Commit 52831f27 authored by Davis McCarthy's avatar Davis McCarthy
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New build of website

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......@@ -21,10 +21,10 @@
<meta name="author" content="Davis McCarthy (davisjmcc), Ruqian Lyu, PuXue Qiao, Vladimir Kiselev (wikiselev), Tallulah Andrews (talandrews), Jennifer Westoby (Jenni_Westoby), Maren Büttner (marenbuettner), Jimmy Lee (THJimmyLee), Krzysztof Polanski, Sebastian Y. Müller, Elo Madissoon, Stephane Ballereau, Maria Do Nascimento Lopes Primo, Rocio Martinez Nunez and Martin Hemberg (m_hemberg)" />
<meta name="author" content="Ruqian Lyu, PuXue Qiao, and Davis J. McCarthy (davisjmcc)" />
<meta name="date" content="2019-10-01" />
<meta name="date" content="2019-10-02" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="apple-mobile-web-app-capable" content="yes" />
......@@ -381,7 +381,7 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
<li class="chapter" data-level="10" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html"><i class="fa fa-check"></i><b>10</b> Clustering and cell annotation</a><ul>
<li class="chapter" data-level="10.1" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#clustering-methods"><i class="fa fa-check"></i><b>10.1</b> Clustering Methods</a><ul>
<li class="chapter" data-level="10.1.1" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#introduction-7"><i class="fa fa-check"></i><b>10.1.1</b> Introduction</a></li>
<li class="chapter" data-level="10.1.2" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#unsupervised-clustering-methods"><i class="fa fa-check"></i><b>10.1.2</b> unsupervised Clustering methods</a></li>
<li class="chapter" data-level="10.1.2" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#unsupervised-clustering-methods"><i class="fa fa-check"></i><b>10.1.2</b> Unsupervised clustering methods</a></li>
</ul></li>
<li class="chapter" data-level="10.2" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#clust-methods"><i class="fa fa-check"></i><b>10.2</b> Clustering example</a><ul>
<li class="chapter" data-level="10.2.1" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#example-1.-graph-based-clustering-deng-dataset"><i class="fa fa-check"></i><b>10.2.1</b> Example 1. Graph-based clustering (deng dataset)</a></li>
......@@ -395,19 +395,24 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
</ul></li>
</ul></li>
<li class="chapter" data-level="11" data-path="trajectory-inference.html"><a href="trajectory-inference.html"><i class="fa fa-check"></i><b>11</b> Trajectory inference</a><ul>
<li class="chapter" data-level="11.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#first-look-at-deng-data"><i class="fa fa-check"></i><b>11.1</b> First look at Deng data</a><ul>
<li class="chapter" data-level="11.1.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#tscan"><i class="fa fa-check"></i><b>11.1.1</b> TSCAN</a></li>
<li class="chapter" data-level="11.1.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#slingshot"><i class="fa fa-check"></i><b>11.1.2</b> Slingshot</a></li>
<li class="chapter" data-level="11.1.3" data-path="trajectory-inference.html"><a href="trajectory-inference.html#gam-general-additive-model-for-identifying-temporally-expressed-genes"><i class="fa fa-check"></i><b>11.1.3</b> GAM general additive model for identifying temporally expressed genes</a></li>
<li class="chapter" data-level="11.1.4" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle"><i class="fa fa-check"></i><b>11.1.4</b> Monocle</a></li>
<li class="chapter" data-level="11.1.5" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle-2"><i class="fa fa-check"></i><b>11.1.5</b> Monocle 2</a></li>
<li class="chapter" data-level="11.1.6" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle-3"><i class="fa fa-check"></i><b>11.1.6</b> Monocle 3</a></li>
<li class="chapter" data-level="11.1.7" data-path="trajectory-inference.html"><a href="trajectory-inference.html#diffusion-maps"><i class="fa fa-check"></i><b>11.1.7</b> Diffusion maps</a></li>
<li class="chapter" data-level="11.1.8" data-path="trajectory-inference.html"><a href="trajectory-inference.html#other-methods"><i class="fa fa-check"></i><b>11.1.8</b> Other methods</a></li>
<li class="chapter" data-level="11.1.9" data-path="trajectory-inference.html"><a href="trajectory-inference.html#comparison-of-the-methods"><i class="fa fa-check"></i><b>11.1.9</b> Comparison of the methods</a></li>
<li class="chapter" data-level="11.1.10" data-path="trajectory-inference.html"><a href="trajectory-inference.html#expression-of-genes-through-time"><i class="fa fa-check"></i><b>11.1.10</b> Expression of genes through time</a></li>
<li class="chapter" data-level="11.1.11" data-path="trajectory-inference.html"><a href="trajectory-inference.html#dynverse"><i class="fa fa-check"></i><b>11.1.11</b> dynverse</a></li>
<li class="chapter" data-level="11.1.12" data-path="trajectory-inference.html"><a href="trajectory-inference.html#sessioninfo-7"><i class="fa fa-check"></i><b>11.1.12</b> sessionInfo()</a></li>
<li class="chapter" data-level="11.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#first-look-at-deng-data"><i class="fa fa-check"></i><b>11.1</b> First look at Deng data</a></li>
<li class="chapter" data-level="11.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#tscan"><i class="fa fa-check"></i><b>11.2</b> TSCAN</a></li>
<li class="chapter" data-level="11.3" data-path="trajectory-inference.html"><a href="trajectory-inference.html#slingshot"><i class="fa fa-check"></i><b>11.3</b> Slingshot</a><ul>
<li class="chapter" data-level="11.3.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#gam-general-additive-model-for-identifying-temporally-expressed-genes"><i class="fa fa-check"></i><b>11.3.1</b> GAM general additive model for identifying temporally expressed genes</a></li>
</ul></li>
<li class="chapter" data-level="11.4" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle"><i class="fa fa-check"></i><b>11.4</b> Monocle</a><ul>
<li class="chapter" data-level="11.4.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle-2"><i class="fa fa-check"></i><b>11.4.1</b> Monocle 2</a></li>
<li class="chapter" data-level="11.4.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle-3"><i class="fa fa-check"></i><b>11.4.2</b> Monocle 3</a></li>
<li class="chapter" data-level="11.4.3" data-path="trajectory-inference.html"><a href="trajectory-inference.html#diffusion-maps"><i class="fa fa-check"></i><b>11.4.3</b> Diffusion maps</a></li>
</ul></li>
<li class="chapter" data-level="11.5" data-path="trajectory-inference.html"><a href="trajectory-inference.html#other-methods"><i class="fa fa-check"></i><b>11.5</b> Other methods</a><ul>
<li class="chapter" data-level="11.5.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#slicer"><i class="fa fa-check"></i><b>11.5.1</b> SLICER</a></li>
<li class="chapter" data-level="11.5.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#ouija"><i class="fa fa-check"></i><b>11.5.2</b> Ouija</a></li>
</ul></li>
<li class="chapter" data-level="11.6" data-path="trajectory-inference.html"><a href="trajectory-inference.html#comparison-of-the-methods"><i class="fa fa-check"></i><b>11.6</b> Comparison of the methods</a></li>
<li class="chapter" data-level="11.7" data-path="trajectory-inference.html"><a href="trajectory-inference.html#expression-of-genes-through-time"><i class="fa fa-check"></i><b>11.7</b> Expression of genes through time</a><ul>
<li class="chapter" data-level="11.7.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#dynverse"><i class="fa fa-check"></i><b>11.7.1</b> dynverse</a></li>
<li class="chapter" data-level="11.7.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#sessioninfo-7"><i class="fa fa-check"></i><b>11.7.2</b> sessionInfo()</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="12" data-path="dechapter.html"><a href="dechapter.html"><i class="fa fa-check"></i><b>12</b> Differential Expression (DE) analysis</a><ul>
......@@ -415,7 +420,8 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
<li class="chapter" data-level="12.1.1" data-path="dechapter.html"><a href="dechapter.html#bulk-rna-seq-1"><i class="fa fa-check"></i><b>12.1.1</b> Bulk RNA-seq</a></li>
<li class="chapter" data-level="12.1.2" data-path="dechapter.html"><a href="dechapter.html#single-cell-rna-seq"><i class="fa fa-check"></i><b>12.1.2</b> Single cell RNA-seq</a></li>
<li class="chapter" data-level="12.1.3" data-path="dechapter.html"><a href="dechapter.html#differences-in-distribution"><i class="fa fa-check"></i><b>12.1.3</b> Differences in Distribution</a></li>
<li class="chapter" data-level="12.1.4" data-path="dechapter.html"><a href="dechapter.html#models-of-single-cell-rnaseq-data"><i class="fa fa-check"></i><b>12.1.4</b> Models of single-cell RNASeq data</a></li>
<li class="chapter" data-level="12.1.4" data-path="dechapter.html"><a href="dechapter.html#benchmarking-of-de-methods-for-scrna-seq-data"><i class="fa fa-check"></i><b>12.1.4</b> Benchmarking of DE methods for scRNA-seq data</a></li>
<li class="chapter" data-level="12.1.5" data-path="dechapter.html"><a href="dechapter.html#models-of-single-cell-rna-seq-data"><i class="fa fa-check"></i><b>12.1.5</b> Models of single-cell RNA-seq data</a></li>
</ul></li>
<li class="chapter" data-level="12.2" data-path="dechapter.html"><a href="dechapter.html#de-in-a-real-dataset"><i class="fa fa-check"></i><b>12.2</b> DE in a real dataset</a><ul>
<li class="chapter" data-level="12.2.1" data-path="dechapter.html"><a href="dechapter.html#introduction-8"><i class="fa fa-check"></i><b>12.2.1</b> Introduction</a></li>
......@@ -547,20 +553,20 @@ the Salmon index that was used for the quantification).</p>
<p>Here we will show you how to create an <code>SCE</code> from a <code>MultiAssayExperiment</code>
object. For example, if you download <code>Shalek2013</code> dataset you will be able to
create an <code>SCE</code> using the following code:</p>
<div class="sourceCode" id="cb904"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb904-1" data-line-number="1"><span class="kw">library</span>(MultiAssayExperiment)</a>
<a class="sourceLine" id="cb904-2" data-line-number="2"><span class="kw">library</span>(SummarizedExperiment)</a>
<a class="sourceLine" id="cb904-3" data-line-number="3"><span class="kw">library</span>(scater)</a>
<a class="sourceLine" id="cb904-4" data-line-number="4">d &lt;-<span class="st"> </span><span class="kw">readRDS</span>(<span class="st">&quot;~/Desktop/GSE41265.rds&quot;</span>)</a>
<a class="sourceLine" id="cb904-5" data-line-number="5">cts &lt;-<span class="st"> </span><span class="kw">assays</span>(<span class="kw">experiments</span>(d)[[<span class="st">&quot;gene&quot;</span>]])[[<span class="st">&quot;count_lstpm&quot;</span>]]</a>
<a class="sourceLine" id="cb904-6" data-line-number="6">tpms &lt;-<span class="st"> </span><span class="kw">assays</span>(<span class="kw">experiments</span>(d)[[<span class="st">&quot;gene&quot;</span>]])[[<span class="st">&quot;TPM&quot;</span>]]</a>
<a class="sourceLine" id="cb904-7" data-line-number="7">phn &lt;-<span class="st"> </span><span class="kw">colData</span>(d)</a>
<a class="sourceLine" id="cb904-8" data-line-number="8">sce &lt;-<span class="st"> </span><span class="kw">SingleCellExperiment</span>(</a>
<a class="sourceLine" id="cb904-9" data-line-number="9"> <span class="dt">assays =</span> <span class="kw">list</span>(</a>
<a class="sourceLine" id="cb904-10" data-line-number="10"> <span class="dt">countData =</span> cts, </a>
<a class="sourceLine" id="cb904-11" data-line-number="11"> <span class="dt">tpmData =</span> tpms</a>
<a class="sourceLine" id="cb904-12" data-line-number="12"> ),</a>
<a class="sourceLine" id="cb904-13" data-line-number="13"> <span class="dt">colData =</span> phn</a>
<a class="sourceLine" id="cb904-14" data-line-number="14">)</a></code></pre></div>
<div class="sourceCode" id="cb675"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb675-1" data-line-number="1"><span class="kw">library</span>(MultiAssayExperiment)</a>
<a class="sourceLine" id="cb675-2" data-line-number="2"><span class="kw">library</span>(SummarizedExperiment)</a>
<a class="sourceLine" id="cb675-3" data-line-number="3"><span class="kw">library</span>(scater)</a>
<a class="sourceLine" id="cb675-4" data-line-number="4">d &lt;-<span class="st"> </span><span class="kw">readRDS</span>(<span class="st">&quot;~/Desktop/GSE41265.rds&quot;</span>)</a>
<a class="sourceLine" id="cb675-5" data-line-number="5">cts &lt;-<span class="st"> </span><span class="kw">assays</span>(<span class="kw">experiments</span>(d)[[<span class="st">&quot;gene&quot;</span>]])[[<span class="st">&quot;count_lstpm&quot;</span>]]</a>
<a class="sourceLine" id="cb675-6" data-line-number="6">tpms &lt;-<span class="st"> </span><span class="kw">assays</span>(<span class="kw">experiments</span>(d)[[<span class="st">&quot;gene&quot;</span>]])[[<span class="st">&quot;TPM&quot;</span>]]</a>
<a class="sourceLine" id="cb675-7" data-line-number="7">phn &lt;-<span class="st"> </span><span class="kw">colData</span>(d)</a>
<a class="sourceLine" id="cb675-8" data-line-number="8">sce &lt;-<span class="st"> </span><span class="kw">SingleCellExperiment</span>(</a>
<a class="sourceLine" id="cb675-9" data-line-number="9"> <span class="dt">assays =</span> <span class="kw">list</span>(</a>
<a class="sourceLine" id="cb675-10" data-line-number="10"> <span class="dt">countData =</span> cts, </a>
<a class="sourceLine" id="cb675-11" data-line-number="11"> <span class="dt">tpmData =</span> tpms</a>
<a class="sourceLine" id="cb675-12" data-line-number="12"> ),</a>
<a class="sourceLine" id="cb675-13" data-line-number="13"> <span class="dt">colData =</span> phn</a>
<a class="sourceLine" id="cb675-14" data-line-number="14">)</a></code></pre></div>
<p>You can also see that several different QC metrics have already been
pre-calculated on the <a href="http://imlspenticton.uzh.ch:3838/conquer/">conquer</a>
website.</p>
......
......@@ -43,7 +43,7 @@ that it is typically much easier to visualize the data in a 2 or
* Scalability: in the last few years the number of cells in scRNA-seq experiments has grown by several orders of magnitude from ~$10^2$ to ~$10^6$
### unsupervised Clustering methods
### Unsupervised clustering methods
Three main ingredients of a complete clustering method:
......@@ -206,7 +206,7 @@ that are very fast, although not the most accurate approaches.
#### Concensus clustering (more robustness, less computational speed)
#### Consensus clustering (more robustness, less computational speed)
##### __Motivation (Two problems of $K$-means)__: \
- __Problem1:__ sensitive to initial partitions \
......@@ -216,7 +216,7 @@ that are very fast, although not the most accurate approaches.
&nbsp; __Solution:__
&nbsp; Run $K$-means with a range of $K$'s.
##### __Algorithm of concensus clustering (simpliest version)__:
##### __Algorithm of consensus clustering (simpliest version)__:
```text
for(k in the range of K){
......@@ -250,7 +250,7 @@ Say we partitioned four data points into 2 clusters.
<center>![](figures/concensus1.png){width=60%}</center>
- __Step2:__ Concensus matrix: \
- __Step2:__ Consensus matrix: \
Average of all the partitions
<center>![](figures/concensus2.png){width=30%}</center>
......
This diff is collapsed.
......@@ -241,7 +241,7 @@ adjustedRandIndex(colData(deng)$cell_type2, colData(deng)$sc3_10_clusters)
```
```
## [1] 0.7796181
## [1] 0.6616899
```
__Note__ `SC3` can also be run in an interactive `Shiny` session:
......@@ -393,11 +393,11 @@ metadata(sceM)$scmap_cell_index$subclusters[1:5,1:5]
```
## D28.1_1 D28.1_13 D28.1_15 D28.1_17 D28.1_2
## [1,] 6 11 7 38 36
## [2,] 1 16 17 44 38
## [3,] 28 17 4 45 25
## [4,] 43 41 40 33 22
## [5,] 36 27 29 11 35
## [1,] 37 25 29 19 30
## [2,] 44 26 41 12 20
## [3,] 27 17 43 43 41
## [4,] 12 31 13 8 11
## [5,] 20 18 16 15 1
```
......
......@@ -9,147 +9,11 @@ knit: bookdown::preview_chapter
```
## Warning in .get_all_sf_sets(object): spike-in set 'ERCC' should have its
## own size factors
```
```
## Warning in .get_all_sf_sets(object): spike-in set 'MT' should have its own
## size factors
```
<div class="figure" style="text-align: center">
<img src="confounders-reads_files/figure-html/confound-pca-reads-1.png" alt="PCA plot of the tung data" width="90%" />
<p class="caption">(\#fig:confound-pca-reads)PCA plot of the tung data</p>
</div>
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'is_cell_control' with fewer than 2 unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_100_features_feature_control' with fewer than 2
## unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_200_features_feature_control' with fewer than 2
## unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_500_features_feature_control' with fewer than 2
## unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_50_features_ERCC' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_100_features_ERCC' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_200_features_ERCC' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_500_features_ERCC' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_50_features_MT' with fewer than 2 unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_100_features_MT' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_200_features_MT' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_500_features_MT' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'use' with fewer than 2 unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'outlier' with fewer than 2 unique levels
```
```
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
```
<div class="figure" style="text-align: center">
<img src="confounders-reads_files/figure-html/confound-find-pcs-reads-1.png" alt="PC correlation with the number of detected genes" width="90%" />
<p class="caption">(\#fig:confound-find-pcs-reads)PC correlation with the number of detected genes</p>
......
......@@ -45,19 +45,6 @@ scran-normalized log2-CPM values:
qclust <- quickCluster(umi.qc, min.size = 30, use.ranks = FALSE)
umi.qc <- computeSumFactors(umi.qc, sizes = 15, clusters = qclust)
umi.qc <- normalize(umi.qc)
```
```
## Warning in .get_all_sf_sets(object): spike-in set 'ERCC' should have its
## own size factors
```
```
## Warning in .get_all_sf_sets(object): spike-in set 'MT' should have its own
## size factors
```
```r
reducedDim(umi.qc, "PCA") <- reducedDim(
runPCA(umi.qc[endog_genes,],
exprs_values = "logcounts", ncomponents = 10), "PCA")
......@@ -91,130 +78,6 @@ each cell, so can explain all the variation for all PCs.]
plotExplanatoryPCs(umi.qc)
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'is_cell_control' with fewer than 2 unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_100_features_feature_control' with fewer than 2
## unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_200_features_feature_control' with fewer than 2
## unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_500_features_feature_control' with fewer than 2
## unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_50_features_ERCC' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_100_features_ERCC' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_200_features_ERCC' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_500_features_ERCC' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_50_features_MT' with fewer than 2 unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_100_features_MT' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_200_features_MT' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'pct_counts_in_top_500_features_MT' with fewer than 2 unique
## levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'use' with fewer than 2 unique levels
```
```
## Warning in getVarianceExplained(dummy, exprs_values = "pc_space", ...):
## ignoring 'outlier' with fewer than 2 unique levels
```
```
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning
## -Inf
```
<div class="figure" style="text-align: center">
<img src="confounders_files/figure-html/confound-find-pcs-total-features-1.png" alt="PC correlation with the number of detected genes" width="90%" />
<p class="caption">(\#fig:confound-find-pcs-total-features)PC correlation with the number of detected genes</p>
......
......@@ -21,10 +21,10 @@
<meta name="author" content="Davis McCarthy (davisjmcc), Ruqian Lyu, PuXue Qiao, Vladimir Kiselev (wikiselev), Tallulah Andrews (talandrews), Jennifer Westoby (Jenni_Westoby), Maren Büttner (marenbuettner), Jimmy Lee (THJimmyLee), Krzysztof Polanski, Sebastian Y. Müller, Elo Madissoon, Stephane Ballereau, Maria Do Nascimento Lopes Primo, Rocio Martinez Nunez and Martin Hemberg (m_hemberg)" />
<meta name="author" content="Ruqian Lyu, PuXue Qiao, and Davis J. McCarthy (davisjmcc)" />
<meta name="date" content="2019-10-01" />
<meta name="date" content="2019-10-02" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="apple-mobile-web-app-capable" content="yes" />
......@@ -381,7 +381,7 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
<li class="chapter" data-level="10" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html"><i class="fa fa-check"></i><b>10</b> Clustering and cell annotation</a><ul>
<li class="chapter" data-level="10.1" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#clustering-methods"><i class="fa fa-check"></i><b>10.1</b> Clustering Methods</a><ul>
<li class="chapter" data-level="10.1.1" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#introduction-7"><i class="fa fa-check"></i><b>10.1.1</b> Introduction</a></li>
<li class="chapter" data-level="10.1.2" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#unsupervised-clustering-methods"><i class="fa fa-check"></i><b>10.1.2</b> unsupervised Clustering methods</a></li>
<li class="chapter" data-level="10.1.2" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#unsupervised-clustering-methods"><i class="fa fa-check"></i><b>10.1.2</b> Unsupervised clustering methods</a></li>
</ul></li>
<li class="chapter" data-level="10.2" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#clust-methods"><i class="fa fa-check"></i><b>10.2</b> Clustering example</a><ul>
<li class="chapter" data-level="10.2.1" data-path="clustering-and-cell-annotation.html"><a href="clustering-and-cell-annotation.html#example-1.-graph-based-clustering-deng-dataset"><i class="fa fa-check"></i><b>10.2.1</b> Example 1. Graph-based clustering (deng dataset)</a></li>
......@@ -395,19 +395,24 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
</ul></li>
</ul></li>
<li class="chapter" data-level="11" data-path="trajectory-inference.html"><a href="trajectory-inference.html"><i class="fa fa-check"></i><b>11</b> Trajectory inference</a><ul>
<li class="chapter" data-level="11.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#first-look-at-deng-data"><i class="fa fa-check"></i><b>11.1</b> First look at Deng data</a><ul>
<li class="chapter" data-level="11.1.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#tscan"><i class="fa fa-check"></i><b>11.1.1</b> TSCAN</a></li>
<li class="chapter" data-level="11.1.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#slingshot"><i class="fa fa-check"></i><b>11.1.2</b> Slingshot</a></li>
<li class="chapter" data-level="11.1.3" data-path="trajectory-inference.html"><a href="trajectory-inference.html#gam-general-additive-model-for-identifying-temporally-expressed-genes"><i class="fa fa-check"></i><b>11.1.3</b> GAM general additive model for identifying temporally expressed genes</a></li>
<li class="chapter" data-level="11.1.4" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle"><i class="fa fa-check"></i><b>11.1.4</b> Monocle</a></li>
<li class="chapter" data-level="11.1.5" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle-2"><i class="fa fa-check"></i><b>11.1.5</b> Monocle 2</a></li>
<li class="chapter" data-level="11.1.6" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle-3"><i class="fa fa-check"></i><b>11.1.6</b> Monocle 3</a></li>
<li class="chapter" data-level="11.1.7" data-path="trajectory-inference.html"><a href="trajectory-inference.html#diffusion-maps"><i class="fa fa-check"></i><b>11.1.7</b> Diffusion maps</a></li>
<li class="chapter" data-level="11.1.8" data-path="trajectory-inference.html"><a href="trajectory-inference.html#other-methods"><i class="fa fa-check"></i><b>11.1.8</b> Other methods</a></li>
<li class="chapter" data-level="11.1.9" data-path="trajectory-inference.html"><a href="trajectory-inference.html#comparison-of-the-methods"><i class="fa fa-check"></i><b>11.1.9</b> Comparison of the methods</a></li>
<li class="chapter" data-level="11.1.10" data-path="trajectory-inference.html"><a href="trajectory-inference.html#expression-of-genes-through-time"><i class="fa fa-check"></i><b>11.1.10</b> Expression of genes through time</a></li>
<li class="chapter" data-level="11.1.11" data-path="trajectory-inference.html"><a href="trajectory-inference.html#dynverse"><i class="fa fa-check"></i><b>11.1.11</b> dynverse</a></li>
<li class="chapter" data-level="11.1.12" data-path="trajectory-inference.html"><a href="trajectory-inference.html#sessioninfo-7"><i class="fa fa-check"></i><b>11.1.12</b> sessionInfo()</a></li>
<li class="chapter" data-level="11.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#first-look-at-deng-data"><i class="fa fa-check"></i><b>11.1</b> First look at Deng data</a></li>
<li class="chapter" data-level="11.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#tscan"><i class="fa fa-check"></i><b>11.2</b> TSCAN</a></li>
<li class="chapter" data-level="11.3" data-path="trajectory-inference.html"><a href="trajectory-inference.html#slingshot"><i class="fa fa-check"></i><b>11.3</b> Slingshot</a><ul>
<li class="chapter" data-level="11.3.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#gam-general-additive-model-for-identifying-temporally-expressed-genes"><i class="fa fa-check"></i><b>11.3.1</b> GAM general additive model for identifying temporally expressed genes</a></li>
</ul></li>
<li class="chapter" data-level="11.4" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle"><i class="fa fa-check"></i><b>11.4</b> Monocle</a><ul>
<li class="chapter" data-level="11.4.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle-2"><i class="fa fa-check"></i><b>11.4.1</b> Monocle 2</a></li>
<li class="chapter" data-level="11.4.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#monocle-3"><i class="fa fa-check"></i><b>11.4.2</b> Monocle 3</a></li>
<li class="chapter" data-level="11.4.3" data-path="trajectory-inference.html"><a href="trajectory-inference.html#diffusion-maps"><i class="fa fa-check"></i><b>11.4.3</b> Diffusion maps</a></li>
</ul></li>
<li class="chapter" data-level="11.5" data-path="trajectory-inference.html"><a href="trajectory-inference.html#other-methods"><i class="fa fa-check"></i><b>11.5</b> Other methods</a><ul>
<li class="chapter" data-level="11.5.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#slicer"><i class="fa fa-check"></i><b>11.5.1</b> SLICER</a></li>
<li class="chapter" data-level="11.5.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#ouija"><i class="fa fa-check"></i><b>11.5.2</b> Ouija</a></li>
</ul></li>
<li class="chapter" data-level="11.6" data-path="trajectory-inference.html"><a href="trajectory-inference.html#comparison-of-the-methods"><i class="fa fa-check"></i><b>11.6</b> Comparison of the methods</a></li>
<li class="chapter" data-level="11.7" data-path="trajectory-inference.html"><a href="trajectory-inference.html#expression-of-genes-through-time"><i class="fa fa-check"></i><b>11.7</b> Expression of genes through time</a><ul>
<li class="chapter" data-level="11.7.1" data-path="trajectory-inference.html"><a href="trajectory-inference.html#dynverse"><i class="fa fa-check"></i><b>11.7.1</b> dynverse</a></li>
<li class="chapter" data-level="11.7.2" data-path="trajectory-inference.html"><a href="trajectory-inference.html#sessioninfo-7"><i class="fa fa-check"></i><b>11.7.2</b> sessionInfo()</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="12" data-path="dechapter.html"><a href="dechapter.html"><i class="fa fa-check"></i><b>12</b> Differential Expression (DE) analysis</a><ul>
......@@ -415,7 +420,8 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
<li class="chapter" data-level="12.1.1" data-path="dechapter.html"><a href="dechapter.html#bulk-rna-seq-1"><i class="fa fa-check"></i><b>12.1.1</b> Bulk RNA-seq</a></li>
<li class="chapter" data-level="12.1.2" data-path="dechapter.html"><a href="dechapter.html#single-cell-rna-seq"><i class="fa fa-check"></i><b>12.1.2</b> Single cell RNA-seq</a></li>
<li class="chapter" data-level="12.1.3" data-path="dechapter.html"><a href="dechapter.html#differences-in-distribution"><i class="fa fa-check"></i><b>12.1.3</b> Differences in Distribution</a></li>
<li class="chapter" data-level="12.1.4" data-path="dechapter.html"><a href="dechapter.html#models-of-single-cell-rnaseq-data"><i class="fa fa-check"></i><b>12.1.4</b> Models of single-cell RNASeq data</a></li>
<li class="chapter" data-level="12.1.4" data-path="dechapter.html"><a href="dechapter.html#benchmarking-of-de-methods-for-scrna-seq-data"><i class="fa fa-check"></i><b>12.1.4</b> Benchmarking of DE methods for scRNA-seq data</a></li>
<li class="chapter" data-level="12.1.5" data-path="dechapter.html"><a href="dechapter.html#models-of-single-cell-rna-seq-data"><i class="fa fa-check"></i><b>12.1.5</b> Models of single-cell RNA-seq data</a></li>
</ul></li>
<li class="chapter" data-level="12.2" data-path="dechapter.html"><a href="dechapter.html#de-in-a-real-dataset"><i class="fa fa-check"></i><b>12.2</b> DE in a real dataset</a><ul>
<li class="chapter" data-level="12.2.1" data-path="dechapter.html"><a href="dechapter.html#introduction-8"><i class="fa fa-check"></i><b>12.2.1</b> Introduction</a></li>
......
......@@ -10,38 +10,101 @@ output: html_document
### Bulk RNA-seq
One of the most common types of analyses when working with bulk RNA-seq
data is to identify differentially expressed genes. By comparing the
genes that change between two conditions, e.g. mutant and wild-type or
stimulated and unstimulated, it is possible to characterize the
molecular mechanisms underlying the change.
Several different methods,
e.g. [DESeq2](https://bioconductor.org/packages/DESeq2) and
[edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html),
have been developed for bulk RNA-seq. Moreover, there are also
extensive
One of the most common types of analyses when working with bulk RNA-seq data is
to identify differentially expressed genes. By comparing the genes that change
between two or more conditions, e.g. mutant and wild-type or stimulated and
unstimulated, it is possible to characterize the molecular mechanisms underlying
the change.
Several different methods, e.g.
[edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html) and
[DESeq2](https://bioconductor.org/packages/DESeq2) and more, have been developed
for bulk RNA-seq and become established as parts of robust and widely-used
analysis workflows. Moreover, there are also extensive
[datasets](http://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-9-r95)
available where the RNA-seq data has been