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@alexrunqin
alexrunqin committed Dec 19, 2024
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2 changes: 1 addition & 1 deletion .nojekyll
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43 changes: 22 additions & 21 deletions 01-processing.html
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Expand Up @@ -404,7 +404,7 @@ <h1 class="title">
<span></span>
<span><span class="co"># assign metadata columns</span></span>
<span><span class="fu">mcols</span><span class="op">(</span><span class="va">images</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">S4Vectors</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/S4Vectors/man/DataFrame-class.html">DataFrame</a></span><span class="op">(</span>imageID <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/names.html">names</a></span><span class="op">(</span><span class="va">images</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>Time for this code chunk to run with 40 cores: 73.99 seconds</p>
<p>Time for this code chunk to run with 40 cores: 69.52 seconds</p>
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<p>When reading the image channels directly from the names of the TIFF images, they will often need to be cleaned for ease of downstream processing. The channel names can be accessed from the <code>CytoImageList</code> object using the <code>channelNames</code> function.</p>
<div class="cell">
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<span> tissue <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"panCK"</span>, <span class="st">"CD45"</span>, <span class="st">"HH3"</span><span class="op">)</span>,</span>
<span> cores <span class="op">=</span> <span class="va">nCores</span></span>
<span> <span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<p>Time for this code chunk to run with 40 cores: 44.22 seconds</p>
<p>Time for this code chunk to run with 40 cores: 44.47 seconds</p>
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<section id="visualise-separation" class="level3" data-number="4.2.1"><h3 data-number="4.2.1" class="anchored" data-anchor-id="visualise-separation">
<span class="header-section-number">4.2.1</span> Visualise separation</h3>
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spatialCoords names(2) : x y
imgData names(1): sample_id</code></pre>
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<p>Time for this code chunk to run with 40 cores: 73.95 seconds</p>
<p>Time for this code chunk to run with 40 cores: 74.32 seconds</p>
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<p>So far, we have obtained our raw TIFF images, performed cell segmentation to isolate individual cells, and then stored our data as a <code>SpatialExperiment</code> object. We can now move on to quality control, data transformation, and normalisation to address batch effects.</p>
</section><section id="sessioninfo" class="level2" data-number="4.4"><h2 data-number="4.4" class="anchored" data-anchor-id="sessioninfo">
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[43] R6_2.5.1 RColorBrewer_1.1-3 spatstat.data_3.1-4
[46] spatstat.univar_3.1-1 Rcpp_1.0.13-1 knitr_1.49
[49] httpuv_1.6.15 Matrix_1.7-1 nnls_1.6
[52] tidyselect_1.2.1 yaml_2.3.10 abind_1.4-8
[55] viridis_0.6.5 codetools_0.2-20 curl_6.0.1
[58] lattice_0.22-6 tibble_3.2.1 KEGGREST_1.46.0
[61] shiny_1.10.0 withr_3.0.2 evaluate_1.0.1
[64] polyclip_1.10-7 Biostrings_2.74.0 filelock_1.0.3
[67] BiocManager_1.30.25 pillar_1.9.0 generics_0.1.3
[70] sp_2.1-4 RCurl_1.98-1.16 BiocVersion_3.20.0
[73] munsell_0.5.1 scales_1.3.0 xtable_1.8-4
[76] glue_1.8.0 tools_4.4.1 locfit_1.5-9.10
[79] rhdf5_2.50.1 grid_4.4.1 AnnotationDbi_1.68.0
[82] colorspace_2.1-1 GenomeInfoDbData_1.2.13 raster_3.6-30
[85] beeswarm_0.4.0 HDF5Array_1.34.0 vipor_0.4.7
[88] cli_3.6.3 rappdirs_0.3.3 fansi_1.0.6
[91] S4Arrays_1.6.0 viridisLite_0.4.2 svglite_2.1.3
[94] dplyr_1.1.4 gtable_0.3.6 digest_0.6.37
[97] SparseArray_1.6.0 rjson_0.2.23 htmlwidgets_1.6.4
[100] memoise_2.0.1 htmltools_0.5.8.1 lifecycle_1.0.4
[103] httr_1.4.7 mime_0.12 bit64_4.5.2 </code></pre>
[52] tidyselect_1.2.1 yaml_2.3.10 rstudioapi_0.17.1
[55] abind_1.4-8 viridis_0.6.5 codetools_0.2-20
[58] curl_6.0.1 lattice_0.22-6 tibble_3.2.1
[61] KEGGREST_1.46.0 shiny_1.10.0 withr_3.0.2
[64] evaluate_1.0.1 polyclip_1.10-7 Biostrings_2.74.0
[67] filelock_1.0.3 BiocManager_1.30.25 pillar_1.9.0
[70] generics_0.1.3 sp_2.1-4 RCurl_1.98-1.16
[73] BiocVersion_3.20.0 munsell_0.5.1 scales_1.3.0
[76] xtable_1.8-4 glue_1.8.0 tools_4.4.1
[79] locfit_1.5-9.10 rhdf5_2.50.1 grid_4.4.1
[82] AnnotationDbi_1.68.0 colorspace_2.1-1 GenomeInfoDbData_1.2.13
[85] raster_3.6-30 beeswarm_0.4.0 HDF5Array_1.34.0
[88] vipor_0.4.7 cli_3.6.3 rappdirs_0.3.3
[91] fansi_1.0.6 S4Arrays_1.6.0 viridisLite_0.4.2
[94] svglite_2.1.3 dplyr_1.1.4 gtable_0.3.6
[97] digest_0.6.37 SparseArray_1.6.0 rjson_0.2.23
[100] htmlwidgets_1.6.4 memoise_2.0.1 htmltools_0.5.8.1
[103] lifecycle_1.0.4 httr_1.4.7 mime_0.12
[106] bit64_4.5.2 </code></pre>
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2 changes: 1 addition & 1 deletion 02-quality_control.html
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Expand Up @@ -411,7 +411,7 @@ <h1 class="title">
<div class="callout-body-container callout-body">
<p><strong>What we’re looking for</strong></p>
<ol type="1">
<li>Do the CD3+ and CD3- peaks clearly separate out in the density plot? To ensure that downstream clustering goes smoothly, we want our cell type specific markers to show 2 distinct peaks representing our CD3+ and CD3- cells. If these</li>
<li>Do the CD3+ and CD3- peaks clearly separate out in the density plot? To ensure that downstream clustering goes smoothly, we want our cell type specific markers to show 2 distinct peaks representing our CD3+ and CD3- cells. If we see 3 or more peaks where we don’t expect, this might be an indicator that further normalization is required.</li>
<li>Are our CD3+ and CD3- peaks consistent across our images? We want to make sure that our density plots for CD3 are largely the same across images so that a CD3+ cell in 1 image is equivalent to a CD3+ cell in another image.</li>
</ol>
</div>
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16 changes: 14 additions & 2 deletions 03a-cell_annotation.html
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Expand Up @@ -445,7 +445,11 @@ <h1 class="title">
</div>
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<div class="callout-body-container callout-body">
<p><strong>How do I identify imperfect clustering?</strong> 1. Do our cell-type specific markers clearly separate out by cluster? We expect to see discrete expression of our markers in specific cell types, e.g.&nbsp;CD4 2. If we instead see “smearing” of our markers across clusters, where several clusters express high levels of a cell type specific marker such as CD4, it is likely a normalization issue.</p>
<p><strong>How do I identify imperfect clustering?</strong></p>
<ol type="1">
<li>Do our cell-type specific markers clearly separate out by cluster? We expect to see discrete expression of our markers in specific cell types, e.g.&nbsp;CD4</li>
<li>If we instead see “smearing” of our markers across clusters, where several clusters express high levels of a cell type specific marker such as CD4, it is likely a normalization issue.</li>
</ol>
</div>
</div>
<div class="callout callout-style-default callout-tip callout-titled" title="Remedying imperfect clustering">
Expand All @@ -458,7 +462,15 @@ <h1 class="title">
</div>
</div>
<div class="callout-body-container callout-body">
<p>Imperfect clustering can stem from many issues: the 3 most common are outlined below: 1. Imperfect segmentation - excessive lateral marker spill over can severely impact downstream clustering, as cell type specific markers leak into nearby cells. This should largely be diagnosed in the segmentation step and will need to be fixed by optimizing the upstream segmentation algorithm. 2. Imperfect normalization - excessively variable intensities across images could cause issues in the normalization process. This can generally be diagnosed with density plots and box plots for specific markers across images and can be fixed by identifying the exact issue, e.g.&nbsp;extremely high values for a small subset of images, and choosing a normalization strategy to remove/reduce this effect. 3. Imperfect clustering - choosing a <code>k</code> that’s too low or too high could lead to imperfect clustering. This is usually diagnosed by clusters which either express too many markers very highly or express too few markers, and is usually remedied by choosing an ideal <code>k</code> based on an elbow plot described below.</p>
<p><strong>Imperfect clustering can stem from many issues: the 3 most common are outlined below:</strong></p>
<ol type="1">
<li>
<strong>Imperfect segmentation</strong> - excessive lateral marker spill over can severely impact downstream clustering, as cell type specific markers leak into nearby cells. This should largely be diagnosed in the segmentation step and will need to be fixed by optimizing the upstream segmentation algorithm.</li>
<li>
<strong>Imperfect normalization</strong> - excessively variable intensities across images could cause issues in the normalization process. This can generally be diagnosed with density plots and box plots for specific markers across images and can be fixed by identifying the exact issue, e.g.&nbsp;extremely high values for a small subset of images, and choosing a normalization strategy to remove/reduce this effect.</li>
<li>
<strong>Imperfect clustering</strong> - choosing a <code>k</code> that’s too low or too high could lead to imperfect clustering. This is usually diagnosed by clusters which either express too many markers very highly or express too few markers, and is usually remedied by choosing an ideal <code>k</code> based on an elbow plot described below.</li>
</ol>
</div>
</div>
</section><section id="using-fusesom-to-estimate-the-number-of-clusters" class="level3" data-number="6.2.2"><h3 data-number="6.2.2" class="anchored" data-anchor-id="using-fusesom-to-estimate-the-number-of-clusters">
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