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      <h1 data-toc-skip>Sequence motifs and kmers</h1>
                        <h4 data-toc-skip class="author">Tudor-Stefan
Cotet, Victor Kreiner, Alexander Yermanos</h4>
            
            <h4 data-toc-skip class="date">10/06/2022</h4>
      
      <small class="dont-index">Source: <a href="https://github.com/alexyermanos/Platypus/tree/mastervignettes/Kmers.Rmd" class="external-link"><code>vignettes/Kmers.Rmd</code></a></small>
      <div class="hidden name"><code>Kmers.Rmd</code></div>

    </div>

    
    
<div class="section level2">
<h2 id="introduction">1 Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
<p>The Platypus family of packages are meant to provide potential
pipelines and examples relevant to the broad field of computational
immunology. The core set of functions can be found at <a href="https://github.com/alexyermanos/Platypus" class="external-link uri">https://github.com/alexyermanos/Platypus</a> and examples of
use can be found in the publications <a href="https://doi.org/10.1093/nargab/lqab023" class="external-link uri">https://doi.org/10.1093/nargab/lqab023</a> and
<strong>insert biorxiv database manuscript here</strong></p>
<p>Stay tuned for updates <a href="https://twitter.com/AlexYermanos" class="external-link uri">https://twitter.com/AlexYermanos</a></p>
<p>This vignette will focus on kmer and sequence motif analyses at a
repertoire level. The two main functions used are VDJ_logoplot_vector
for sequence logoplots and VDJ_kmers for kmer distributions
visualization.</p>
</div>
<div class="section level2">
<h2 id="loading-the-vgm">2. Loading the VGM<a class="anchor" aria-label="anchor" href="#loading-the-vgm"></a>
</h2>
<p>The output of the VDJ_GEX_matrix is the main object for all
downstream functions in Platypus. We can create this directly into the R
session by using the public data available on PlatypusDB. We will use
the VDJ data of bone marrow plasma cells from five mice that were
immunized with OVA (and the MPLA adjuvant) from Neumeier^, Yermanos^ et
al 2022 PNAS.</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="../reference/PlatypusDB_fetch.html">PlatypusDB_fetch</a></span><span class="op">(</span>PlatypusDB.links <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"neumeier2021b//VDJmatrix"</span><span class="op">)</span>, </span>
<span>                                     load.to.enviroment <span class="op">=</span> <span class="cn">T</span>, combine.objects <span class="op">=</span> <span class="cn">F</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## 2022-08-07 20:34:12: Starting download of neumeier2021b__VDJmatrix.RData...</span></span></code></pre>
<pre><code><span><span class="co">## [1] "neumeier2021b__VDJmatrix"</span></span></code></pre>
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">VDJ</span> <span class="op">&lt;-</span> <span class="va">neumeier2021b__VDJmatrix</span><span class="op">[[</span><span class="fl">1</span><span class="op">]</span><span class="op">]</span></span></code></pre></div>
</div>
<div class="section level2">
<h2 id="vdj_kmers-for-kmer-distribution-analysisvisualization">3. VDJ_kmers for kmer distribution analysis/visualization<a class="anchor" aria-label="anchor" href="#vdj_kmers-for-kmer-distribution-analysisvisualization"></a>
</h2>
<p>The VDJ_kmers function is flexible enough to allow for kmer counting
for various sequence types (inputted as VDJ sequence columns in the
sequence.column parameter) and groups (in the grouping.column parameter
- e.g., grouping.column = ‘sample_id’ for sample-level statistics).</p>
<p>In the following analysis, we will focus on the 3-kmers for CDR3
sequences, by setting the kmer.k parameter to 5 and sequence.column one
to c(‘VDJ_cdr3s_nt’, ‘VJ_cdr3s_nt’) for full CDR3s.</p>
<div class="section level3">
<h3 id="visualizing-the-distribution-of-kmers">3.1 Visualizing the distribution of kmers<a class="anchor" aria-label="anchor" href="#visualizing-the-distribution-of-kmers"></a>
</h3>
<p>Kmer distributions can be visualized by setting the plot.format
argument to ‘barplot’.</p>
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">VDJ</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/VDJ_kmers.html">VDJ_kmers</a></span><span class="op">(</span>sequence.column <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'VDJ_cdr3s_nt'</span>, <span class="st">'VJ_cdr3s_nt'</span><span class="op">)</span>, </span>
<span>                  grouping.column <span class="op">=</span> <span class="st">'sample_id'</span>,</span>
<span>                  kmer.k <span class="op">=</span> <span class="fl">3</span>,</span>
<span>                  plot.format <span class="op">=</span> <span class="st">'barplot'</span><span class="op">)</span></span></code></pre></div>
<p><img src="Kmers_files/figure-html/unnamed-chunk-3-1.png" width="700"></p>
<p>We can choose to plot only a specific subset of all available kmers
via the specific.kmers parameter. For example, we will choose only ‘GGT’
and ‘GTA’ as our kmers.</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">VDJ</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/VDJ_kmers.html">VDJ_kmers</a></span><span class="op">(</span>sequence.column <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'VDJ_cdr3s_nt'</span>, <span class="st">'VJ_cdr3s_nt'</span><span class="op">)</span>, </span>
<span>                  grouping.column <span class="op">=</span> <span class="st">'sample_id'</span>,</span>
<span>                  kmer.k <span class="op">=</span> <span class="fl">3</span>,</span>
<span>                  plot.format <span class="op">=</span> <span class="st">'barplot'</span>,</span>
<span>                  specific.kmers <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'GGT'</span>, <span class="st">'GTA'</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
<p><img src="Kmers_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
<p>Moreover, we can choose to only plot a maximum number of kmers for
clarity, by modifying the max.kmers parameter.</p>
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">VDJ</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/VDJ_kmers.html">VDJ_kmers</a></span><span class="op">(</span>sequence.column <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'VDJ_cdr3s_nt'</span>, <span class="st">'VJ_cdr3s_nt'</span><span class="op">)</span>, </span>
<span>                  grouping.column <span class="op">=</span> <span class="st">'sample_id'</span>,</span>
<span>                  kmer.k <span class="op">=</span> <span class="fl">3</span>,</span>
<span>                  plot.format <span class="op">=</span> <span class="st">'barplot'</span>,</span>
<span>                  max.kmers <span class="op">=</span> <span class="fl">64</span><span class="op">)</span></span></code></pre></div>
<p><img src="Kmers_files/figure-html/unnamed-chunk-5-1.png" width="700"></p>
</div>
<div class="section level3">
<h3 id="plotting-formats">3.2 Plotting formats<a class="anchor" aria-label="anchor" href="#plotting-formats"></a>
</h3>
<p>VDJ_kmers includes additional plotting formats for the kmer results,
such as PCA dimensionality reduction of the kmer vectors (plot.format =
‘pca’) and kmer density plots (plot.format = ‘density’).</p>
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co">#PCA plot</span></span>
<span><span class="va">VDJ</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/VDJ_kmers.html">VDJ_kmers</a></span><span class="op">(</span>sequence.column <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'VDJ_cdr3s_nt'</span>, <span class="st">'VJ_cdr3s_nt'</span><span class="op">)</span>, </span>
<span>                  grouping.column <span class="op">=</span> <span class="st">'sample_id'</span>,</span>
<span>                  kmer.k <span class="op">=</span> <span class="fl">3</span>,</span>
<span>                  plot.format <span class="op">=</span> <span class="st">'pca'</span><span class="op">)</span></span></code></pre></div>
<p><img src="Kmers_files/figure-html/unnamed-chunk-6-1.png" width="700"></p>
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co">#Density plot</span></span>
<span><span class="va">VDJ</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/VDJ_kmers.html">VDJ_kmers</a></span><span class="op">(</span>sequence.column <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">'VDJ_cdr3s_nt'</span>, <span class="st">'VJ_cdr3s_nt'</span><span class="op">)</span>, </span>
<span>                  grouping.column <span class="op">=</span> <span class="st">'sample_id'</span>,</span>
<span>                  kmer.k <span class="op">=</span> <span class="fl">3</span>,</span>
<span>                  plot.format <span class="op">=</span> <span class="st">'density'</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## Picking joint bandwidth of 2980</span></span></code></pre>
<p><img src="Kmers_files/figure-html/unnamed-chunk-7-1.png" width="700"></p>
</div>
</div>
<div class="section level2">
<h2 id="sequence-motifs-analysis---logoplots">4. Sequence motifs analysis - logoplots<a class="anchor" aria-label="anchor" href="#sequence-motifs-analysis---logoplots"></a>
</h2>
<p>Sequence logoplots can be created via the VDJ_logoplot_vector
function.</p>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">pasted_CDR3s</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">VDJ</span><span class="op">$</span><span class="va">VDJ_cdr3s_aa</span>, <span class="va">VDJ</span><span class="op">$</span><span class="va">VJ_cdr3s_aa</span><span class="op">)</span></span>
<span><span class="va">p</span> <span class="op">&lt;-</span>  <span class="fu"><a href="../reference/VDJ_logoplot_vector.html">VDJ_logoplot_vector</a></span><span class="op">(</span>cdr3.vector <span class="op">=</span> <span class="va">pasted_CDR3s</span>, seq_type <span class="op">=</span> <span class="st">"aa"</span>, length_cdr3 <span class="op">=</span> <span class="st">"auto"</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## Returning logoplot based on 3868 sequences of a total 20409 input sequences</span></span></code></pre>
<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">p</span></span></code></pre></div>
<p><img src="Kmers_files/figure-html/unnamed-chunk-8-1.png" width="700"></p>
</div>
<div class="section level2">
<h2 id="version-information">5. Version information<a class="anchor" aria-label="anchor" href="#version-information"></a>
</h2>
<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html" class="external-link">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
<pre><code><span><span class="co">## R version 4.2.1 (2022-06-23 ucrt)</span></span>
<span><span class="co">## Platform: x86_64-w64-mingw32/x64 (64-bit)</span></span>
<span><span class="co">## Running under: Windows 10 x64 (build 19044)</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## Matrix products: default</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## locale:</span></span>
<span><span class="co">## [1] LC_COLLATE=German_Germany.utf8  LC_CTYPE=German_Germany.utf8   </span></span>
<span><span class="co">## [3] LC_MONETARY=German_Germany.utf8 LC_NUMERIC=C                   </span></span>
<span><span class="co">## [5] LC_TIME=German_Germany.utf8    </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## attached base packages:</span></span>
<span><span class="co">## [1] stats     graphics  grDevices utils     datasets  methods   base     </span></span>
<span><span class="co">## </span></span>
<span><span class="co">## other attached packages:</span></span>
<span><span class="co">##  [1] Platypus_3.4.1  forcats_0.5.1   stringr_1.4.0   dplyr_1.0.9    </span></span>
<span><span class="co">##  [5] purrr_0.3.4     readr_2.1.2     tidyr_1.2.0     tibble_3.1.8   </span></span>
<span><span class="co">##  [9] ggplot2_3.3.6   tidyverse_1.3.2</span></span>
<span><span class="co">## </span></span>
<span><span class="co">## loaded via a namespace (and not attached):</span></span>
<span><span class="co">##   [1] nlme_3.1-157           bitops_1.0-7           fs_1.5.2              </span></span>
<span><span class="co">##   [4] ggtree_3.4.1           lubridate_1.8.0        httr_1.4.3            </span></span>
<span><span class="co">##   [7] GenomeInfoDb_1.32.2    rprojroot_2.0.3        tools_4.2.1           </span></span>
<span><span class="co">##  [10] backports_1.4.1        bslib_0.4.0            utf8_1.2.2            </span></span>
<span><span class="co">##  [13] R6_2.5.1               BiocGenerics_0.42.0    DBI_1.1.3             </span></span>
<span><span class="co">##  [16] lazyeval_0.2.2         colorspace_2.0-3       withr_2.5.0           </span></span>
<span><span class="co">##  [19] tidyselect_1.1.2       curl_4.3.2             compiler_4.2.1        </span></span>
<span><span class="co">##  [22] textshaping_0.3.6      cli_3.3.0              rvest_1.0.2           </span></span>
<span><span class="co">##  [25] xml2_1.3.3             desc_1.4.1             labeling_0.4.2        </span></span>
<span><span class="co">##  [28] sass_0.4.2             phylogram_2.1.0        scales_1.2.0          </span></span>
<span><span class="co">##  [31] ggridges_0.5.3         pkgdown_2.0.6          systemfonts_1.0.4     </span></span>
<span><span class="co">##  [34] digest_0.6.29          yulab.utils_0.0.5      rmarkdown_2.14        </span></span>
<span><span class="co">##  [37] XVector_0.36.0         pkgconfig_2.0.3        htmltools_0.5.3       </span></span>
<span><span class="co">##  [40] highr_0.9              dbplyr_2.2.1           fastmap_1.1.0         </span></span>
<span><span class="co">##  [43] rlang_1.0.4            readxl_1.4.0           rstudioapi_0.13       </span></span>
<span><span class="co">##  [46] farver_2.1.1           gridGraphics_0.5-1     jquerylib_0.1.4       </span></span>
<span><span class="co">##  [49] generics_0.1.3         jsonlite_1.8.0         googlesheets4_1.0.0   </span></span>
<span><span class="co">##  [52] RCurl_1.98-1.7         magrittr_2.0.3         GenomeInfoDbData_1.2.8</span></span>
<span><span class="co">##  [55] ggplotify_0.1.0        patchwork_1.1.1        S4Vectors_0.34.0      </span></span>
<span><span class="co">##  [58] Rcpp_1.0.9             munsell_0.5.0          fansi_1.0.3           </span></span>
<span><span class="co">##  [61] ape_5.6-2              lifecycle_1.0.1        stringi_1.7.8         </span></span>
<span><span class="co">##  [64] yaml_2.3.5             zlibbioc_1.42.0        plyr_1.8.7            </span></span>
<span><span class="co">##  [67] ggseqlogo_0.1          grid_4.2.1             parallel_4.2.1        </span></span>
<span><span class="co">##  [70] crayon_1.5.1           lattice_0.20-45        Biostrings_2.64.0     </span></span>
<span><span class="co">##  [73] haven_2.5.0            hms_1.1.1              knitr_1.39            </span></span>
<span><span class="co">##  [76] pillar_1.8.0           stats4_4.2.1           reprex_2.0.1          </span></span>
<span><span class="co">##  [79] glue_1.6.2             evaluate_0.15          ggfun_0.0.6           </span></span>
<span><span class="co">##  [82] kmer_1.1.2             modelr_0.1.8           vctrs_0.4.1           </span></span>
<span><span class="co">##  [85] treeio_1.20.1          tzdb_0.3.0             cellranger_1.1.0      </span></span>
<span><span class="co">##  [88] gtable_0.3.0           assertthat_0.2.1       cachem_1.0.6          </span></span>
<span><span class="co">##  [91] xfun_0.31              broom_1.0.0            tidytree_0.3.9        </span></span>
<span><span class="co">##  [94] ragg_1.2.2             googledrive_2.0.0      gargle_1.2.0          </span></span>
<span><span class="co">##  [97] aplot_0.1.6            IRanges_2.30.0         memoise_2.0.1         </span></span>
<span><span class="co">## [100] ellipsis_0.3.2</span></span></code></pre>
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