pype_from_seurat

Start cellpyping a Seurat object.
This function saves the user from building his own cellpypes object,
which is done with <code>list(umi, neighbors,embed, totalUMI)</code>.

Annotate single-cell RNA sequencing data manually based on
marker gene thresholds.
Find cell type rules (gene+threshold) through exploration,
use the popular piping operator '%>%' to reconstruct complex
cell type hierarchies.
'cellpypes' models technical noise to find positive and negative cells for
a given expression threshold and returns cell type labels or pseudobulks.
Cite this package as Frauhammer (2022) <doi:10.5281/zenodo.6555728> and
visit <https://github.com/FelixTheStudent/cellpypes> for tutorials and newest
features.

Felix Frauhammer

cellpypes

Cell Type Pipes for Single-Cell RNA Sequencing Data

pype_from_seurat function

<dl><dt>seurat</dt>
<dd>A Seurat object.</dd>
<dt>graph_name</dt>
<dd>Supply one of the graphs. To see options, type
<code>names(seurat@graphs)</code>. If left empty (<code>NULL</code>, the default),
<code>pype_from_seurat</code> will try to guess the correct name for you.</dd></dl>

Arguments

A cellpypes object is a <a href="/link/list?package=cellpypes&version=0.3.0" data-mini-rdoc="cellpypes::list">list</a> with four slots:<dl>
<dt><code>raw </code></dt>
<dd>(sparse) matrix with genes in rows, cells in columns</dd><dt><code>totalUMI </code></dt>
<dd>the colSums of obj$raw</dd><dt><code>embed </code></dt>
<dd>two-dimensional embedding of the cells, provided as data.frame
or tibble with two columns and one row per cell.</dd><dt><code>neighbors </code></dt>
<dd>index matrix with one row per cell and k columns, where
k is the number of nearest neighbors (we recommend 15&lt;k&lt;100, e.g. k=50).
Here are two ways to get the neighbors index matrix:<ul>
<li>Use <code>find_knn(featureMatrix)$idx</code>, where featureMatrix could be
principal components, latent variables or normalized genes (features in
rows, cells in columns).</li>
<li>use <code>as(seurat@graphs[["RNA_nn"]], "dgCMatrix")&gt; .1</code> to extract
the kNN
graph computed on RNA. The <code>&gt; .1</code> ensures this also works with RNA_snn,
wknn/wsnn or any other
available graph – check with <code>names(seurat@graphs)</code>.</li>
</ul>
</dd>
</dl>

cellpypes Objects

Convert Seurat to cellpypes object. — pype_from_seurat

<dl>

<dt>seurat</dt>
<dd>A Seurat object.</dd>


<dt>graph_name</dt>
<dd>Supply one of the graphs. To see options, type
<code>names(seurat@graphs)</code>. If left empty (<code>NULL</code>, the default),
<code>pype_from_seurat</code> will try to guess the correct name for you.</dd>

</dl>

A cellpypes object is a <a href='https://rdrr.io/r/base/list.html'>list</a> with four slots:<dl>
<dt><code>raw </code></dt>
<dd>(sparse) matrix with genes in rows, cells in columns</dd>

<dt><code>totalUMI </code></dt>
<dd>the colSums of obj$raw</dd>

<dt><code>embed </code></dt>
<dd>two-dimensional embedding of the cells, provided as data.frame
or tibble with two columns and one row per cell.</dd>

<dt><code>neighbors </code></dt>
<dd>index matrix with one row per cell and k columns, where
k is the number of nearest neighbors (we recommend 15&lt;k&lt;100, e.g. k=50).
Here are two ways to get the neighbors index matrix:<ul>
<li>Use <code>find_knn(featureMatrix)$idx</code>, where featureMatrix could be
principal components, latent variables or normalized genes (features in
rows, cells in columns).</li>
<li>use <code>as(seurat@graphs[["RNA_nn"]], "dgCMatrix")&gt; .1</code> to extract
the kNN
graph computed on RNA. The <code>&gt; .1</code> ensures this also works with RNA_snn,
wknn/wsnn or any other
available graph – check with <code>names(seurat@graphs)</code>.</li>
</ul>
</dd>


</dl>

pype_from_seurat: Convert Seurat to cellpypes object.

Description

Usage

Value

Arguments

cellpypes Objects