Now published in eLife.
A re-analysis of the Single-cell transcriptomic analysis of Alzheimer’s disease using a standardised data processing and pseudobulk differential expression approach.
We used scFlow
(v0.6.1) for all of the 24 patients with Alzheimer’s disease pathology and the
24 control patients. The config file with all the parameters is available in
./scFlow_files. This approach resulted in more stringent quality control,
leading to the exclusion of more, low quality cells.
An single-cell object (SCE) of the Mathys et al. study into Single-nucleus transcriptomic analysis and differential expression (DE) of Alzheimer’s disease data after processing with scFlow is available on synapse see: https://doi.org/10.7303/syn51758062.1.
Note this includes the processed count matrix and associated metadata. This
will be needed to run the analysis below. Also note that here we match the cell
types to the original paper but we have also made available a more granular cell
type groupings which you can access in the metadata - the cluster_celltype
column rather than the allan_celltype column.
The run_reanalysis_Mathys_19.R in the R folder can be used to derive the
EGs from the reprocessed Mathys et al., 2019 Alzheimer’s disease patient
snRNA-Seq data. This script uses a custom written function to apply pseudobulk
differential analysis to any single-cell dataset (see sc_cell_type_de.R).
We also provide a docker file to create an image to rerun the analysis - removing the need of the user to install all the dependencies themselves. Simply run the following with docker installed:
docker pull neurogenomicslab/reanalysis_mathys_2019
or recreate the docker image with:
docker build -t reanalysis_mathys_2019 .
Whether you pull or recreate the image, next run it:
docker run -e PASSWORD=reanalysis --rm -p 8787:8787 reanalysis_mathys_2019
Now navigate to localhost:8787 in a web browser and log in with:
username: rstudio
password: reanalysis
to access the docker image. Then clone the repo in the terminal of Rstudio
with:
git clone https://github.com/neurogenomics/reanalysis_Mathys_2019
#install data
cd reanalysis_Mathys_2019/
wget https://figshare.com/ndownloader/files/38819949 -O ./data/sce.qs
#rerun analysis
cd R/
Rscript run_reanalysis_Mathys_19.R
Once this runs you can look in the results folder or also knit the Rmd file (Mathys_results_comparison_pb.html) to view the results.
Mathys_results_comparison_pb.html gives a comparison between the results
reported in the original publication and our findings using pseudobulk
differential expression and a standardised data processing approach. Note the
authors took cells as independent replicates, a cell-level analysis, in their
work and compared the consistency in directionality and rank of their
differentially expressed genes (DEGs) against a poisson mixed model. We show
that these DEGs are just an artefact of taking cells as independent replicates
by plotting the number of DEGs found against the cell counts. There is a strong
correlation for their results but not for the pseudobulk DEGs.
Run random_perm_pseudorep_pseudobulk_analysis.R to replicate the random permutation analysis
based on pseudoreplication and pseudobulk differential expression methods.
If you want further details or are using this work please see/cite our manuscript.