GRaNIE_singleCell_eGRNs.RmdAbstract
This newest vignette focuses on how to use the
GRaNIE package for single-cell data, the 10x multiome
in particular. This vignette will be modified, extended and
updated regularly, so stay tuned for our newest thoughts and
recommendations as we explore the applicability of the package for
the inference of single-cell eGRNs.
This vignettes summarizes our views and experiences on running
GRaNIE for 10x multiome data. While GRaNIE has
been developed originally for bulk data, it can in fact also be applied,
with particular preprocessing, to single-cell data. As many tools that
integrate single-cell data, some kind of aggregation is necessary to
reduce the scarcity of the data - ATAC in particular. While many tools
do this implicitly in their methods, somewhat hidden from the user, we
employ here a different approach: We preprocess the data manually and
feed it into GRaNIE in a pseudobulk manner so that the
original frameworks works just as well, while giving a lot of
flexibility to the user in how exactly the data preprocessing and
granularity of the data should look like. From our experience, very
often this is very question-specific and data-dependent, and no
universal solution exists that works equally well for everything.
Disclaimer: These are just recommendations here based on our (limited) experience and testing so far. We cannot guarantee this works also well for your data. Feel free to contact us for questions and feedback, we are happy for discussions and comments.
In this vignette, we pretty much follow this
Seurat vignette for the preprocessing of the RNA and ATAC data and
subsequent clustering for 10x multiome data. However,
GRaNIE is not dependent on a specific way of preprocessing
as long as the final count matrices that are used as input are
appropriate - see subsequent chapters here for details on what
appropriate means here in this context.
Thus, feel free to modify and extend the preprocessing and
RNA/ATAC integration as proposed here - let us know whether and how well
it worked, we are very happy for receiving feedback for
GRaNIE in single-cell / pseudobulk mode.
If you do not have 10x multiome data, you need to find a way
to match the ATAC and RNA data by yourself, and most of the steps in the
tutorial below may not apply. Share your experiences with us
how and whether you managed to run GRaNIE for your
data!
The first step is to create a multimodal Seurat object
with paired transcriptome and ATAC-seq profiles. For details on how to
generate it, you only need the output of, for example,
CellRanger ARC for 10x multiome experiments. You may check
various excellent tutorials such as this
Seurat vignette.
For the 10x multiome data, we next perform pre-processing and dimensional reduction on both assays independently, using standard approaches for RNA and ATAC-seq data, as summarized below:
For integrating both modalities, you can use any method you find appropriate for your data. Here, we use a weighted-nearest neighbor (WNN) analysis, an unsupervised framework to learn the relative utility of each data type in each cell. By learning cell-specific modality ‘weights’, and constructing a WNN graph that integrates the modalities, we represent a weighted combination of the RNA and ATAC-seq modalities that we can subsequently use for generating our pseudobulk samples.
In short, we run this on the Seurat object;
FindMultiModalNeighbors with
reduction.list = list("pca", "lsi"), dims.list = list(1:50, 2:50)
RunUMAPWe now have both data modalities in a reduced dimensionality representation.
After integration of both RNA and ATAC modalities, we can now perform a clustering on RNA+ATAC data on the single cell level.
For the 10x multiome, we so far used the WNN graph for UMAP visualization and subsequent clustering as described here. However, many other methods may be used and we will update this vignette with alternatives in the future.
In a nutshell, these are the functions we may run here:
FindClusters with a specific resolution
value that we usually vary between 0.1 and 20. Reasonable cluster
resolutions are data and question dependent. For recommendations, see
the notes below.AggregateExpression to aggregate counts within each
cluster to cluster pseudocounts based on their (cluster) identities.
Each cluster forms a sample as used in the GRaNIE analysis.
Importantly, we found that mean count aggregation works
better than sum count aggregation: For the former, the
resulting eGRNs from GraNIE are more stable and similar to
each other as compared to the eGRNs that come from the sum count
aggregation.We strongly suggest choosing a resolution that gives at least
20-30 clusters, as fewer clusters (or samples consequently in the
GRaNIE analysis) can cause statistical issues and artifacts
as observed in the enhancer-gene diagnostic plots for some
datasets. If the desired number of clusters is small, pay
close(r) attention to the enhancer-gene QC plots, in particular whether
the signal from the randomized connections looks flat (enough). For more
details, see the Package
Details.
On the other extreme, too many clusters will result in data
that becomes too scares - especially the ATAC-seq data suffers from
scarcity. Throughout our experiments, we have seen that when
having too many clusters (close to or more than 100, for example), the
scarcity of ATAC becomes so big that either man peaks are filtered out
in the filterData() function or that the abundance of zeros
results in very few connections in the eGRN. Thus, we advise on
an intermediate level of cluster that is also plausible biologically,
but testing different cluster resolutions is generally also a good idea
for initial exploration.
GRaNIE
Lastly, we need a few processing steps to bring both RNA and ATAC
count matrices into the required format that can be directly used by
GRaNIE. This encompasses mainly simple transformation
steps.
No special steps are needed here except for creating a
peakID column. For more details, see the example scripts we
provide and link in this document.
For RNA, one extra step is needed: We need to translate the gene
names as provided by Seurat into proper Ensembl IDs. To do
so, there are two options:
CellRanger
can be used for it. It is called features.tsv.gz or alike
and contains in total 6 columns (Ensembl ID, gene
name, set name, chr, start,
end). This can be utilized to use the Ensembl IDs in the ID
column of the RNA count matrix (default name is
ENSEMBL).hg38 and mm10 that can be used. You
can download them here.We are now all set to run GRaNIE, using the
cluster-specific count matrices for both ATAC and RNA and, optionally,
the metadata file. We usually use GRaNIE in the default
mode, as the pseudobulk data mimics bulk data close enough from our
experience.
We want to emphasize in particular to use filterData()
to filter both RNA and particularly ATAC to exclude peaks that have very
low count values. We usually use
minNormalizedMean_peaks_bulk = 5 and
minNormalizedMean_RNA_bulk = 1 although other values may be
reasonable too. Let us know what your experiences are, we are happy for
any feedback. Especially for analyses with many clusters, many peaks
have very low means from our experience with consequently low variation
- so excluding them may be a better idea than keeping them to remove
noise.
Depending on the number of clusters, GRaNIE may run a
little longer than the typical bulk analysis but from our experience not
much longer.
In our main repository for GRaNIE (outside
Bioconductor), we provide a set of scripts that can help setting up a
GRaNIE analysis for 10x multiome data. The scripts are
located here.
If you run into issues with the scripts, let us know!
GRaNIE preparation
We provide a script to takes a Seurat object with
ATAC and RNA assays as input and processes the
data according to what we described here in this vignette, performs 10
and more clustering runs for resolutions between 0.5 and 20
(user-adjustable) and produces the properly processed count and metadata
files that can be directly used as input for GRaNIE.
GRaNIE in batch mode
We also provide a script that runs GRaNIE in batch mode,
once per cluster resolution as produced in the step before. The
input is essentially a folder, along with GraNIE parameters
for a standard analysis, and the function then iterates over all input
files and performs one GraNIE analysis at a time for a
user-provided list of cluster resolutions.
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