=str_remove(sample, "SRR1039")) %>%
  # select columns to view    
  select(sample, sample_name)

Setting up the data

We'll set up the airway data for our RNA-seq analysis. We'll create the column with shorter sample names and a column with gene symbols. We can get the gene symbols for these Ensembl gene ids using the Bioconductor annotation package for human, org.Hs.eg.db.

# setup data workflow
counts <-
  airway %>%
  mutate(sample_name = str_remove(sample, "SRR1039")) %>%
  mutate(symbol = AnnotationDbi::mapIds(org.Hs.eg.db::org.Hs.eg.db,
    keys = feature,
    keytype = "ENSEMBL",
    column = "SYMBOL",
    multiVals = "first"
  ))
# take a look
counts

Filtering lowly transcribed genes

Genes with very low counts across all libraries provide little evidence for differential expression and they can interfere with some of the statistical approximations that are used later in the pipeline. They also add to the multiple testing burden when estimating false discovery rates, reducing power to detect differentially expressed genes. These genes should be filtered out prior to further analysis.

We can perform the filtering using tidybulk keep_abundant or identify_abundant. These functions can use the edgeR filterByExpr function described in [@law2016rna] to automatically identify the genes with adequate abundance for differential expression testing. By default, this will keep genes with ~10 counts in a minimum number of samples, the number of the samples in the smallest group. In this dataset the smallest group size is four (as we have four dex-treated samples versus four untreated). Alternatively, we could use identify_abundant to identify which genes are abundant or not (TRUE/FALSE), rather than just keeping the abundant ones.

# Filtering counts
counts_filtered <- counts %>% keep_abundant(factor_of_interest = dex)

# take a look
counts_filtered

After running keep_abundant we have a column called .abundant containing TRUE (identify_abundant would have TRUE/FALSE).

Scaling counts to normalise

Scaling of counts, normalisation, is performed to eliminate uninteresting differences between samples due to sequencing depth or composition. A more detailed explanation can be found here. In the tidybulk package the function scale_abundance generates scaled counts, with scaling factors calculated on abundant (filtered) transcripts and applied to all transcripts. We can choose from different normalisation methods. Here we will use the default, edgeR's trimmed mean of M values (TMM), [@robinson2010scaling]. TMM normalisation (and most scaling normalisation methods) scale relative to one sample.

# Scaling counts
counts_scaled <- counts_filtered %>% scale_abundance()

# take a look
counts_scaled

After we run scale_abundance we should see some columns have been added at the end. The counts_scaled column contains the scaled counts.

We can visualise the difference of abundance densities before and after scaling. As tidybulk output is compatible with tidyverse, we can simply pipe it into standard tidyverse functions such as filter, pivot_longer and ggplot. We can also take advantage of ggplot's facet_wrap to easily create multiple plots.

counts_scaled %>%

  # Reshaping
  pivot_longer(cols = c("counts", "counts_scaled"), names_to = "source", values_to = "abundance") %>%

  # Plotting
  ggplot(aes(x = abundance + 1, color = sample_name)) +
  geom_density() +
  facet_wrap(~source) +
  scale_x_log10() +
  custom_theme

In this dataset the distributions of the counts are not very different to each other before scaling but scaling does make the distributions more similar. If we saw a sample with a very different distribution we may need to investigate it.

As tidybulk smoothly integrates with ggplot2 and other tidyverse packages it can save on typing and make plots easier to generate. Compare the code for creating density plots with tidybulk versus standard base R below (standard code adapted from [@law2016rna]).

tidybulk

# tidybulk
airway %>%
  keep_abundant(factor_of_interest = dex) %>%
  scale_abundance() %>%
  pivot_longer(cols = c("counts", "counts_scaled"), names_to = "source", values_to = "abundance") %>%
  ggplot(aes(x = abundance + 1, color = sample)) +
  geom_density() +
  facet_wrap(~source) +
  scale_x_log10() +
  custom_theme

base R using edgeR

# Example code, no need to run

# Prepare data set
library(edgeR)
dgList <- SE2DGEList(airway)
group <- factor(dgList$samples$dex)
keep.exprs <- filterByExpr(dgList, group = group)
dgList <- dgList[keep.exprs, , keep.lib.sizes = FALSE]
nsamples <- ncol(dgList)
logcounts <- log2(dgList$counts)

# Setup graphics
col <- RColorBrewer::brewer.pal(nsamples, "Paired")
par(mfrow = c(1, 2))

# Plot raw counts
plot(density(logcounts[, 1]), col = col[1], lwd = 2, ylim = c(0, 0.26), las = 2, main = "", xlab = "")
title(main = "Counts")
for (i in 2:nsamples) {
  den <- density(logcounts[, i])
  lines(den$x, den$y, col = col[i], lwd = 2)
}
legend("topright", legend = dgList$samples$Run, text.col = col, bty = "n")

# Plot scaled counts
dgList_norm <- calcNormFactors(dgList)
lcpm_n <- cpm(dgList_norm, log = TRUE)
plot(density(lcpm_n[, 1]), col = col[1], lwd = 2, ylim = c(0, 0.26), las = 2, main = "", xlab = "")
title("Counts scaled")
for (i in 2:nsamples) {
  den <- density(lcpm_n[, i])
  lines(den$x, den$y, col = col[i], lwd = 2)
}
legend("topright", legend = dgList_norm$samples$Run, text.col = col, bty = "n")

Exploratory analyses

Dimensionality reduction

By far, one of the most important plots we make when we analyse RNA sequencing data are principal-component analysis (PCA) or multi-dimensional scaling (MDS) plots. We reduce the dimensions of the data to identify the greatest sources of variation in the data. A principal components analysis is an example of an unsupervised analysis, where we don't need to specify the groups. If your experiment is well controlled and has worked well, what we hope to see is that the greatest sources of variation in the data are the treatments/groups we are interested in. It is also an incredibly useful tool for quality control and checking for outliers. We can use the reduce_dimensions function to calculate the dimensions.

# Get principal components
counts_scal_PCA <-
  counts_scaled %>%
  reduce_dimensions(method = "PCA")

This joins the result to the counts object.

# Take a look
counts_scal_PCA

For plotting, we can select just the sample-wise information with pivot_sample.

# take a look
counts_scal_PCA %>% pivot_sample()

We can now plot the reduced dimensions.

# PCA plot
counts_scal_PCA %>%
  pivot_sample() %>%
  ggplot(aes(x = PC1, y = PC2, colour = dex, shape = cell)) +
  geom_point() +
  geom_text_repel(aes(label = sample_name), show.legend = FALSE) +
  custom_theme

The samples separate by treatment on PC1 which is what we hope to see. PC2 separates the N080611 cell line from the other samples, indicating a greater difference between that cell line and the others.

Hierarchical clustering with heatmaps

An alternative to principal component analysis for examining relationships between samples is using hierarchical clustering. Heatmaps are a nice visualisation to examine hierarchical clustering of your samples. tidybulk has a simple function we can use, keep_variable, to extract the most variable genes which we can then plot with tidyHeatmap.

counts_scal_PCA %>%

  # extract 500 most variable genes
  keep_variable(.abundance = counts_scaled, top = 500) %>%
  as_tibble() %>%

  # create heatmap
  heatmap(
    .column = sample_name,
    .row = feature,
    .value = counts_scaled,
    transform = log1p
  ) %>%
  add_tile(dex) %>%
  add_tile(cell)

In the heatmap we can see the samples cluster into two groups, treated and untreated, for three of the cell lines, and the cell line (N080611) again is further away from the others.

Tidybulk enables a simplified way of generating a clustered heatmap of variable genes. Compare the code below for tidybulk versus a base R method.

base R using edgeR

# Example code, no need to run

library(edgeR)
dgList <- SE2DGEList(airway)
group <- factor(dgList$samples$dex)
keep.exprs <- filterByExpr(dgList, group = group)
dgList <- dgList[keep.exprs, , keep.lib.sizes = FALSE]
dgList <- calcNormFactors(dgList)
logcounts <- cpm(dgList, log = TRUE)
var_genes <- apply(logcounts, 1, var)
select_var <- names(sort(var_genes, decreasing = TRUE))[1:500]
highly_variable_lcpm <- logcounts[select_var, ]
colours <- c("#440154FF", "#21908CFF", "#fefada")
col.group <- c("red", "grey")[group]
gplots::heatmap.2(highly_variable_lcpm, col = colours, trace = "none", ColSideColors = col.group, scale = "row")

Differential expression

tidybulk integrates several popular methods for differential transcript abundance testing: the edgeR quasi-likelihood [@chen2016reads] (tidybulk default method), edgeR likelihood ratio [@mccarthy2012differential], limma-voom [@law2014voom] and DESeq2 [@love2014moderated]. A common question researchers have is which method to choose. With tidybulk we can easily run multiple methods and compare.

We give test_differential_abundance our tidybulk counts object and a formula, specifying the column that contains our groups to be compared. If all our samples were from the same cell line, and there were no additional factors contributing variance such as batch differences, we could use the formula ~ dex. However, each treated and untreated sample is from a different cell line so we add the cell line as an additional factor ~ dex + cell.

de_all <-

  counts_scal_PCA %>%

  # edgeR QLT
  test_differential_abundance(
    ~ dex + cell,
    method = "edgeR_quasi_likelihood",
    prefix = "edgerQLT_"
  ) %>%

  # edgeR LRT
  test_differential_abundance(
    ~ dex + cell,
    method = "edgeR_likelihood_ratio",
    prefix = "edgerLR_"
  ) %>%

  # limma-voom
  test_differential_abundance(
    ~ dex + cell,
    method = "limma_voom",
    prefix = "voom_"
  ) %>%

  # DESeq2
  test_differential_abundance(
    ~ dex + cell,
    method = "deseq2",
    prefix = "deseq2_"
  )

# take a look

de_all

This outputs the columns from each method such as log-fold change (logFC), false-discovery rate (FDR) and probability value (p-value). logFC is log2(treated/untreated).

Comparison of methods

We can visually compare the significance for all methods. We will notice that there is some difference between the methods.

de_all %>%
  pivot_transcript() %>%
  select(edgerQLT_PValue, edgerLR_PValue, voom_P.Value, deseq2_pvalue, transcript) %>%
  ggpairs(1:4)

Single method

If we just wanted to run one differential testing method we could do that. The default method is edgeR quasi-likelihood.

counts_de <- counts_scal_PCA %>%
  test_differential_abundance(~ dex + cell)

Tidybulk enables a simplified way of performing an RNA sequencing differential expression analysis (with the benefit of smoothly integrating with ggplot2 and other tidyverse packages). Compare the code for a tidybulk edgeR analysis versus standard edgeR below.

standard edgeR

# Example code, no need to run

library(edgeR)
dgList <- SE2DGEList(airway)
group <- factor(dgList$samples$dex)
keep.exprs <- filterByExpr(dgList, group = group)
dgList <- dgList[keep.exprs, , keep.lib.sizes = FALSE]
dgList <- calcNormFactors(dgList)
cell <- factor(dgList$samples$cell)
design <- model.matrix(~ group + cell)
dgList <- estimateDisp(dgList, design)
fit <- glmQLFit(dgList, design)
qlf <- glmQLFTest(fit, coef=2)

Volcano plots

Volcano plots are a useful genome-wide plot for checking that the analysis looks good. Volcano plots enable us to visualise the significance of change (p-value) versus the fold change (logFC). Highly significant genes are towards the top of the plot. We can also colour significant genes e.g. genes with false-discovery rate < 0.05. In order to decide which genes are differentially expressed, we usually take a cut-off of 0.05 on the FDR (or adjusted P value), NOT the raw p-value. This is because we are testing many genes (multiple testing), and the chances of finding differentially expressed genes is very high when you do that many tests. Hence we need to control the false discovery rate, which is the adjusted p-value column in the results table. What this means is that if 100 genes are significant at a 5% false discovery rate, we are willing to accept that 5 will be false positives.

# volcano plot, minimal
counts_de %>%
  ggplot(aes(x = logFC, y = PValue, colour = FDR < 0.05)) +
  geom_point() +
  scale_y_continuous(trans = "log10_reverse") +
  custom_theme

We'll extract the symbols for a few top genes (by P value) to use in a more informative volcano plot, integrating some of the packages in tidyverse.

topgenes_symbols <-
  counts_de %>%
  pivot_transcript() %>%
  arrange(PValue) %>%
  head(6) %>%
  pull(symbol)

topgenes_symbols

counts_de %>%
  pivot_transcript() %>%

  # Subset data
  mutate(significant = FDR < 0.05 & abs(logFC) >= 2) %>%
  mutate(symbol = ifelse(symbol %in% topgenes_symbols, as.character(symbol), "")) %>%

  # Plot
  ggplot(aes(x = logFC, y = PValue, label = symbol)) +
  geom_point(aes(color = significant, size = significant, alpha = significant)) +
  geom_text_repel() +

  # Custom scales
  custom_theme +
  scale_y_continuous(trans = "log10_reverse") +
  scale_color_manual(values = c("black", "#e11f28")) +
  scale_size_discrete(range = c(0, 2))

Automatic bibliography

Tidybulk provides a handy function called get_bibliography that keeps track of the references for the methods used in your tidybulk workflow. The references are in BibTeX format and can be imported into your reference manager.

get_bibliography(counts_de)

Cell type composition analysis

If we are sequencing tissue samples, we may want to know what cell types are present and if there are differences in expression between them. tidybulk has a deconvolve_cellularity function that can help us do this.

For this example we will use a subset of the breast cancer dataset from The Cancer Genome Atlas (TCGA).

BRCA <- rladiestunis2021tidytranscriptomics::BRCA 
BRCA

With tidybulk, we can easily infer the proportions of cell types within a tissue using one of several published methods (Cibersort [@newman2015robust], EPIC [@racle2017simultaneous] and llsr [@abbas2009deconvolution]). Here we will use Cibersort which provides a default signature called LM22 to define the cell types. LM22 contains 547 genes that identify 22 human immune cell types.

BRCA_cell_type <-
  BRCA %>%
  deconvolve_cellularity(prefix="cibersort: ", cores = 1)

BRCA_cell_type

Cell type proportions are added to the tibble as new columns. The prefix makes it easy to reshape the data frame if needed, for visualisation or further analyses.

BRCA_cell_type_long <-
  BRCA_cell_type %>%
  pivot_sample() %>%

  # Reshape
  pivot_longer(
    contains("cibersort"),
    names_prefix = "cibersort: ",
    names_to = "cell_type",
    values_to = "proportion"
  )

BRCA_cell_type_long

We can plot the proportions of immune cell types for each patient.

BRCA_cell_type_long %>%

  # Plot proportions
  ggplot(aes(x = sample, y = proportion, fill = cell_type)) +
  geom_bar(stat = "identity") +
  custom_theme

Key Points

• Bulk RNA sequencing data can be represented and analysed in a 'tidy' way using tidySummarizedExperiment, tidybulk and the tidyverse.
• tidySummarizedExperiment enables us to visualise and manipulate a Bioconductor SummarizedExperiment object as if it were in tidy data format.
• Some of the key steps in an RNA sequencing analysis are filtering lowly abundant transcripts, adjusting for differences in sequencing depth and composition, testing for differential expression, and visualising the data, which can all be performed in a tidy way with tidybulk.
• tidybulk allows streamlined multi-method analyses
• tidybulk allow easy analyses of cell type composition

Part 2 Single-cell RNA-seq with tidySingleCellExperiment

A typical scRNA-seq workflow is shown in Background information. We don't have time in this workshop to go into depth on each step but you can read more about scRNA-seq workflows in the online book Orchestrating Single-Cell Analysis with Bioconductor.

In Part 1 we showed how we can study the cell-type composition of a biological sample using bulk RNA sequencing. Single cell sequencing enables a more direct estimation of cell-type composition and gives greater resolution. For bulk RNA sequencing we need to infer the cell types using the abundance of transcripts in the whole sample, with single-cell RNA sequencing we can directly measure the transcripts in each cell and then classify the cells into cell types.

# load packages
library(ggplot2)
library(purrr)
library(scater)
library(scran)
library(igraph)
library(batchelor)
library(SingleR)
library(scuttle)
library(EnsDb.Hsapiens.v86)
library(celldex)
library(ggbeeswarm)
library(tidySingleCellExperiment)

Introduction to tidySingleCellExperiment

The single-cell RNA sequencing data used here is 3000 cells in total, subsetted from 20 samples from 10 peripheral blood mononuclear cell (pbmc) datasets. The datasets are from GSE115189/SRR7244582 [@Freytag2018], SRR11038995 [@Cai2020, SCP345 (singlecell.broadinstitute.org), SCP424 [@Ding2020], SCP591 [@Karagiannis2020] and 10x-derived 6K and 8K datasets (support.10xgenomics.com/). The data is in SingleCellExperiment format. SingleCellExperiment is a very popular container of single cell RNA sequencing data.

Similar to what we saw with tidySummarizedExperiment, tidySingleCellExperiment package enables any SingleCellExperiment object to be displayed and manipulated according to tidy data principles, without affecting any SingleCellExperiment behaviour.

If we load the tidySingleCellExperiment package and then view the pbmc data it displays as a tibble.

# load pbmc single cell RNA sequencing data
pbmc <- rladiestunis2021tidytranscriptomics::pbmc

# take a look
pbmc

It can be interacted with using SingleCellExperiment commands such as assayNames.

assayNames(pbmc)

We can also interact with our object as we do with any tidyverse tibble.

Setting up the data

In this case we want to polish an annotation column. We will extract the sample, dataset and group information from the file name column into separate columns.

# First take a look at the file column
pbmc %>% select(file)

# Create columns for sample, dataset and groups
pbmc <-
  pbmc %>%

  # Extract sample and group
  extract(file, "sample", "../data/([a-zA-Z0-9_]+)/outs.+", remove = FALSE) %>%

  # Extract data source
  extract(file, c("dataset", "groups"), "../data/([a-zA-Z0-9_]+)_([0-9])/outs.+")

# Take a look
pbmc %>% select(sample, dataset, groups)

Quality control

A key quality control step performed in single cell analysis is assessment of the proportion of mitochondrial transcripts. A high mitochondrial count can indicate the cell is dead or dying and is thus poor quality and should be filtered out.

Nesting

A powerful tool tidySingleCellExperiment enables us to use with our single-cell data is tidyverse's nest. Nesting allows us to easily perform independent analyses on subsets of the data. For example, our data contains 10 single-cell datasets from different sources, we can nest by dataset and analyse the mitochondrial content for each.

To demonstrate the power of nesting we'll first show the mitochondrial analysis for one of the 10 datasets.

one_dataset <- pbmc %>% filter(dataset =="GSE115189")

We get the chromosomal location for each gene in the dataset so we can identify the mitochondrial genes. We'll get a warning that some of the ids don't find a match but it should be just a small proportion.

location <- mapIds(
  EnsDb.Hsapiens.v86,
  keys = rownames(one_dataset),
  column = "SEQNAME",
  keytype = "SYMBOL"
)

Next we calculate the mitchondrial content for each cell in the dataset.

mito_info_one_dataset <- perCellQCMetrics(one_dataset, subsets = list(Mito = which(location == "MT")))

mito_info_one_dataset

We then label the cells with high mitochondrial content as outliers.

mito_info_one_dataset <- mito_info_one_dataset %>%     
      # Converting to tibble
      as_tibble(rownames = "cell") %>%

      # Label cells with high mitochondrial content
      mutate(high_mitochondrion = isOutlier(subsets_Mito_percent, type = "higher"))

mito_info_one_dataset

Finally, we join the mitochondrial information back to the original data so we will be able to filter out the cells with high mitochondrial content.

mito_info_one_dataset <- left_join(one_dataset, mito_info_one_dataset, by = "cell")

mito_info_one_dataset

The steps above perform the mitochondrial QC analysis for one dataset. To analyse all 10 datasets we could repeat the steps for each dataset or create a function and apply it to each dataset. However, a more efficient way is to use nesting. We can nest our data by dataset.

knitr::include_graphics("../inst/vignettes/nesting.png")

pbmc_nested <- pbmc %>% 
    nest(data = -dataset)

pbmc_nested

We will create a table (tibble) with the mitochondrial QC information for each dataset. Here we use map which is often used in combination with nest to perform commands on nested data.

location <- mapIds(
  EnsDb.Hsapiens.v86,
  keys = rownames(pbmc),
  column = "SEQNAME",
  keytype = "SYMBOL"
)

mito_info_all_datasets <- pbmc_nested %>%
    mutate(mitochondrion_info = map(
    data,
    ~ # Calculate mitochondrial statistics
      perCellQCMetrics(.x, subsets = list(Mito = which(location == "MT"))) %>%

      # Convert to tibble
      as_tibble(rownames = "cell") %>%

      # Label cells with high mitochondrial content
      mutate(high_mitochondrion = isOutlier(subsets_Mito_percent, type = "higher"))
  ))

mito_info_all_datasets

As before, we join the mitochondrial information to the original data.

  # Join the mitochondrial information to the original SingleCellExperiment data
 mito_info_all_datasets <- mito_info_all_datasets %>%
    mutate(data = map2(
    data, mitochondrion_info,
    ~ left_join(.x, .y, by = "cell")
  )) %>%

  # Remove the separate mitochondrial information table
  select(-mitochondrion_info)

mito_info_all_datasets

Remove the nesting to get back one table containing all datasets.

pbmc <- mito_info_all_datasets %>%
  unnest(data)

pbmc

Nesting has allowed us to perform the QC on each dataset in an efficient way.

We can use tidyverse to reshape the data and create beeswarm plots to visualise the mitochondrial content.

pbmc %>%

  # Reshaping
  pivot_longer(c(detected, sum, subsets_Mito_percent)) %>%
  ggplot(aes(
    x = dataset, y = value,
    color = high_mitochondrion,
    alpha = high_mitochondrion,
    size = high_mitochondrion
  )) +

  # Plotting
  geom_quasirandom() +
  facet_wrap(~name, scale = "free_y") +

  # Customisation
  scale_color_manual(values = c("black", "#e11f28")) +
  scale_size_discrete(range = c(0, 2)) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 50, hjust = 1, vjust = 1))

In the faceted plot, detected is number of genes in each of the 10 datasets, sum is total counts.

We then proceed to filter out cells with high mitochondrial content.

pbmc <- pbmc %>% filter(!high_mitochondrion)

Scaling and Integrating

As we are working with multiple datasets, we need to integrate them and adjust for technical variability between them. Here we'll nest by dataset (batch), normalise within each batch with multiBatchNorm and correct for batch effects with fastMNN.

# Scaling within each dataset
pbmc <-
  pbmc %>%

  # Define batch
  mutate(batch = dataset) %>%
  nest(data = -batch) %>%

  # Normalisation   
  mutate(data = multiBatchNorm(data)) %>%

  # Integration
  pull(data) %>%
  fastMNN() 

Identify clusters

We proceed with identifying cell clusters.

# Assign clusters to the 'colLabels'
# of the SingleCellExperiment object
colLabels(pbmc) <- # from SingleCellExperiment
  pbmc %>%
  buildSNNGraph(use.dimred="corrected") %>% # from scran - shared nearest neighbour
  cluster_walktrap() %$% # from igraph
  membership %>%
  as.factor()

# Reorder columns
pbmc %>% select(label, everything())

Thanks to tidySingleCellExperiment we can interrogate the object with tidyverse commands and use count to count the number of cells in each cluster.

pbmc %>% count(label)

Reduce dimensions

Besides PCA which is a linear dimensionality reduction, we can apply neighbour aware methods such as UMAP, to better define locally similar cells. We can calculate the first 3 UMAP dimensions using the scater framework.

# Calculate UMAP with scater
pbmc <-
  pbmc %>%
  runUMAP(ncomponents = 3, dimred="corrected") # from scater

And we can plot the clusters as a 3D plot using plotly. This time we are colouring by estimated cluster labels to visually check the cluster labels.

pbmc %>%
  plot_ly(
    x = ~`UMAP1`,
    y = ~`UMAP2`,
    z = ~`UMAP3`,
    colors = friendly_cols[1:10],
    color = ~label,
    size=0.5 
  )

knitr::include_graphics("../inst/vignettes/plotly_2.png")

Cell type classification

Manual cell type classification

We can identify cluster markers (genes). As example, we are selecting the top 10 for each cluster. We can then plot a heatmap of those gene markers across cells.

# Identify top 10 markers per cluster
marker_genes <-
    pbmc %>%
    findMarkers(groups=pbmc$label, assay.type = "reconstructed") %>%  # scran
    as.list() %>%
    map(~ head(.x, 10) %>%  rownames()) %>%
    unlist()

# Plot heatmap
pbmc %>%
  plotHeatmap(                                  # from scater
    features=marker_genes, 
    columns=order(pbmc$label), 
    colour_columns_by=c("label"), 
    exprs_values  = "reconstructed"
  ) 

Automatic cell type classification

We can infer cell type identities (e.g. T cell) using SingleR [@aran2019reference] and manipulate the output using tidyverse. SingleR accepts any log-normalised transcript abundance matrix

# Reference cell types
blueprint <- BlueprintEncodeData()

cell_type <-

  # extracting counts from SingleCellExperiment object
  assays(pbmc)$reconstructed %>%

    # SingleR
  SingleR(
    ref = blueprint,
    labels = blueprint$label.main,
    clusters = pbmc %>% pull(label)
  ) %>%

  # Formatting results
  as.data.frame() %>%
  as_tibble(rownames = "label") %>%
  select(label, first.labels)

cell_type

We join the cell type information to our pbmc data.

# Join cell type info
pbmc <-
  pbmc %>%
  left_join(cell_type, by = "label")

# Reorder columns
pbmc %>% select(cell, first.labels, everything())

We can easily summarise the results. For example, we can see how cell type classification overlaps with cluster classification.

pbmc %>% count(label, first.labels)

Key Points

• Some basic steps of a single-cell RNA sequencing analysis are dimensionality reduction, cluster identification and cell type classification
• tidySingleCellExperiment is an invisible layer that operates on a SingleCellExperiment object and enables us to visualise and manipulate data as if it were a tidy data frame.
• tidySingleCellExperiment object is a SingleCellExperiment object so it can be used with any SingleCellExperiment compatible method

Contributing

If you want to suggest improvements for this workshop or ask questions, you can do so as described here.

Reproducibility

Record package and version information with sessionInfo

sessionInfo()

References

stemangiola/rladiestunis2021_tidytranscriptomics documentation built on Dec. 23, 2021, 5:30 a.m.