In stemangiola/biocasia2020_tidytranscriptomics: Introduction to Tidy Transcriptomics

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# Part 1 Bulk RNA-seq Core

This workshop will present how to perform analysis of RNA sequencing data following the tidy data paradigm [@wickham2014tidy]. The tidy data paradigm provides a standard way to organise data values within a dataset, where each variable is a column, each observation is a row, and data is manipulated using an easy-to-understand vocabulary. Most importantly, the data structure remains consistent across manipulation and analysis functions.

This can be achieved for RNA sequencing data with the [tidybulk](https://stemangiola.github.io/tidybulk/), [tidyHeatmap](https://stemangiola.github.io/tidyHeatmap) [@mangiola2020tidyheatmap] and [tidyverse](https://www.tidyverse.org/) [@wickham2019welcome] packages. The tidybulk package provides a tidy data structure and a modular framework for bulk transcriptional analyses. tidyHeatmap provides a tidy implementation of ComplexHeatmap. These packages are part of the tidytranscriptomics suite that introduces a tidy approach to RNA sequencing data representation and analysis


### Acknowledgements
Some of the material in Part 1 was adapted from an R for RNA sequencing workshop first run [here](http://combine-australia.github.io/2016-05-11-RNAseq/). Use of the airway dataset was inspired by the [DESeq2 vignette](http://bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html).

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

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.

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

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

Note: you should load the tidybulk library after the tidyverse core packages for best integration.

# load libraries

# dataset
library(airway)

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

# tidyverse-friendly packages
library(plotly)
library(ggrepel)
library(GGally)
library(tidyHeatmap)
library(tidybulk)

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)
      )
  )

Setting up the data

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.

The airway data is stored as a Bioconductor RangedSummarizedExperiment object. We will convert this object into a tidybulk tibble. A tibble is the tidyverse table format.

In this workshop we will be using 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 (as we'll see later). It also can make the steps clearer and easier to see. For more details on the pipe see here.

# load airway RNA sequencing data
data(airway)

# convert to tidybulk tibble
counts_airway <-
    airway %>%
    tidybulk()

# take a look
counts_airway

The counts_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-wise 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 shorten the sample names. We can remove the SRR1039 prefix that's present in all of them, 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.

We can get the gene symbols for these Ensembl gene ids using the Bioconductor annotation package for human, org.Hs.eg.db and add them as a column using mutate again.

With tidyverse, all above steps can be linked with the %>%, as shown below. This has the benefits that

• no temporary variables need to be created
• less typing is required
• the steps can be seen more clearly.

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

From this tidybulk tibble, we can perform differential expression analysis with the tidybulk package.

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 vs 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_tt %>% 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)) +
    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 %>%
    tidybulk() %>%
  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), 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) %>%

    # create heatmap
    heatmap(
          .column = sample,
          .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).

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, feature) %>%
  ggpairs(1:4)

Note: You may notice that the methods produce columns with different names for similar outputs. If you wish to make these consistent you can do that with tidyverse rename. For example, to rename the p value adjusted columns you could run below.

de_all %>% rename(deseq2_FDR = deseq2_padj, voom_FDR = voom_adj.P.Val)

Or if we just wanted to run one 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(~ 0 + group + cell)
dgList <- estimateDisp(dgList, design)
fit <- glmQLFit(dgList, design)
TvsU <- makeContrasts(TvsU=grouptrt-groupuntrt, levels=design)
qlf <- glmQLFTest(fit, contrast=TvsU)

Plots after testing for differentially expressed

We'll extract the symbols for a few top genes (by P value) to use in some of the plots we will make.

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

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)

# 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

A more informative plot, integrating some of the packages in tidyverse.

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))

Stripcharts

Before following up on the differentially expressed genes with further lab work, it is also recommended to have a look at the expression levels of the individual samples for the genes of interest. We can use stripcharts to do this. These will help show if expression is consistent amongst replicates in the groups.

With stripcharts we can see if replicates tend to group together and how the expression compares to the other groups. We'll also add a box plot to show the distribution. Tidyverse faceting makes it easy to create a plot for each gene.

strip_chart <-
    counts_scaled %>%

    # extract counts for top differentially expressed genes
    filter(symbol %in% topgenes_symbols) %>%

    # make faceted stripchart
    ggplot(aes(x = dex, y = counts_scaled + 1, fill = dex, label = sample)) +
    geom_boxplot() +
    geom_jitter() +
    facet_wrap(~symbol) +
    scale_y_log10()+
    custom_theme

strip_chart

Interactive Plots

A really nice feature of using tidyverse and ggplot2 is that we can make interactive plots quite easily using the plotly package. This can be very useful for exploring what genes or samples are in the plots. We can make interactive plots directly from our ggplot2 object (strip_chart). Having label in the aes is useful to visualise the identifier of the data point (here the sample id) or other variables when we hover over the plot.

We can also specify which parameters from the aes we want to show up when we hover over the plot with tooltip.

strip_chart %>% ggplotly(tooltip = c("label", "y"))

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_tidy <- 
    biocasia2020tidytranscriptomics::BRCA %>%
    tidybulk(patient, transcript, count)

BRCA_tidy

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.

deconvolve_cellularity adds cell type proportions to the tibble as new columns. The prefix makes it easy to reshape the data frame if needed, for visualisation or further analyses. For example, we can plot the proportions of immune cell types for each patient.

BRCA_tidy %>%

  # Identifying cell types
  deconvolve_cellularity(action="get", cores=2) %>%

  # Reshaping 
  pivot_longer(
    contains("cibersort"), 
    names_prefix = "cibersort: ", 
    names_to = "cell_type",
    values_to = "proportion"
  ) %>%

  # Plotting
  ggplot(aes(x=patient, y=proportion, fill=cell_type)) + 
  geom_bar(stat = "identity") +
  custom_theme 

Key Points

• RNA sequencing data can be represented and analysed in a 'tidy' way using tidybulk and the tidyverse
• With the modularity offered by piping we don't need to create variables, unless an object is used more than one. This improves robustness of the code.
• The principles of tidy transcriptomics are to interface as much as possible with commonly known manipulation and visualisation tools, rather than creating custom functions.
• Some of the key steps in an RNA sequencing analysis are (i) filtering lowly abundant transcripts, (ii) adjusting for differences in sequencing depth and composition, (iii) testing for differential expression
• Dimensionality reduction (PCA or MDS) plots are very important for exploring the data
• Density plots, volcano plots, strip-charts and heatmaps are useful visualisation tools for evaluating the hypothesis testing.
• tidybulk allows streamlined multi-method analyses
• tidybulk allow easy analyses of cell type composition
• Testing for differences in tissue composition between samples is analogous to the testing for differences in transcript abundance

Supplementary

Some things we don't have time to cover in Part 1 of this workshop can be found in the Supplementary material.

Part 2 Single-cell RNA-seq

Introduction

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

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.

SingleCellExperiment is a very popular container for single cell RNA sequencing data [@sce].

tidySCE provides a bridge between the SingleCellExperiment single-cell package [@sce] and the tidyverse [@wickham2019welcome]. It enables the display of the SingleCellExperiment object as a tidyverse tibble, and provides SingleCellExperiment-compatible dplyr, tidyr, ggplot and plotly functions.

# load additional libraries
library(biocasia2020tidytranscriptomics)
library(dplyr)
library(purrr)
library(stringr)
library(SummarizedExperiment)
library(SingleCellExperiment)
library(scater)
library(scran)
library(igraph)
library(SingleR)
library(tidySCE)

Create tidySCE

This is a SingleCellExperiment object but it is evaluated as tibble. So it is fully compatible both with SingleCellExperiment and tidyverse APIs.

pbmc_small_tidy <- biocasia2020tidytranscriptomics::pbmc_small %>% tidy()

It looks like a tibble

pbmc_small_tidy

But it is a SingleCellExperiment object after all

assayNames(pbmc_small_tidy) # from SummarizedExperiment

Polish the data

We can interact with our object as we do with any tibble. In this case we want to polish an annotation column.

pbmc_small_polished <-
    pbmc_small_tidy %>%

    # Clean groups
    mutate(groups = groups %>% str_remove("^g")) %>%

    # Extract sample
    extract(file, "sample", "../data/sample([a-z0-9]+)/outs.+")

pbmc_small_polished

Calculate the log of the scaled counts

We can treat pbmc_small_polished as a SingleCellExperiment object

counts <- assay(pbmc_small_polished, "counts")                         # from SummarizedExperiment
libsizes <- colSums(counts)
size.factors <- libsizes/mean(libsizes)
logcounts(pbmc_small_polished) <- log2(t(t(counts)/size.factors) + 1)  # from SingleCellExperiment
assayNames(pbmc_small_polished)                                        # from SummarizedExperiment

Exploratory analyses

Here we plot abundance of two transcripts for each group.

pbmc_small_polished_abundance = 
  pbmc_small_polished %>%

  # Extract abundance
  join_transcripts(transcripts=c("HLA-DRA", "TCL1A")) 

pbmc_small_polished_abundance

We proceed to plot. Note: we don't need to create many of these temporary variables. They are created here for educational purposes only.

pbmc_small_polished_abundance %>%
  ggplot2::ggplot(aes(groups, abundance_counts + 1, fill=groups)) +
  geom_boxplot(outlier.shape=NA) +
  geom_jitter(alpha=0.5, width=0.2) +
  scale_y_log10() +
  facet_wrap(~transcript, scales="free_y") +
  custom_theme

Reduce dimensions

PCA

We proceed with data processing with Bioconductor packages, such as scran [@lun2016pooling] and scater [@mccarthy2017scater].

variable_genes <-
    pbmc_small_polished %>%
    modelGeneVar() %>%                 # from scran
    getTopHVGs(prop=0.1)               # from scran

# Perform PCA with scater
pbmc_small_pca <-
    pbmc_small_polished %>%
    runPCA(subset_row=variable_genes)  # from scater

pbmc_small_pca

If a tidyverse-compatible package is not included in the tidySCE collection, we can use as_tibble to permanently convert tidySCE into a tibble.

# Create pairs plot with GGally
pbmc_small_pca %>%
    as_tibble() %>%
    GGally::ggpairs(columns=4:8, ggplot2::aes(colour=groups)) +
    custom_theme

UMAP

Beside 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.

pbmc_small_UMAP <-
    pbmc_small_pca %>%
    runUMAP(ncomponents=3)           # from scater

pbmc_small_UMAP

Identify clusters

We can proceed with cluster identification with scran.

pbmc_small_cluster <- pbmc_small_UMAP

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

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

Now we can interrogate the object as if it was a regular tibble data frame.

pbmc_small_cluster %>% count(groups, label)

And we can plot them using 3D plot using plotly.

pbmc_small_cluster %>%
  plot_ly(
    x = ~UMAP1,
    y = ~UMAP2,
    z = ~UMAP3,
    color = ~ label,
    colors = friendly_cols[1:5]
  )

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

Plotting heatmap

Using scater

plotHeatmap(                                  # from scater
  pbmc_small_cluster, 
  features=variable_genes, 
  columns=order(pbmc_small_cluster$label), 
  colour_columns_by=c("label")
) 

Using tidyverse

pbmc_small_cluster %>%

  # Get transcript abundance
  join_transcripts(transcripts=variable_genes) %>%

  # Plot heatmap
  heatmap(                                            # from tidyHeatmap
    .row = transcript,
    .column = cell, 
    .value = abundance_logcounts
  ) %>%

  # Add annotation
  add_tile(label) %>%
  add_point(PC1)

Cell type classification

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

# DO NOT EXECUTE - CELLDEX DEPENDENCY NOT IN BIOCONDUCTOR YET

# Get cell type reference data
hpca <- HumanPrimaryCellAtlasData()

# Infer cell identities
cell_type_df <-

  # extracting counts from SingleCellExperiment object
  assays(pbmc_small_cluster)$logcounts%>%

    # SingleR
  SingleR(
    ref = hpca,
    labels = hpca$label.main,
    method = "cluster",
    clusters = pbmc_small_cluster %>% pull(label)
  ) %>%

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

We have pre-calculated the cluster classification.

cell_type_df <- biocasia2020tidytranscriptomics::cell_type_df
cell_type_df

# Join UMAP and cell type info
pbmc_small_cell_type <-
  pbmc_small_cluster %>%
  left_join(
    cell_type_df, 
    by = "label"
  )

pbmc_small_cell_type 

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

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

Nested analyses

A powerful tool we can use with tidySCE is nest. We can easily perform independent analyses on subsets of the dataset. First we classify cell types in lymphoid and myeloid; then, nest based on the new classification

pbmc_small_nested <-
  pbmc_small_cell_type %>%
  filter(first.labels != "Platelets") %>%
  mutate(cell_class = 
           if_else(
             `first.labels` %in% c("Macrophage", "Monocyte"),
             "myeloid", 
             "lymphoid"
           )
          ) %>%
  nest(data = -cell_class)

pbmc_small_nested

Now we can independently perform analyses for the lymphoid and myeloid cell subsets.

pbmc_small_nested %>%
mutate(variable_genes = map_chr(
  data, ~ .x %>%
    modelGeneVar() %>%
    getTopHVGs(prop=0.05) %>% 
    paste(collapse=", ")
)) 

Key Points

• Some basic steps of a single-cell RNA sequencing analysis are dimensionality reduction, cluster identification and cell type classification
• tidySCE 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.
• tidySCE object is a SingleCellExperiment object so it can be used with any SingleCellExperiment compatible method

```{poll class.source="poll"} Poll: What would be your preferred name for the package currently called tidySCE?

# Contributing
If you want to suggest improvements for this workshop or ask questions, you can do so as described [here](https://github.com/stemangiola/biocasia2020_tidytranscriptomics/blob/master/CONTRIBUTING.md).

# Reproducibility
Record package and version information with `sessionInfo`

```r
sessionInfo()

References

stemangiola/biocasia2020_tidytranscriptomics documentation built on Oct. 26, 2020, 3:36 p.m.