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<h1 class="title toc-ignore">Crossover-identification-with-sscocaller-and-comapr</h1>
<h4 class="author">Ruqian Lyu</h4>
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<strong>Last updated:</strong> 2021-05-25
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</p>
<p>
<strong>Checks:</strong> <span class="glyphicon glyphicon-ok text-success" aria-hidden="true"></span> 6 <span class="glyphicon glyphicon-exclamation-sign text-danger" aria-hidden="true"></span> 1
</p>
<p>
<strong>Knit directory:</strong> <code>~/Projects/rejy_2020_single-sperm-co-calling/</code> <span class="glyphicon glyphicon-question-sign" aria-hidden="true" title="This is the local directory in which the code in this file was executed."> </span>
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<p>
This reproducible <a href="http://rmarkdown.rstudio.com">R Markdown</a> analysis was created with <a
href="https://github.com/jdblischak/workflowr">workflowr</a> (version 1.6.2). The <em>Checks</em> tab describes the reproducibility checks that were applied when the results were created. The <em>Past versions</em> tab lists the development history.
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<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongRMarkdownfilestronguncommittedchanges"> <span class="glyphicon glyphicon-exclamation-sign text-danger" aria-hidden="true"></span> <strong>R Markdown file:</strong> uncommitted changes </a>
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<p>The R Markdown is untracked by Git. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run <code>wflow_publish</code> to commit the R Markdown file and build the HTML.</p>
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<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongEnvironmentstrongempty"> <span class="glyphicon glyphicon-ok text-success" aria-hidden="true"></span> <strong>Environment:</strong> empty </a>
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<p>Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.</p>
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<a data-toggle="collapse" data-parent="#workflowr-checks" href="#strongSeedstrongcodesetseed20190102code"> <span class="glyphicon glyphicon-ok text-success" aria-hidden="true"></span> <strong>Seed:</strong> <code>set.seed(20190102)</code> </a>
</p>
</div>
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<p>The command <code>set.seed(20190102)</code> was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.</p>
</div>
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</code></pre>
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There are no past versions. Publish this analysis with <code>wflow_publish()</code> to start tracking its development.
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</div>
<div id="introduction" class="section level2">
<h2>Introduction</h2>
<p>We will demonstrate the usage of <a href="https://gitlab.svi.edu.au/biocellgen-public/sscocaller"><code>sscocaller</code></a> and <a href="https://github.com/ruqianl/comapr"><code>comapr</code></a> for identifying and visualising crossovers regions from single-sperm DNA sequencing dataset.</p>
<p><code>sscocaller</code>(<a href="https://gitlab.svi.edu.au/biocellgen-public/sscocaller" class="uri">https://gitlab.svi.edu.au/biocellgen-public/sscocaller</a>) applies a binomial Hidden Markov Model for inferring haplotypes of single sperm genomes from the aligned DNA reads in a BAM file. The inferred haplotype sequence can then be used for calling crossovers by identifying haplotype shifts (see <a href="https://github.com/ruqianl/comapr"><code>comapr</code></a> ).</p>
</div>
<div id="downloading-example-dataset" class="section level2">
<h2>Downloading example dataset</h2>
<p>An individual mouse genetic map was constructed by DNA sequencing of 217 sperm cells from a F1 hybrid mouse (B6 X CAST) <span class="citation">(Hinch et al. 2019)</span>. We will apply <code>sscocaller</code> on this dataset and it can be downloaded from GEO (Gene Expression Omnibus) with accession <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE125326">GSE125326</a></p>
<p>The slurm submission script <code>submit-wgetSRAFastqdump.sh</code> at <a href="https://gitlab.svi.edu.au/biocellgen-public/hinch-single-sperm-DNA-seq-processing.git">repo</a> can be used for downloading the <code>.sra</code> files and dumping them into paired fastq files for each sperm (including two bulk sperm samples).</p>
</div>
<div id="dataset-preprocessing" class="section level2">
<h2>Dataset preprocessing</h2>
<p>The preprocessing steps include read filtering and mapping, subsample reads and append cell barcodes to reads, merge bams, and find informative SNP markers.</p>
<div id="alignment" class="section level3">
<h3>1 Alignment</h3>
<p>The downloaded fastq files for each sperm cells (and the bulk sperm samples) were aligned to mouse reference genome mm10. The workflow <a href="https://gitlab.svi.edu.au/biocellgen-public/hinch-single-sperm-DNA-seq-processing.git"><code>run_alignment.snk</code></a> which is a <a href="https://snakemake.readthedocs.io/en/stable/">Snakemake</a> file that defined steps/rules including</p>
<ul>
<li>running <a href="https://github.com/OpenGene/fastp"><code>fastp</code></a> for filtering reads and adapter trimming</li>
<li>running <a href="https://github.com/lh3/minimap2"><code>minimap2</code></a> for mapping reads to reference genome mm10</li>
<li>running GATK MarkDuplicates</li>
<li>running GATK AddOrReplaceReadGroup</li>
<li>running sorting and indexing bam files using <code>samtools</code></li>
</ul>
</div>
<div id="subsample-reads-and-append-cb-tag" class="section level3">
<h3>2 Subsample reads and append CB tag</h3>
<p><code>sscocaller</code> is designed to process DNA reads with CB (cell barcode) tags from all single sperm cells stored in one BAM file. And to reduce some processing burdens, the mapped reads for each sperm were de-duplicated and subsamples to a fraction of 0.5.</p>
<p>In addition, before merging reads from each sperm, the CB (cell barcode, the SRR ID) tag was appended to each DNA read using <a href="https://github.com/ruqianl/appendCB">appendCB</a>. Refer to steps defined in <code>run_subsample.snk</code>.</p>
</div>
<div id="merge-single-sperm-bam-files-into-one-bam" class="section level3">
<h3>3 Merge single-sperm bam files into one Bam</h3>
<p><code>samtools</code> was used for merge CB-taged reads from all single sperm to one BAM file. See <code>submit-mergeBams.sh</code>.</p>
</div>
<div id="find-informative-snp-markers" class="section level3">
<h3>4 Find informative SNP markers</h3>
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<p>The informative SNP markers are those SNPs which differ between the two mouse stains that were used to generate the F1 hybrid mouse (CAST and BL6). The following steps were applied which largely align with what has been described in the original paper <span class="citation">(Hinch et al. 2019)</span>.</p>
<p>The bulk sperm sample <code>SRR8454653</code> was used for calling de no vo variants for this mouse individual using GATK HaplotypeCaller. Only the HET SNPs with <code>MQ>50</code> AND <code>DP>10</code> AND <code>DP<80</code> were kept. The SNPs were further filtered to only keep the positions which have been called as Homo_alternative <code>CAST_EiJ.mgp.v5.snps.dbSNP142.vcf.gz</code> downloaded from the dbsnp database from Mouse Genome Project<span class="citation">(Keane et al. 2011)</span>.</p>
</div>
</div>
<div id="running-sscocaller" class="section level2">
<h2>Running sscocaller</h2>
<p>With the DNA reads from each sperm were tagged and merged into one BAM file, we can run <code>sscocaller</code> for inferring the haplotype states against the list of informative SNP markers for each chromosome in each sperm.</p>
<p>The required input files are:</p>
<pre><code>mergedBam = "output/alignment/mergedBam/mergedAll.bam",
vcfRef="output/variants/denovoVar/SRR8454653.mkdup.sort.rg.filter.snps.castVar.vcf.gz",
bcFile="output/alignment/mergedBam/mergedAll.bam.barcodes.txt"</code></pre>
<p><code>run_sscocaller.snk</code> defines the rule for running <code>sscocaller</code> on each chromosome for sperm cells. The command line was:</p>
<pre><code>sscocaller --threads 4 --chrom "chr1" --chrName chr {input.mergedBam} \
{input.vcfRef} {input.bcFile} --maxTotalReads 150 --maxDP 10 \
sscocaller/hinch/hinch_
</code></pre>
</div>
<div id="output-files" class="section level2">
<h2>Output files</h2>
<p>The generated output files (for each chromosome, here showing chr1):</p>
<ul>
<li>hinch_chr1_altCount.mtx, sparse matrix file, containing the alternative allele counts (the CAST alleles)</li>
<li>hinch_chr1_totalCount.mtx, sparse matrix file, containing the total allele counts (the CAST + BL6 alleles)</li>
<li>hinch_chr1_vi.mtx, sparse matrix file, containing the inferred Viterbi state (haplotype state) for each chromosome against the list of SNP markers in "_snpAnnot.txt".</li>
<li>hinch_chr1_viSegInfo.txt, txt file, containing the inferred Viterbi state segments information. Details below</li>
<li>hinch_chr1_snpAnnot.txt, txt file, containing the row annotations (SNPs) for the above sparse matrices.</li>
</ul>
<p><em>Note</em>, the columns in these sparse matrices correspond to cells in the input <code>bcFile</code>.</p>
<p>**_viSegInfo.txt** contains summary statistics of inferred Viterbi state segments.</p>
<p>A Viterbi segment is defined by a list of consecutive SNPs having the same Viterbi state.</p>
<p>The columns in the <code>*_viSegInfo.txt</code> are:</p>
<ul>
<li>ithSperm,</li>
<li>Starting SNP position,</li>
<li>Ending SNP position,</li>
<li>the number of SNPs supporting the segment</li>
<li>the log likelihood ratio of the Viterbi segment</li>
<li>the inferred hidden state</li>
</ul>
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<p>The loglikelihood ratio is calculated by taking the inferred log likelihood and subtract the reversed log likelihood.</p>
<p>For example, the segment with two SNPs in the figure below: <img src="../public/meta_images/ratio_ll.png" /> The numbers in brackets indicating the (alternative allele counts, total allele counts) aligned to the two SNP positions.</p>
<p>The inferred log likelihood can be expressed as:</p>
<p><span class="math display">\[
inferredLogll = log(Trans_L)+log(dbinom(3,4,0.9))+log(dbinom(4,4,0.9))+log(Trans_R)
\]</span> The reversed log likelihood is then:</p>
<p><span class="math display">\[
reversedLogll = log(noTrans_L)+log(dbinom(3,4,0.1))+log(dbinom(4,4,0.1))+log(noTrans_R)
\]</span> Hence the logllRatio:</p>
<p><span class="math display">\[
logllRatio = inferredLogll - reversedLogll
\]</span></p>
<p>A larger <code>logllRatio</code> indicating we are more confident with the inferred Viterbi states for markers in the segment.</p>
</div>
<div id="diagnosic-plots" class="section level2">
<h2>Diagnosic plots</h2>
<p>The output files from <code>sscocaller</code> can be directly parsed through <code>readHapState</code> function. However, we have a look at some cell-level metrics and segment-level metrics before we parse the <code>sscocaller</code> output files.</p>
<div id="per-cell-qc" class="section level3">
<h3>Per cell QC</h3>
<p>The function <code>perCellQC</code> generates cell-level metrics in a data.frame and the plots in a list.</p>
<p>We first identify the relevant file paths:</p>
<p><code>dataset_dir</code> is the ouput directory from running <code>sscocaller</code> and <code>barcodeFile_path</code> points to the file containing the list of cell barcodes.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="kw">suppressPackageStartupMessages</span>({</span>
<span id="cb3-2"><a href="#cb3-2"></a> <span class="kw">library</span>(comapr)</span>
<span id="cb3-3"><a href="#cb3-3"></a> <span class="kw">library</span>(ggplot2)</span>
<span id="cb3-4"><a href="#cb3-4"></a> <span class="kw">library</span>(dplyr)</span>
<span id="cb3-5"><a href="#cb3-5"></a> <span class="kw">library</span>(Gviz)</span>
<span id="cb3-6"><a href="#cb3-6"></a> <span class="kw">library</span>(SummarizedExperiment)</span>
<span id="cb3-7"><a href="#cb3-7"></a>})</span></code></pre></div>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>path_dir <-<span class="st"> "/mnt/beegfs/mccarthy/scratch/general/Datasets/Hinch2019/"</span></span>
<span id="cb4-2"><a href="#cb4-2"></a>dataset_dir <-<span class="st"> </span><span class="kw">paste0</span>(path_dir,<span class="st">"output/alignment/sscocaller/hinch/"</span>)</span>
<span id="cb4-3"><a href="#cb4-3"></a>barcodeFile_path <-<span class="kw">paste0</span>(path_dir,<span class="st">"output/alignment/mergedBam/mergedAll.bam.barcodes.txt"</span>)</span></code></pre></div>
<p>We can locate the files and list the files to have a look:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw">list.files</span>(<span class="dt">path=</span>dataset_dir)[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>]</span></code></pre></div>
<pre><code>[1] "hinch_chr1_altCount.mtx" "hinch_chr1_snpAnnot.txt"
[3] "hinch_chr1_totalCount.mtx" "hinch_chr1_vi.mtx"
[5] "hinch_chr1_viSegInfo.txt" </code></pre>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>BiocParallel<span class="op">::</span><span class="kw">register</span>(BiocParallel<span class="op">::</span><span class="kw">MulticoreParam</span>(<span class="dt">workers =</span> <span class="dv">2</span>))</span>
<span id="cb7-2"><a href="#cb7-2"></a><span class="co">#BiocParallel::register(BiocParallel::SerialParam())</span></span></code></pre></div>
<p>Running <code>perCellChrQC</code> function to find the cell-level statistics:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>pcqc <-<span class="st"> </span><span class="kw">perCellChrQC</span>(<span class="st">"hinch"</span>,</span>
<span id="cb8-2"><a href="#cb8-2"></a> <span class="dt">chroms=</span><span class="kw">paste0</span>(<span class="st">"chr"</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">4</span>),</span>
<span id="cb8-3"><a href="#cb8-3"></a> <span class="dt">path=</span>dataset_dir,</span>
<span id="cb8-4"><a href="#cb8-4"></a> <span class="dt">barcodeFile=</span>barcodeFile_path)</span></code></pre></div>
<p>The generated scatter plots for selected chromosomes:</p>
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<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a>pcqc<span class="op">$</span>plot</span></code></pre></div>
<pre><code>Warning: Transformation introduced infinite values in continuous x-axis</code></pre>
<p><img src="figure/Crossover-identification-with-sscocaller-and-comapr.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>X-axis plots the number of haplotype transitions (<code>nCORaw</code>) for each cell and Y-axis plots the number of total SNPs detected in a cell. A large <code>nCORaw</code> might indicate the cell being a diploid cell included by accident or doublets. Cells with a lower <code>totalSNPs</code> might indicate poor cell quality.</p>
<p>A data.frame with cell-level metric is also returned:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>pcqc<span class="op">$</span>cellQC</span></code></pre></div>
<pre><code># A tibble: 776 x 4
Chrom totalSNP nCORaw barcode
<fct> <int> <dbl> <chr>
1 chr1 293471 30 SRR8454655
2 chr2 263514 21 SRR8454655
3 chr3 241774 21 SRR8454655
4 chr4 223045 32 SRR8454655
5 chr1 363924 31 SRR8454656
6 chr2 301028 29 SRR8454656
7 chr3 260600 36 SRR8454656
8 chr4 250873 18 SRR8454656
9 chr1 349103 26 SRR8454665
10 chr2 302099 9 SRR8454665
# … with 766 more rows</code></pre>
</div>
<div id="persegchrqc" class="section level3">
<h3>perSegChrQC</h3>
<p><code>PerSegQC</code> function visualises statistics of inferred haplotype state segments, which helps decide filtering thresholds for removing crossovers that do not have enough evidence by the data and the very close double crossovers which are biologically unlikely.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a>psqc <-<span class="st"> </span><span class="kw">perSegChrQC</span>(<span class="st">"hinch"</span>,<span class="dt">chroms=</span><span class="kw">paste0</span>(<span class="st">"chr"</span>,<span class="dv">1</span>),</span>
<span id="cb13-2"><a href="#cb13-2"></a> <span class="dt">path=</span>dataset_dir,</span>
<span id="cb13-3"><a href="#cb13-3"></a> <span class="dt">barcodeFile=</span>barcodeFile_path,</span>
<span id="cb13-4"><a href="#cb13-4"></a> <span class="dt">maxRawCO =</span> <span class="dv">30</span>)</span>
<span id="cb13-5"><a href="#cb13-5"></a>psqc<span class="op">+</span><span class="kw">theme_classic</span>()</span></code></pre></div>
<p><img src="figure/Crossover-identification-with-sscocaller-and-comapr.Rmd/unnamed-chunk-8-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
</div>
<div id="parsing-files-using-comapr" class="section level2">
<h2>Parsing files using <code>comapr</code></h2>
<p>Now we have some idea about the features of this dataset, we can read in the files from <code>sscocaller</code> which can be directly parsed through <code>readHapState</code> function. This function returns a <code>RangedSummarizedExperiment</code> object with <code>rowRanges</code> containing SNP positions that have ever contributed to crossovers in a cell, while <code>colData</code> contains the cell annotations such as barcodes.</p>
<p>The following filters have been applied:</p>
<ul>
<li>Segment level filters:
<ul>
<li>minSNP=30, the segment that results in one/two crossovers has to have more than 30 SNPs of support</li>
<li>minlogllRatio=150, the segment that results in one/two crossovers has to have logllRatio larger than 150.</li>
<li>bpDist=1e5, the segment that results in one/two crossovers has to have base pair distances larger than 1e5</li>
</ul></li>
<li>Cell level filters:
<ul>
<li>maxRawCO, the maximum number of raw crossovers (the number of state transitions from the _vi.mtx file) for a cell</li>
<li>minCellSNP=200, there have to be more than 200 SNPs detected within a cell, otherwise this cell is removed.</li>
</ul></li>
</ul>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1"></a>hinch_rse <-<span class="st"> </span><span class="kw">readHapState</span>(<span class="dt">sampleName =</span> <span class="st">"hinch"</span>,</span>
<span id="cb14-2"><a href="#cb14-2"></a> <span class="dt">path =</span> dataset_dir,</span>
<span id="cb14-3"><a href="#cb14-3"></a> <span class="dt">chrom=</span><span class="kw">paste0</span>(<span class="st">"chr"</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">19</span>),</span>
<span id="cb14-4"><a href="#cb14-4"></a> <span class="dt">barcodeFile =</span> barcodeFile_path,</span>
<span id="cb14-5"><a href="#cb14-5"></a> <span class="dt">minSNP =</span> <span class="dv">30</span>, <span class="dt">minCellSNP =</span> <span class="dv">200</span>,</span>
<span id="cb14-6"><a href="#cb14-6"></a> <span class="dt">maxRawCO =</span> <span class="dv">55</span>,</span>
<span id="cb14-7"><a href="#cb14-7"></a> <span class="dt">minlogllRatio =</span> <span class="dv">150</span>,</span>
<span id="cb14-8"><a href="#cb14-8"></a> <span class="dt">bpDist =</span> <span class="fl">1e5</span>)</span>
<span id="cb14-9"><a href="#cb14-9"></a><span class="kw">saveRDS</span>(hinch_rse,<span class="dt">file =</span> <span class="st">"output/outputR/analysisRDS/hinch_rse.rds"</span>)</span></code></pre></div>
<p>The <code>hinch_rse</code> object:</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a>hinch_rse</span></code></pre></div>
<pre><code>class: RangedSummarizedExperiment
dim: 48542 160
metadata(10): ithSperm Seg_start ... bp_dist barcode
assays(1): vi_state
rownames: NULL
rowData names(0):
colnames(160): SRR8454655 SRR8454656 ... SRR8454869 SRR8454870
colData names(1): barcodes</code></pre>
<p>The <code>rowRanges</code> of <code>hinch_rse</code></p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a>SummarizedExperiment<span class="op">::</span><span class="kw">rowRanges</span>(hinch_rse)</span></code></pre></div>
<pre><code>GRanges object with 48542 ranges and 0 metadata columns:
seqnames ranges strand
<Rle> <IRanges> <Rle>
[1] chr1 3000258 *
[2] chr1 3001490 *
[3] chr1 3001712 *
[4] chr1 3001745 *
[5] chr1 3003414 *
... ... ... ...
[48538] chr19 61324579 *
[48539] chr19 61325233 *
[48540] chr19 61325919 *
[48541] chr19 61327767 *
[48542] chr19 61330760 *
-------
seqinfo: 19 sequences from an unspecified genome; no seqlengths</code></pre>
<p>The <code>assay</code> slot contains the Viterbi state matrix (SNP by Cell):</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a>SummarizedExperiment<span class="op">::</span><span class="kw">assay</span>(hinch_rse)[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>]</span></code></pre></div>
<pre><code>5 x 5 sparse Matrix of class "dgCMatrix"
SRR8454655 SRR8454656 SRR8454657 SRR8454658 SRR8454660
[1,] . . . . .
[2,] . 2 . . .