Convert AB1-format Sanger sequences to FASTQ

This data set is from 5 separate clutches of fish (1 control - uninjected, 2 injected with strong phenotype, 2 injected with mild phenotype), with injections from a guide against the ptena gene. For this exercise, the raw data comes as AB1 (Sanger) format. To convert AB1 files to FASTQ, we use ab1ToFastq(), which is a wrapper for functions in the “sangerseqR” package with additional quality score trimming.

Although there are many ways to organize such a project, we organize the data (raw and processed) data into a set of directories, with a directory for each type of data (e.g., ‘ab1’ for AB1 files, ‘fastq’ for FASTQ files, ‘bam’ for BAM files, etc.); this can continue with directories for scripts, for figures, and so on. With this structure in place, the following code sets up various directories.

library(CrispRVariants)
library(sangerseqR)

# List AB1 filenames, get sequence names,  make names for the fastq files
# Note that we only include one ab1 file with CrispRVariants because
# of space constraints.  All bam files are included

data_dir <- system.file(package="CrispRVariants", "extdata/ab1/ptena")
fq_dir <- tempdir()
ab1_fnames <- dir(data_dir, "ab1$", recursive=TRUE, full=TRUE)
sq_nms <- gsub(".ab1","",basename(ab1_fnames))

# Replace spaces and slashes in filename with underscores
fq_fnames  <- paste0(gsub("[\ |\\/]", "_", dirname(ab1_fnames)), ".fastq")

# abifToFastq to read AB1 files and write to FASTQ
dummy <- mapply( function(u,v,w) {
        abifToFastq(u,v,file.path(fq_dir,w))
}, sq_nms, ab1_fnames, fq_fnames)

We will collect sequences from each embryo into the same FASTQ file. Note that abifToFastq appends output to existing files where possible. In this example, there is only 1 sequence, which will be output to 1 file:

length(unique(ab1_fnames))

## [1] 1

length(unique(fq_fnames))

## [1] 1

Some of the AB1 files may not have a sufficient number of bases after quality score trimming (default is 20 bases). In these cases, abifToFastq() issues a warning (suppressed here).

Map the FASTQ reads

We use FASTQ format because it is the major format used by most genome alignment algorithms. At this stage, the alignment could be done outside of R (e.g., using command line tools), but below we use R and a call to system() to keep the whole workflow within R. Note that this also requires various software tools (e.g., bwa, samtools) to already be installed.

The code below iterates through all the FASTQ files generated above and aligns them to a pre-indexed genome.

library("Rsamtools")

# BWA indices were generated using bwa version 0.7.10
bwa_index <- "GRCHz10.fa.gz"
bam_dir <- system.file(package="CrispRVariants", "extdata/bam")
fq_fnames <- file.path(fq_dir,unique(fq_fnames))
bm_fnames <- gsub(".fastq$",".bam",basename(fq_fnames))
srt_bm_fnames <- file.path(bam_dir, gsub(".bam","_s",bm_fnames))

# Map, sort and index the bam files, remove the unsorted bams
for(i in 1:length(fq_fnames)) {
  cmd <- paste0("bwa mem ", bwa_index, " ", fq_fnames[i],
                " | samtools view -Sb - > ", bm_fnames[i])
  message(cmd, "\n"); system(cmd)
  indexBam(sortBam(bm_fnames[i],srt_bm_fnames[i]))
  unlink(bm_fnames[i])
}

See the help for bwa index at the bwa man page and for general details on mapping sequences to a genome reference.

List the BAM files

To allow easy matching to experimental condition (e.g., useful for colour labeling) and for subsetting to experiments of interest, we often organize the list of BAM files together with accompanying metadata in a machine-readable table beforehand. Here we read the bam filenames from a metadata table which also contains sample names and experimental grouping information. Note that we could also have used the bam filenames listed above.

# The metadata and bam files for this experiment are included with CrispRVariants
library("readxl")
md_fname <- system.file(package="CrispRVariants", "extdata/metadata/metadata.xls")
md <- readxl::read_xls(md_fname, 1)
md

## # A tibble: 5 × 7
##   bamfile                directory Short.name Targeting.type sgRNA1 sgRNA2 Group
##   <chr>                  <chr>     <chr>      <chr>          <chr>  <lgl>  <chr>
## 1 ab1_ptena_phenotype_e… ptena/ph… ptena 1    single         ptena… NA     stro…
## 2 ab1_ptena_phenotype_e… ptena/ph… ptena 2    single         ptena… NA     stro…
## 3 ab1_ptena_wildtype_lo… ptena/wi… ptena 3    single         ptena… NA     mild 
## 4 ab1_ptena_wildtype_lo… ptena/wi… ptena 4    single         ptena… NA     mild 
## 5 ab1_ptena_uninjected_… ptena/un… control    single         ptena… NA     cont…

# Get the bam filenames from the metadata table
bam_dir <- system.file(package="CrispRVariants", "extdata/bam")
bam_fnames <- file.path(bam_dir, md$bamfile)

# check that all files exist
all( file.exists(bam_fnames) )

## [1] TRUE

Create the target location and reference sequence

Given a set of BAM files with the amplicon sequences of interest mapped to the reference genome, we need to collect a few additional pieces of information about the guide sequence and define the area around the guide that we want to summarize the mutation spectrum over.

The coordinates of the region of interest can be obtained by running BLAST or BLAT on the guide sequence or by mapping the guide sequence to the reference sequence. The coordinates, or “target” should be represented as a GenomicRanges::GRanges object. This can be created directly, but here we will import the coordinates of the guide sequence from a BED file using the rtracklayer package. The import() commmand below returns a GRanges object.

library(rtracklayer)
# Represent the guide as a GenomicRanges::GRanges object
gd_fname <- system.file(package="CrispRVariants", "extdata/bed/guide.bed")
gd <- rtracklayer::import(gd_fname)
gd

## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |        name     score
##          <Rle>         <IRanges>  <Rle> | <character> <numeric>
##   [1]    chr17 23648474-23648496      - |   ptena_ccA         0
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

The 23bp (including PAM) ptena guide sequence used in this experiment is located on chromosome chr17 from 23648474-23648496. We prefer to analyse a slightly larger region. Below, we’ll extend the guide region by 5 bases on each side when counting variants. Note that the expected cut site (used later for labeling variants), after extension, isat base 22 with respect to the start of the new target sequence.

gdl <- GenomicRanges::resize(gd, width(gd) + 10, fix = "center")

With the Bioconductor BSgenome packages, the reference sequence itself can be retrieved directly into a DNAStringSet object. For other genomes, the reference sequence can be retrieved from a genome by first indexing the genome with samtools faidx and then fetching the required region (for an alternative method, see the note for Windows and Galaxy users below). Here we are using the GRCHz10 zebrafish genome. The reference sequence was fetched and saved as follows:

system("samtools faidx GRCHz10.fa.gz")

reference=system(sprintf("samtools faidx GRCHz10.fa.gz %s:%s-%s",
                         seqnames(gdl)[1], start(gdl)[1], end(gdl)[1]),
                 intern = TRUE)[[2]]

# The guide is on the negative strand, so the reference needs to be reverse complemented
reference=Biostrings::reverseComplement(Biostrings::DNAString(reference))
save(reference, file = "ptena_GRCHz10_ref.rda")

We’ll load the previously saved reference sequence.

ref_fname <- system.file(package="CrispRVariants", "extdata/ptena_GRCHz10_ref.rda")
load(ref_fname)
reference

## 33-letter DNAString object
## seq: GCCATGGGCTTTCCAGCCGAACGATTGGAAGGT

Note the NGG sequence (here, TGG) is present with the 5 extra bases on the end.

# First read the alignments into R.  The alignments must include
# the read sequences and the MD tag
alns <- GenomicAlignments::readGAlignments(bam_fnames[[1]], 
          param = Rsamtools::ScanBamParam(tag = "MD", what = c("seq", "flag")),
          use.names = TRUE)

# Then reconstruct the reference for the target region.
# If no target region is given, this function will reconstruct
# the complete reference sequence for all reads.
rfa <- refFromAlns(alns, gdl)

# The reconstructed reference sequence is identical to the sequence
# extracted from the reference above
print(rfa == reference)

## [1] TRUE

Creating a CrisprSet

The next step is to create a CrisprSet object, which is the container that stores the relevant sequence information, alignments, observed variants and their frequencies.

# Note that the zero point (target.loc parameter) is 22
crispr_set <- readsToTarget(bam_fnames, target = gdl, reference = reference,
                            names = md$Short.name, target.loc = 22)
crispr_set

## CrisprSet object containing 5 CrisprRun samples
## Target location:
## GRanges object with 1 range and 2 metadata columns:
##       seqnames            ranges strand |        name     score
##          <Rle>         <IRanges>  <Rle> | <character> <numeric>
##   [1]    chr17 23648469-23648501      - |   ptena_ccA         0
##   -------
##   seqinfo: 1 sequence from an unspecified genome; no seqlengths
## [1] "Most frequent variants:"
##            ptena 1 ptena 2 ptena 3 ptena 4 control
## no variant       3       4       4       0       7
## -1:4D            0       0       0       2       0
## 6:1D             0       0       0       1       1
## 1:7I             1       0       0       0       0
## 2:1D,4:5I        0       0       0       1       0
## Other            0       0       1       1       0

# The counts table can be accessed with the "variantCounts" function
vc <- variantCounts(crispr_set)
print(class(vc))

## [1] "matrix" "array"

You can see that in the table of variant counts, variants are summarised by the location of their insertions and deletions with respect to the target site. Non-variant sequences and sequences with a single nucleotide variant (SNV) but no insertion or deletion (indel) are displayed first, followed by the indel variants from most to least frequent For example, the most frequent non-wild-type variant, “-1:4D” is a 4 base pair deletion starting 1 base upstream of the zero point.

Creating summary plots of variants

We want to plot the variant frequencies along with the location of the guide sequence relative to the known transcripts. If you do this repeatedly for the same organism, it is worthwhile to save the database in a local file and read in with loadDb(), since this is quicker than retrieving it from UCSC (or Ensembl) each time.

We start by creating a transcript database of Ensembl genes. The gtf was downloaded from Ensembl version 81. We first took a subset of just the genes on chromosome 17 and then generated a transcript database.

# Extract genes on chromosome 17 (command line)
# Note that the Ensembl gtf does not include the "chr" prefix, so we add it here 
gtf=Danio_rerio.GRCz10.81.gtf.gz
zcat ${gtf} | awk '($1 == 17){print "chr"$0}' > Danio_rerio.GRCz10.81_chr17.gtf

# In R
library(GenomicFeatures)
gtf_fname <- "Danio_rerio.GRCz10.81_chr17.gtf"
txdb <- GenomicFeatures::makeTxDbFromGFF(gtf_fname, format = "gtf")
saveDb(txdb, file= "GRCz10_81_chr17_txdb.sqlite")

We now load the the previously saved database

plotVariants() is a wrapper function that groups together a plot of the transcripts of the gene/s overlapping the guide (optional), CrispRVariants::plotAlignments(), which displays the alignments of the consensus variant sequences to the reference, and CrispRVariants::plotFreqHeatmap(), which produces a table of the variant counts per sample, coloured by either their counts or percentage contribution to the variants observed for a given sample. If a transcript database is supplied, the transcript plot is annotated with the guide location. Arguments for plotAlignments() and plotFreqHeatmap() can be passed to plotVariants() as lists named plotAlignments.args and plotFreqHeatmap.args, respectively.

# The gridExtra package is required to specify the legend.key.height 
# as a "unit" object.  It is not needed to call plotVariants() with defaults
library(gridExtra)

# Match the clutch id to the column names of the variants
group <- md$Group

p <- plotVariants(crispr_set, txdb = txdb, gene.text.size = 8, 
    row.ht.ratio = c(1,8), col.wdth.ratio = c(4,2),
    plotAlignments.args = list(line.weight = 0.5, ins.size = 2, 
                               legend.symbol.size = 4),
    plotFreqHeatmap.args = list(plot.text.size = 3, x.size = 8, group = group, 
                                legend.text.size = 8, 
                                legend.key.height = grid::unit(0.5, "lines"))) 

## Warning in min(xranges): no non-missing arguments to min; returning Inf

## Warning in max(xranges): no non-missing arguments to max; returning -Inf

## Warning in max(yranges): no non-missing arguments to max; returning -Inf

## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.

(Top) schematic of gene structure showing guide location (left) consensus sequences for variants (right) variant counts in each embryo.

The plotVariants() options set the text size of the transcript plot annotation (gene.text.size) and the relative heights (row.ht.ratio) and widths (col.wdth.ratio) of the plots.

The plotAlignments arguments set the symbol size in the figure (ins.size) and in the legend (legend.symbol), the line thickness for the (optional) annotation of the guide region and cleavage site (line.weight).

For plotFreqHeatmap we define an grouping variable for colouring the x-axis labels (group), the size of the text within the plot (plot.text.size) and on the x-axis (x.size) and set the size of the legend text (legend.text.size).

Calculating the mutation efficiency

The mutation efficiency is the number of reads that include an insertion or deletion. Chimeric reads and reads containing single nucleotide variants near the cut site may be counted as variant reads, non-variant reads, or excluded entirely. See the help page for the function mutationEfficiency for more details.

We can see in the plot above that the control sample includes a variant sequence 6:1D, also present in sample ptena 4. We will exclude all sequences with this variant from the efficiency calculation. We also demonstrate below how to exclude particular variants.

# Calculate the mutation efficiency, excluding indels that occur in the "control" sample
# and further excluding the "control" sample from the efficiency calculation
eff <- mutationEfficiency(crispr_set, filter.cols = "control", exclude.cols = "control")
eff

##   ptena 1   ptena 2   ptena 3   ptena 4   Average    Median   Overall     StDev 
##     25.00     42.86     20.00     80.00     41.96     33.93     42.86      1.50 
## ReadCount 
##     21.00

# Suppose we just wanted to filter particular variants, not an entire sample.
# This can be done using the "filter.vars" argument
eff2 <- mutationEfficiency(crispr_set, filter.vars = "6:1D", exclude.cols = "control")

# The results are the same in this case as only one variant was filtered from the control
identical(eff,eff2)

## [1] TRUE

We see above that sample ptena 4 has an efficiency of 80%, i.e. 4 variant sequences, plus one sequence “6:1D” which is counted as a non-variant sequence as it also occurs in the control sample.

Get consensus alleles

The consensus sequences for variant alleles can be accessed using consensusSeqs. This function allows filtering by variant frequency or read count, as for plotAlignments and plotFreqHeatmap. Consensus alleles are returned with respect to the positive strand.

sqs <- consensusSeqs(crispr_set)
sqs

## DNAStringSet object of length 8:
##     width seq                                               names               
## [1]    33 ACCTTCCAATCGTTCGGCTGGAAAGCCCATGGC                 no variant
## [2]    29 NCCTTCCANTCGGCTGGAAAGCCCATGGC                     -1:4D
## [3]    32 ACCTTCNNTCGTTCGGCTGGAAAGCCCATGGC                  6:1D
## [4]    40 NCCTTCCAATCAGTTCGAATTCGGCTGGAGAGCCCATAGC          1:7I
## [5]    34 NCCTTCCAATCGGTTCGGCTGGAAAGCCCATGGC                1:1I
## [6]    40 NCCTTCCANCNCCTTCCTTTTCGGCTGGAAAGCCCATGGC          1:2I,3:5I
## [7]    29 ANNTTCNNATCGGCTGGNAAGCCNNTGGC                     -2:4D
## [8]    37 NCCTTCCACAAACACNTTCGGCTGGAAAGCCCATGGC             2:1D,4:5I

# The ptena guide is on the negative strand.
# Confirm that the reverse complement of the "no variant" allele
# matches the reference sequence:
Biostrings::reverseComplement(sqs[["no variant"]]) == reference

## [1] TRUE

Plot chimeric alignments

When deciding whether chimeric alignments should be considered as variant sequences, it can be useful to plot the frequent chimeras.

ch <- getChimeras(crispr_set, sample = "ptena 4")

# Confirm that all chimeric alignments are part of the same read
length(unique(names(ch))) == 1

## [1] TRUE

# Set up points to annotate on the plot
annotations <- c(resize(gd, 1, fix = "start"), resize(gd, 1, fix = "end"))
annotations$name <- c("ptena_start", "ptena_end")

plotChimeras(ch, annotations = annotations)

Here we see the read aligns as two tandem copies of the region chr17:23648420-23648656. The endpoint of each copy is not near the guide sequence. We do not consider this a genuine mutation, so we’ll recalculate the mutation efficiency excluding the chimeric reads and the control variant as before.

mutationEfficiency(crispr_set, filter.cols = "control", exclude.cols = "control",
                   include.chimeras = FALSE)

##   ptena 1   ptena 2   ptena 3   ptena 4   Average    Median   Overall     StDev 
##     25.00     42.86      0.00     75.00     35.71     33.93     36.84      1.50 
## ReadCount 
##     19.00

We see that the mutation effiency for “ptena 4” is now 75%, i.e. 3 genuine variant sequences, 1 sequence counted as “non-variant” because it occurs in the control, and the chimeric read excluded completely

Filtering data in plotVariants

For the following examples, we will use the ptena data set. We must first load the data and create a CrispRVariants::CrisprSet object.

# Setup for ptena data set
library("CrispRVariants")
library("rtracklayer")
library("GenomicFeatures")
library("readxl")

# Load the guide location
gd_fname <- system.file(package="CrispRVariants", "extdata/bed/guide.bed")
gd <- rtracklayer::import(gd_fname)
gdl <- resize(gd, width(gd) + 10, fix = "center")

# The saved reference sequence corresponds to the guide 
# plus 5 bases on either side, i.e. gdl
ref_fname <- system.file(package="CrispRVariants", 
                         "extdata/ptena_GRCHz10_ref.rda")
load(ref_fname)

# Load the metadata table, which gives the sample names
md_fname <- system.file(package="CrispRVariants",
                        "extdata/metadata/metadata.xls")
md <- readxl::read_xls(md_fname, 1)

# Get the list of bam files
bam_dir <- system.file(package="CrispRVariants", "extdata/bam")
bam_fnames <- file.path(bam_dir, md$bamfile)

# Check that all files were found
all(file.exists(bam_fnames))

## [1] TRUE

crispr_set <- readsToTarget(bam_fnames, target = gdl, reference = reference,
                            names = md$Short.name, target.loc = 22,
                            verbose = FALSE)

# Load the transcript database
txdb_fname <- system.file("extdata/GRCz10_81_ptena_txdb.sqlite", 
                          package="CrispRVariants")
txdb <- AnnotationDbi::loadDb(txdb_fname)

Here is the ptena data set plotted with default options:

p <- plotVariants(crispr_set, txdb = txdb)

## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns

The layout of this plot is controlled mainly by two parameters: row.ht.ratio and col.wdth.ratio. row.ht.ratio (default c(1,6)) controls the relative sizes of the transcript plot and the other plots. Below we show how to change the ratio so that the transcript plot is relatively larger:

p <- plotVariants(crispr_set, txdb = txdb, row.ht.ratio = c(1,3))

## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns

Similarly, col.wdth.ratio controls the width ratio of the alignment plot and the heatmap (default c(2,1), i.e. the alignment plot is twice as wide as the heatmap). Below we alter this to make the alignment plot comparatively wider:

p <- plotVariants(crispr_set, txdb = txdb, col.wdth.ratio = c(4,1))

The remaining examples in this section use the gol data set.

# Load gol data set
library("CrispRVariants")
data("gol_clutch1")

The data used in plotAlignments and plotFreqHeatmap can be filtered by either frequency via min.freq, count via min.count, or to show a set number of alleles sorted by frequency, via top.n. Within plotVariants, these filtering options need to be set for both plotAlignments and plotFreqHeatmap. We also add space to the bottom of the plot to prevent clipping of the labels.

library(GenomicFeatures)
p <- plotVariants(gol, plotAlignments.args = list(top.n = 3),
             plotFreqHeatmap.args = list(top.n = 3),
             left.plot.margin = ggplot2::unit(c(0.1,0,5,0.2), "lines"))

At present, filtering by sample (column) is possible for plotFreqHeatmap via the order parameter (which can also be used to reorder columns), but not plotAlignments.

plotVariants(gol, plotAlignments.args = list(top.n = 3),
             plotFreqHeatmap.args = list(top.n = 3, order = c(1,5,3)),
             left.plot.margin = ggplot2::unit(c(0.1,0,5,0.2), "lines"))

## TableGrob (2 x 1) "arrange": 2 grobs
##   z     cells    name                grob
## 1 1 (1-1,1-1) arrange rect[GRID.rect.993]
## 2 2 (2-2,1-1) arrange     gtable[arrange]

plotAlignments

plotAlignments(gol, top.n = 3, ins.size = 6)

plotAlignments(gol, top.n = 3, legend.symbol.size = 6)

plotAlignments(gol, top.n = 5, max.insertion.size = 25)

# Here we set a fairly high value of 50% for min.insertion.freq
# As ambiguous nucleotides occur frequently in this data set,
# there are no alleles passing this cutoff.
plotAlignments(gol, top.n = 5, min.insertion.freq = 50)

plotAlignments(gol, top.n = 5, max.insertion.size = 25, min.insertion.freq = 50)

# No white space between rows
plotAlignments(gol, top.n = 3, tile.height = 1)

# More white space between rows
plotAlignments(gol, top.n = 3, tile.height = 0.3)

plotAlignments(gol, top.n = 3, highlight.guide = FALSE)

library(IRanges)
guide <- IRanges::IRanges(15,28)
plotAlignments(gol, top.n = 3, guide.loc = guide)

# Here we increase the size of the axis labels and make
# two columns for the legend
plotAlignments(gol, top.n = 5, axis.text.size = 12, 
               legend.text.size = 12, legend.cols = 2)

# Don't highlight the PAM sequence
plotAlignments(gol, top.n = 3, highlight.pam = FALSE)

# Highlight 3 bases upstream to 3 bases downstream of the target.loc
plotAlignments(gol, top.n = 3, pam.start = 19, pam.end = 25)

plotAlignments(gol, top.n = 3, guide.loc = IRanges(5,10),
               pam.start = 8, pam.end = 13)

plotAlignments(gol, top.n = 3, line.weight = 3)

plotAlignments(gol, top.n = 3, codon.frame = 1)

plot_data <- plotAlignments(gol, top.n = 3, create.plot = FALSE)
names(plot_data)
# This data can be modified as required, then replotted using:
do.call(plotAlignments, plot_data)

plotFreqHeatmap

plotFreqHeatmap produces a heatmap of the counts or proportions of the variant alleles. Typically, plotFreqHeatmap is passed a CrisprSet object, but it can also accept a matrix if greater flexibility is required (see below). As plotFreqHeatmap returns a ggplot object, it can also be modified using standard ggplot2 syntax. For example, we add a title to the plot below.

By default, when given an object of class CrisprSet, plotFreqHeatmap shows the allele counts and the header shows the total number of on-target reads in each sample. For example, the following code shows the three most common variant alleles in the gol dataset. The header here does not equal the sum of the columns as not all variants are shown in the plot.

# Save the plot to a variable then add a title using ggplot2 syntax.
# If the plot is not saved to a variable the unmodified plot is displayed.
p <- plotFreqHeatmap(gol, top.n = 3)

## Warning in min(xranges): no non-missing arguments to min; returning Inf

## Warning in max(xranges): no non-missing arguments to max; returning -Inf

## Warning in max(yranges): no non-missing arguments to max; returning -Inf

p + labs(title = "A. plotFreqHeatmap with default options")

plotFreqHeatmap with default options

Plotting allele proportions

When calling plotFreqHeatmap with a CrisprSet object the type argument controls the information shown in text in the heatmap. Setting type = "counts" (the default) shows allele counts, setting type = "proportions" shows allele proportions. This also affects the default header values. When type = "proportions" the header shows the column sums, i.e. the percentage of the total number of reads shown in the plot.

plotFreqHeatmap(gol, top.n = 3, type = "proportions")

## Warning in min(xranges): no non-missing arguments to min; returning Inf

## Warning in max(xranges): no non-missing arguments to max; returning -Inf

## Warning in max(yranges): no non-missing arguments to max; returning -Inf

plotFreqHeatmap showing allele proportions

Changing colours of x-labels

The x-labels can be coloured by experimental group. To do this, a grouping vector must be supplied by setting parameter group. Columns are ordered according to the levels of the group. There should be one group value per column in the data.

ncolumns <- ncol(variantCounts(gol))
ncolumns

## [1] 8

grp <- rep(c(1,2), each = ncolumns/2)
p <- plotFreqHeatmap(gol, top.n = 3, group = grp, as.percent = FALSE)

## Warning in min(xranges): no non-missing arguments to min; returning Inf

## Warning in max(xranges): no non-missing arguments to max; returning -Inf

## Warning in max(yranges): no non-missing arguments to max; returning -Inf

## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.

p + labs(title = "B. coloured X labels with tiles coloured by count")

plotFreqHeatmap with X-axis labels coloured by experimental group and tiles coloured by count instead of proportion

The default colours are designed to be readable on a white background and colour-blind safe. These can be changed by supplying a vector of colours for each level of the group. Colours must be supplied if there are more than 7 experimental groups.

grp_clrs <- c("red", "purple")
p <- plotFreqHeatmap(gol, top.n = 3, group = grp, group.colours = grp_clrs,
                type = "proportions", header = "counts",
                legend.position = "bottom")

## Warning in min(xranges): no non-missing arguments to min; returning Inf

## Warning in max(xranges): no non-missing arguments to max; returning -Inf

## Warning in max(yranges): no non-missing arguments to max; returning -Inf

## Warning: Vectorized input to `element_text()` is not officially supported.
## ℹ Results may be unexpected or may change in future versions of ggplot2.

p <- p + labs(title = "C. Modified plotFreqHeatmap")
p

plotFreqHeatmap with labels showing allele proportions, header showing counts per sample and modified legend position.

plotFreqHeatmap(gol, top.n = 3, 
                legend.key.height = ggplot2::unit(1.5, "lines"))

## Warning in min(xranges): no non-missing arguments to min; returning Inf

## Warning in max(xranges): no non-missing arguments to max; returning -Inf

## Warning in max(yranges): no non-missing arguments to max; returning -Inf

var_counts <- variantCounts(gol, top.n = 3)
# (additional modifications to var_counts can be added here)
plotFreqHeatmap(var_counts)

barplotAlleleFreqs

barplotAlleleFreqs includes two different colour schemes - a default rainbow scheme and a blue-red gradient. Note that the transcript database txdb must be passed by name as this function accepts ellipsis arguments.

Here barplotAlleleFreqs is run with the default parameters:

barplotAlleleFreqs(crispr_set, txdb = txdb)

## Looking up variant locations

## Loading required namespace: VariantAnnotation

## 'select()' returned many:1 mapping between keys and columns
## 'select()' returned many:1 mapping between keys and columns

## Classifying variants

## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions

In this case barplotAlleleFreqs is run with the alternative palette.

barplotAlleleFreqs(crispr_set, txdb = txdb, palette = "bluered")

## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions

By default, a table of the number of sequences and alleles is plotted next to the barplot. This can be switched off. In this case, barplotAlleleFreqs will return an ggplot object, allowing further alteration of the appearance through the usual ggplot2::theme settings.

barplotAlleleFreqs(crispr_set, txdb = txdb, include.table = FALSE)

## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions

barplotAlleleFreqs.CrisprSet uses VariantAnnotation::locateVariants to look up the variant locations with respect to a transcript database. The default behaviour of barplotAlleleFreqs.matrix is to perform a naive classification of the variants as frameshift or non-frameshift by size. This approach ignores transcript scructure, but can be useful to give a faster overview, or in cases where the transcript structure is unknown.

var_counts <- variantCounts(crispr_set)
barplotAlleleFreqs(var_counts)

If the parameter classify is set to FALSE, the variants are plotted with no further aggregation. If there are more than seven variants, colours must be provided.

rainbowPal10 <- c("#781C81","#3F479B",
                  "#4277BD","#529DB7",
                  "#62AC9B","#86BB6A",
                  "#C7B944","#E39C37",
                  "#E76D2E","#D92120")

barplotAlleleFreqs(var_counts, classify = FALSE, bar.colours = rainbowPal10)

An arbitrary classification can also be used. CrispRVariants provides some utility functions to assist in classifying variants. Note that methods of the CrisprSet class are accessed with crisprSet$function() rather than function(crisprSet).

Here are some examples of variant classification:

# Classify variants as insertion/deletion/mixed
byType <- crispr_set$classifyVariantsByType()

## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions

byType

##            no variant                 -1:4D                  6:1D 
##          "no variant"            "deletion"            "deletion" 
##                  1:7I                  1:1I             1:2I,3:5I 
##           "insertion"           "insertion" "multiple insertions" 
##                 -2:4D             2:1D,4:5I                 Other 
##            "deletion"  "insertion/deletion"               "Other"

# Classify variants by their location, without considering size
byLoc <- crispr_set$classifyVariantsByLoc(txdb=txdb)

## Looking up variant locations

## 'select()' returned many:1 mapping between keys and columns
## 'select()' returned many:1 mapping between keys and columns

## Classifying variants

## Warning in dispatchDots(.self$.getFilteredCigarTable, ...): dispatchDots may
## not work as expected with S4 functions

byLoc

##   no variant        -1:4D         6:1D         1:7I         1:1I    1:2I,3:5I 
## "no variant"     "coding"     "coding"     "coding"     "coding"     "coding" 
##        -2:4D    2:1D,4:5I        Other 
##     "coding"     "coding"      "Other"

# Coding variants can then be classified by setting a size cutoff
byLoc <- crispr_set$classifyCodingBySize(byLoc, cutoff = 6)
byLoc

##             no variant                  -1:4D                   6:1D 
##           "no variant" "frameshift indel < 6" "frameshift indel < 6" 
##                   1:7I                   1:1I              1:2I,3:5I 
## "frameshift indel > 6" "frameshift indel < 6" "frameshift indel > 6" 
##                  -2:4D              2:1D,4:5I                  Other 
## "frameshift indel < 6"    "inframe indel > 6"                "Other"

# Combine filtering and variant classification, using barplotAlleleFreqs.matrix
vc <- variantCounts(crispr_set)

# Select variants that occur in at least two samples
keep <- names(which(rowSums(vc > 0) > 1))
keep

## [1] "no variant" "6:1D"       "Other"

# Use this classification and the selected variants
barplotAlleleFreqs(vc[keep,], category.labels = byLoc[keep])

p <- plotAlignments(gol, top.n = 3)
p + theme(legend.margin = ggplot2::unit(0, "cm"))

## Warning: `legend.margin` must be specified using `margin()`
## ℹ For the old behavior use `legend.spacing`

Plot the reference sequence

Processing large data with CrispRVariants requires some time. It can be useful to first plot the reference sequence to check that the intended target location is specified. Here we use the reference sequence from the gol data set included in CrispRVariants. Any Biostrings::DNAString can be used. Note that CrispRVariants::plotAlignments accepts elliptical arguments in its signature, so non-signature arguments must be supplied by name. The code below shows the minimum arguments required for running CrispRVariants::plotAlignments.

# Get a reference sequence
library("CrispRVariants")
data("gol_clutch1")
ref <- gol$ref

#Then to make the plot:
plotAlignments(ref, alns = NULL, target.loc = 22, ins.sites = data.frame())