Standard analysis track: Midbrain dataset

Contents

• Introduction
• Downloading the midbrain dataset
• Installation
  • scrnabox.slurm installation
  • CellRanger installation
  • R library preparation and R package installation
• scRNAbox: Standard Analysis Track
  • Step 0: Set up
  • Step 1: FASTQ to gene expression matrix
  • Step 2: Create Seurat object and remove ambient RNA
  • Step 3: Quality control and filtering
  • Step 4: Doublet removal
  • Step 5: Integration
  • Step 6: Clustering
  • Step 7: Cluster annotation
    • Tool 1: Cluster marker GSEA
    • Tool 2: Expression profiling of known marker genes
    • Tool 3: Reference-based annotation
    • Annotate
  • Step 8: Differential gene expression
    • Add metadata
    • Cell-based DGE using all cells
    • Cell-based DGE using cell type groups
    • Sample-based DGE using all cells
    • Sample-based DGE using cell type groups
• Analysis of differential gene expression outputs
• Publication-ready figures
• Job Configurations

---

Introduction

This guide illustrates the steps taken to analyze the midbrain dataset (Smajic et al. 2022) that was presented in our pre-print manuscript. This dataset describes single-nuclei transcriptomes from the post-mortem midbrains of five individuals with Parkinson’s disease (PD) and six controls sequenced separately.

---

Downlaoding the midbrain dataset

In you want to use the midbrain dataset to test the scRNAbox pipeline, please see here for detialed instructions on how to download the publicly available data.

---

Installation

scrnabox.slurm installation

To download the latest version of scrnabox.slurm (v0.1.52.50) run the following command:

wget https://github.com/neurobioinfo/scrnabox/releases/download/v0.1.52.5/scrnabox.slurm.zip
unzip scrnabox.slurm.zip

For a description of the options for running scrnabox.slurm run the following command:

bash /pathway/to/scrnabox.slurm/launch_scrnabox.sh -h 

If the scrnabox.slurm has been installed properly, the above command should return the folllowing:

scrnabox pipeline version 0.1.52.50
------------------- 
mandatory arguments:
                -d  (--dir)  = Working directory (where all the outputs will be printed) (give full path)
                --steps  =  Specify what steps, e.g., 2 to run step 2. 2-6, run steps 2 through 6

        optional arguments:
                -h  (--help)  = See helps regarding the pipeline arguments. 
                --method  = Select your preferred method: HTO and SCRNA for hashtag, and Standard scRNA, respectively. 
                --msd  = You can get the hashtag labels by running the following code (HTO Step 4). 
                --markergsea  = Identify marker genes for each cluster and run marker gene set enrichment analysis (GSEA) using EnrichR libraries (Step 7). 
                --knownmarkers  = Profile the individual or aggregated expression of known marker genes. 
                --referenceannotation  = Generate annotation predictions based on the annotations of a reference Seurat object (Step 7). 
                --annotate  = Add clustering annotations to Seurat object metadata (Step 7). 
                --addmeta  = Add metadata columns to the Seurat object (Step 8). 
                --rundge  = Perform differential gene expression contrasts (Step 8). 
                --seulist  = You can directly call the list of Seurat objects to the pipeline. 
                --rcheck  = You can identify which libraries are not installed.  

 ------------------- 
 For a comprehensive help, visit  https://neurobioinfo.github.io/scrnabox/site/ for documentation. 

---

CellRanger installation

For information regarding the installation of CellRanger, please visit the 10X Genomics documentation. If CellRanger is already installed on your HPC system, you may skip the CellRanger installation procedures.

For our analysis of the midbrain dataset we used the 10XGenomics GRCh38-3.0.0 reference genome and CellRanger v5.0.1. For more information regarding how to prepare reference genomes for the CellRanger counts pipeline, please see the 10X Genomics documentation.

---

R library preparation and R package installation

We must prepapre a common R library where we will load all of the required R packages. If the required R packages are already installed on your HPC system in a common R library, you may skip the following procedures.

We will first install R. The analyses presented in our pre-print manuscript were conducted using v4.2.1.

# install R
module load r/4.2.1

Then, we will run the installation code, which creates a directory where the R packages will be loaded and will install the required R packages:

# Folder for R packages 
R_PATH=~/path/to/R/library
mkdir -p $R_PATH

# Install package
Rscript ./scrnabox.slurm/soft/R/install_packages.R $R_PATH

---

scRNAbox pipeline

Step 0: Set up

Now that scrnabox.slurm, CellRanger, R, and the required R packages have been installed, we can proceed to our analysis with the scRNAbox pipeline. We will create a pipeline folder designated for the analysis and run Step 0, selecting the standard analysis track (--method SCRNA), using the following code:

mkdir pipeline
cd pipeline

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 0 \
--method SCRNA

Next, we will navigate to the scrnabox_config.ini file in ~/pipeline/job_info/configs to define the HPC account holder (ACCOUNT), the path to the environmental module (MODULEUSE), the path to CellRanger from the environmental module directory (CELLRANGER), CellRanger version (CELLRANGER_VERSION), R version (R_VERSION), and the path to the R library (R_LIB_PATH):

cd ~/pipeline/job_info/configs
nano scrnabox_config.ini

ACCOUNT=account-name
MODULEUSE=/path/to/environmental/module 
CELLRANGER=/path/to/cellranger/from/module/directory 
CELLRANGER_VERSION=5.0.1
R_VERSION=4.2.1
R_LIB_PATH=/path/to/R/library

Next, we can check to see if all of the required R packages have been properly installed using the following command:

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 0 \
--rcheck 

---

Step 1: FASTQ to gene expression matrix

In Step 1, we will run the CellRanger counts pipeline to generate feature-barcode expression matrices from the FASTQ files. While it is possible to manually prepare the library.csv files for each of the 11 samples in the experiment prior to running Step 1, we are going to opt for automated library preparation. For more information regarding the manual prepartion of library.csv files, please see the the CellRanger library preparation tutorial.

For our analysis of the midbrain dataset we set the following execution parameters for Step 1 (~/pipeline/job_info/parameters/step1_par.txt):

Parameter	Value
par_automated_library_prep	Yes
par_fastq_directory	/path/to/directory/containing/fastqs
par_sample_names	PD1, PD2, PD3, PD4, PD5, CTRL1, CTRL2, CTRL3, CTRL4, CTRL5, CTRL6
par_rename_samples	Yes
par_new_sample_names	Parkinson1, Parkinson2, Parkinson3, Parkinson4, Parkinson5, Control1, Control2, Control3, Control4, Control5, Control6
par_paired_end_seq	TRUE
par_ref_dir_grch	~/genome/10xGenomics/refdata-cellranger-GRCh38-3.0.0
par_r1_length	NULL (commented out)
par_r2_length	NULL (commented out)
par_mempercode	30
par_include_introns	Yes
par_no_target_umi_filter	NULL (commented out)
par_expect_cells	NULL (commented out)
par_force_cells	NULL (commented out)
par_no_bam	NULL (commented out)

Note: The parameters file for each step is located in ~/pipeline/job_info/parameters. For a comprehensive description of the execution parameters for each step see here.

Given that CellRanger runs a user interface and is not submitted as a job, it is recommended to run Step 1 in a 'screen', which will allow the the task to keep running if the connection is broken. To run Step 1, use the following command:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

screen -S run_Midbrain_application_case
bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 1

The outputs of the CellRanger counts pipeline are deposited into ~/pipeline/step1.

---

Step 2: Create Seurat object and remove ambient RNA

In Step 2, we are going to use the CellRanger-generated feature-barcode matrices to produce unique Seurat objects for each of the 11 samples. In this step, we have the option to correct the expression matrices for ambient RNA contamination; however, because Smajic et al. did not perform this analytical procedure we will skip it. In addition, we will perform cell cycle scoring. Prior to performing cell cycle scoring, we must normalize and scale the counts matrix.

For our analysis of the midbrain dataset we set the following execution parameters for Step 2 (~/pipeline/job_info/parameters/step2_par.txt):

Parameter	Value
par_save_RNA	Yes
par_save_metadata	Yes
par_ambient_RNA	No
par_min.cells_L	1
par_normalization.method	LogNormalize
par_scale.factor	10000
par_selection.method	vst
par_nfeatures	2500

We can run Step 2 using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 2

Step 2 produces the following outputs for each sample. As an example we show the outputs for sample Control1:

step2
├── figs2
│   ├── ambient_RNA_estimation_Control1.pdf
│   ├── ambient_RNA_markers_Control1.pdf
│   ├── cell_cyle_dim_plot_Control1.pdf
│   ├── vioplot_Control1.pdf
│   └── zoomed_in_vioplot_Control1.pdf
├── info2
│   ├── estimated_ambient_RNA_Control1.txt
│   ├── MetaData_Control1.txt
│   ├── meta_info_Control1.txt
│   ├── Control1_ambient_rna_summary.rds
│   ├── Control1_RNA.txt
│   ├── sessionInfo.txt
│   └── summary_Control1.txt
└── objs2
    └── Control1.rds

Note: For a comprehensive description of the outputs for each analytical step, please see the Outputs section of the scRNAbox documentation.

Figure 1. Figures produced by Step 2 of the scRNAbox pipeline. The figures for the Control1 sample are shown as an example. A) Estimated ambient RNA contamination rate (Rho) by SoupX. Estimates of the RNA contamination rate using various estimators are visualized via a frequency distribution; the true contamination rate is assigned as the most frequent estimate (red line; 5.1%). B) Log10 ratios of observed counts to expected counts for marker genes from each cluster. Clusters are defined by the CellRanger counts pipeline. The red line displays the estimated RNA contamination rate if the estimation was based entirely on the corresponding gene. C) Principal component analysis (PCA) of Seurat S and G2M cell cycle reference genes. D) Violin plots showing the distribution of cells according to quality control metrics calculated in Step 2. E) Zoomed in violin plots, from the minimum to the mean, showing the distribution of cells according to quality control metrics calculated in Step 2.

---

Step 3: Quality control and filtering

In Step 3, we are going to perform quality control procedures and filter out low quality cells. We are going to filter out cells with < 1000 unique RNA transcripts, < 1500 total RNA transcripts, and > 10% mitochondria and ribosomal RNA. In addition, we are going to remove mitochondrial-encoded and ribosomal genes.

For our analysis of the midbrain dataset we set the following execution parameters for Step 3 (~/pipeline/job_info/parameters/step2_par.txt):

Parameter	Value
par_save_RNA	Yes
par_save_metadata	Yes
par_seurat_object	NULL
par_nFeature_RNA_L	1000
par_nFeature_RNA_U	NULL
par_nCount_RNA_L	1500
par_nCount_RNA_U	NULL
par_mitochondria_percent_L	0
par_mitochondria_percent_U	10
par_ribosomal_percent_L	0
par_ribosomal_percent_U	10
par_remove_mitochondrial_genes	Yes
par_remove_ribosomal_genes	Yes
par_remove_genes	NULL
par_regress_cell_cycle_genes	No
par_regress_custom_genes	No
par_regress_genes	NULL
par_normalization.method	LogNormalize
par_scale.factor	10000
par_selection.method	vst
par_nfeatures	2500
par_top	10
par_npcs_pca	30

We can run Step 3 using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 3

Step 3 produces the following outputs for each sample. As an example we show the outputs for sample Control1:

step3
├── figs3
│   ├── dimplot_pca_Control1.pdf
│   ├── elbowplot_Control1.pdf
│   ├── filtered_QC_vioplot_Control1.pdf
│   └── VariableFeaturePlot_Control1.pdf
├── info3
│   ├── MetaData_Control1.txt
│   ├── meta_info_Control1.txt
│   ├── most_variable_genes_Control1.txt
│   ├── Control1_RNA.txt
│   ├── sessionInfo.txt
│   └── summary_Control1.txt
└── objs3
    └── Control1.rds

Figure 2. Figures produced by Step 3 of the scRNAbox pipeline. The figures for the Control1 sample are shown as an example. A) Violin plots showing the distribution of cells according to quality control metrics after filtering by user-defined thresholds. B) Scatter plot showing the top 2500 most variable features; the top 10 most variable features are labelled. C) Principal component analysis (PCA) visualizing the first two principal component (PC). D) Elbow plot to visualize the percentage of variance explained by each PC.

---

Step 4: Doublet removal

In this Step, we are going to identify doublets (erroneous barcodes produced by two or more cells) and remove them from downstream analyses using the DoubletFinder tool (McGinnis et al. 2019). For optimal performance, DoubletFinder requires the user to define the following parameters:

• The number of statistically significant PCs (par_PCs)
• The number of artificial doublets to generate (par_pN)
• The expected doublet rate for each sample (par_expected_doublet_rate)

The number of statistically significant PCs can be informed by the elbow plots produced in Step 3; in this case the top 25 PCs should maintain a robust compression of the data across samples. DoubletFinder is largely invariant to the number of artifical doublets generated, therefore we will maintain the default parameter of 0.25. The expected doublet rate can be informed by the number of recovered cells (~8% for ~10,000 cells recovered). The number of recovered cells can be informed by the barcodes.tsv.gz file produced by the CellRanger counts pipeline, which is located in ~/pipeline/step1/<sample>/output_folder/outs/filtered_feature_bc_matrix.

The expected doublet rates are approximations obtained from the 10X Genomics Next GEM Single Cell 3' v3.1 documentation, which was used by Smajic et al. for library preparation.

For our analysis of the midbrain dataset we set the following execution parameters for Step 4 (~/pipeline/job_info/parameters/step4_par.txt):

Parameter	Value
par_save_RNA	Yes
par_save_metadata	Yes
par_seurat_object	NULL
par_RunUMAP_dims	25
par_RunUMAP_n.neighbors	65
par_dropDN	Yes
par_PCs	25
par_pN	0.25
par_sct	FALSE
par_sample_names	Control1, Control2, Control3, Control4, Control5, Control6, Parkinson1, Parkinson2, Parkinson3, Parkinson4, Parkinson5
par_expected_doublet_rate	0.042, 0.042, 0.023, 0.04, 0.027, 0.053, 0.023, 0.053, 0.034, 0.023, 0.05

We can run Step 4 using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 4

Step 4 produces the following outputs for each sample. As an example we show the outputs for sample Control1:

step4
├── figs4
│   ├── Control1_DF.classifications.pdf
│   └── Control1_doublet_summary.pdf
├── info4
│   ├── MetaData_Control1.txt
│   ├── meta_info_Control1.txt
│   ├── n_predicted_doublets_Control1.txt
│   ├── Control1_RNA.txt
│   └── sessionInfo.txt
└── objs4
    └── Control1.rds

Figure 3. Figures produced by Step 4 of the scRNAbox pipeline standard track. The figures for the Control1 sample are shown as an example. A) Results of doublet detection analysis with DoubletFinder. Left: violin plot displaying the distribution of the proportion of artificial nearest neighbours (pANN) across singlets and doublets. Right: a bar plot of the number of predicted singlets and doublets. B) Uniform Manifold Approximation Projection (UMAP) plots coloured by droplet assignments (singlet or doublet).

---

Step 5: Integration

In Step 5, we are going to integrate the individual Seurat objects to enable joint analyses across all 11 samples. We will then perform normalization, scaling and linear dimensional reduction on the integrated assay. The outputs from Step 5 will inform the optimal clustering parameters for Step 6.

For our analysis of the midbrain dataset we set the following execution parameters for Step 5:

Parameter	Value
par_save_RNA	Yes
par_save_metadata	Yes
par_seurat_object	NULL
par_one_seurat	No
par_integrate_seurat	Yes
par_merge_seurat	No
par_DefaultAssay	RNA
par_normalization.method	LogNormalize
par_scale.factor	10000
par_selection.method	vst
par_nfeatures	4000
par_FindIntegrationAnchors_dim	25
par_RunPCA_npcs	30
par_RunUMAP_dims	25
par_RunUMAP_n.neighbors	65
par_compute_jackstraw	yes

We can run Step 5 using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 5

Step 5 produces the following outputs:

step5
├── figs5
│   ├── integrated_DimPlot_pca.pdf
│   ├── integrated_DimPlot_umap.pdf
│   ├── integrated_elbow.pdf
│   └── integrated_Jackstraw_plot.pdf
├── info5
│   ├── int_meta_info_seu_step5.csv
│   ├── sessionInfo.txt
│   ├── seu_int_RNA.txt
│   └── seu_int_MetaData.txt
└── objs5
    └── seu_step5.rds

Figure 4. Figures produced by Step 5 of the scRNAbox pipeline standard track. A) Principal component analysis (PCA) visualizing the first two principal components (PC) of the integrated assay, colour coded by sample. B) Uniform Manifold Approximation and Projections (UMAP) plot of the integrated assay, colour coded by sample. C) Elbow plot to visualize the percentage of variance explained by each PC. D) Jackstraw plot to visualize the distribution of p-values for each PC.

---

Step 6: Clustering

In Step 6, we will cluster the cells to indentify groups with similar expression profiles. Based on the Elbow and Jackstraw plots produced in Step 5, we are going to use the first 25 PCs as input. We will cluster the cells at clustering resolutions ranging from 0.0 to 2.0. To determine the stability of clusters at each clustering resolution, we will run the Louvain clustering algorithm 25 times for each resolution, while shuffling the order of the nodes in the graph for each iteration. We will then compute the Adjusted Rand Index (ARI) between pairs of clusters at a given clustering resolution.

For our analysis of the midbrain dataset we set the following execution parameters for Step 6:

Parameter	Value
par_save_RNA	Yes
par_save_metadata	Yes
par_seurat_object	NULL
par_skip_integration	No
par_FindNeighbors_dims	25
par_RunUMAP_dims	25
par_FindNeighbors_k.param	30
par_FindNeighbors_prune.SNN	1/15
par_FindClusters_resolution	0, 0.05, 0.2, 0.6, 0.8, 1.0, 1.2, 1.4, 1.5, 1.6, 1.8, 2.0
par_compute_ARI	Yes
par_RI_reps	25

We can run Step 6 using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 6

Step 6 produces the following outputs:

step6
├── ARI
│   ├── ARI.pdf
│   └── clustering_ARI.xlsx
├── figs6
│   ├── clustree_int.pdf
│   ├── integrated_snn_res.0.05.pdf
│   ├── integrated_snn_res.0.2.pdf
│   ├── integrated_snn_res.0.6.pdf
│   ├── integrated_snn_res.0.8.pdf
│   ├── integrated_snn_res.0.pdf
│   ├── integrated_snn_res.1.2.pdf
│   ├── integrated_snn_res.1.4.pdf
│   ├── integrated_snn_res.1.5.pdf
│   ├── integrated_snn_res.1.6.pdf
│   ├── integrated_snn_res.1.8.pdf
│   ├── integrated_snn_res.1.pdf
│   └── integrated_snn_res.2.pdf
├── info6
│   ├── meta_info.csv
│   ├── sessionInfo.txt
│   ├── seu_MetaData.txt
│   └── seu_RNA.txt
└── objs6
    └── seu_step6.rds

Figure 5. Figures produced by Step 6 of the scRNAbox pipeline. A) Uniform Manifold Approximation and Projections (UMAP) plot, coloured according to the clusters identified at a resolution of 1.5. UMAP plots are produced for each user-defined clustering resolution. B) Mean (top panel) and standard deviation (sd; middle panel) of the Adjusted RNA Index (ARI) between clustering pairs at each user-defined clustering resolution. The bottom panel shows the number of clusters at each user-defined clustering resolution. C) ClustTree plot to visualize inter-cluster dynamics at varying cluster resolutions.

---

Step 7: Cluster annotation

In Step 7, we are going to annotate the clusters identified in Step 6 to identify the cell types comprising the midbrain dataset. scRNAbox provides three distinct tools for cluster annotations:

• Tool 1: Cluster marker and gene set enrichment analysis (GSEA)
• Tool 2: Expression profiling of known marker genes
• Tool 3: Reference-based annotation

Additionally, users can add cluster annotations to the Seurat object.

For comprehensive description of each cluster annotation tool, please see the Step 7:Cluster annotation section of the scRNAbox documentation or our pre-print manuscript.

---

Tool 1: Cluster marker GSEA

Using Tool 1, we are first going to identify differentially expressed marker genes for each cluster. We must define the number of marker genes for each cluster that we want scRNAbox to report and select a clustering resolution that we want to annotate. In this case we will report the top two marker genes for each cluster at a clustering resolution of 1.5.

To identify the marker genes for each cluster, we set the following execution parameters for Step 7 (step7_par.txt):

Annotation tool	Parameter	Value
General	par_save_RNA	No
General	par_save_metadata	No
General	par_seurat_object	NULL
General	par_level_cluster	integrated_snn_res.1.5
Tool 1	par_run_find_marker	Yes
Tool 1	par_run_enrichR	No
Tool 1	par_top_sel	2
Tool 1	par_db	NULL

We can identify the marker genes for each cluster using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 7 \
--markergsea T

The above code produces the following outputs:

step7
├── figs7
│   └── marker
│       └── heatmap.pdf
├── info7
│   ├── marker
│   │   ├── cluster_just_genes.xlsx
│   │   ├── ClusterMarkers.csv
│   │   ├── ClusterMarkers.rds
│   │   ├── cluster_whole.xlsx
│   │   └── top_sel.csv
│   └── sessionInfo_find_marker.txt
└── objs7
    └── seu_step7.rds

Figure 6. Figure produced by Tool 1 (marker gene gene set enrichment analysis) of scRNAbox's cluster annotation module (Step 7). A heatmap is produced to visualize the expression of the top markers genes at the cell level, stratified by cluster. The top marker genes for each cluster identified at a clustering resolution of 1.5 is shown.

---

Now that we have identified the marker genes for each cluster, we will perform a gene set enrichment analysis (GSEA); we will test the differentially expressed genes (DEG) in the positive direction (Log2 fold-change > 0.00) for enrichment across gene set libraries that define cell types using the EnrichR tool. For this analysis, we will use the following libraries:

• Descartes_Cell_Types_and_Tissue_2021;
• CellMarker_Augmented_2021;
• Azimuth_Cell_Types_2021 cell type libraries.

To perform GSEA, we set the following execution parameters for Step 7 (step7_par.txt):

Annotation tool	Parameter	Value
General	par_save_RNA	No
General	par_save_metadata	No
General	par_seurat_object	NULL
General	par_level_cluster	integrated_snn_res.1.5
Tool 1	par_run_find_marker	No
Tool 1	par_run_enrichR	Yes
Tool 1	par_top_sel	2
Tool 1	par_db	NULL

If your HPC allows access to the internet, we can perform GSEA using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 7 \
--markergsea T

Note: If your HPC does not allow access to the internet, you will have to run GSEA locally. For more information, please see the Step 7:Cluster annotation section of the scRNAbox documentation.

The above code produces the following outputs. As an example, we are only showing the outputs for cluster 0:

step7
└── annot_enrich
    └── cluster0
        ├── Er_genes_clust_0_Azimuth_Cell_Types_2021.csv
        ├── Er_genes_clust_0_CellMarker_Augmented_2021.csv
        ├── Er_genes_clust_0_Descartes_Cell_Types_and_Tissue_2021.csv
        ├── plotenrich_clust_0_1.pdf
        ├── plotenrich_clust_0_2.pdf
        └── plotenrich_clust_0_3.pdf

Figure 7. Figures produced by Tool 1 (marker gene gene set enrichment analysis) of scRNAbox's cluster annotation module (Step 7). Upon identifying the top marker genes for each cluster at the user-defined clustering resolution, users can perfrom a gene set enrichment analysis (GSEA) using the EnrichR tool for all marker genes in the positive direction (Log2 fold-change > 0.00). Bar plots are produced to visualize the most enriched terms for each cluster. As an example, the top enrichment results across A) Azimuth Cell Types 2021, B) Descartes Cell Types and Tissue 2021, and C) CellMarker Augmented 2021 cell type libraries for cluster 5 are shown.

Note: It is possible to identify cluster-specific marker genes and perform GSEA at the same time by setting both par_run_find_marker= "Yes" and par_run_enrichR= "Yes.

---

Tool 2: Expression profiling of known marker genes

Using Tool 2, we are going to profile the midbrain dataset for known cell type marker genes. First, we are going to visualize the expression of the marker genes used by Smajic et al. to define their clusters:

Cell type	Gene
Oligodendrocytes	MOBP
OPC	VCAN
Astrocytes	AQP4
Ependymal	FOXJ1
Microglia	CD74
Endothelial	CLDN5
Pericytes	PDGFRB
Excitatory neurons	SLC17A6
Inhibitory neurons	GAD2
GABAergic neurons	GAD2, GRIK1
Dopaminergic neurons (DaN)	TH
Degenerating DaN	CADPS2

To visualize these features, we set the following execution parameters for Step 7 (step7_par.txt):

Annotation tool	Parameter	Value
General	par_save_RNA	No
General	par_save_metadata	No
General	par_seurat_object	NULL
General	par_level_cluster	integrated_snn_res.1.5
Tool 2	par_run_module_score	No
Tool 2	par_run_visualize_markers	Yes
Tool 2	par_module_score	NULL
Tool 2	par_select_features_list	MOBP, VCAN, AQP4, FOXJ1, CD74, CLDN5, GFRB, SLC17A6, GAD2, GRIK1, TH, CADPS2
Tool 2	par_select_features_csv	NULL

We can visualize the expression of these features using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 7 \
--knownmarkers T

The above code produces the following outputs:

step7
├── figs7
    └── visualize_features
        ├── list_dot_plot.pdf
        ├── list_feature_plot.pdf
        └── list_violin_plot.pdf

Figure 8. Figures produced by Tool 2 (profiling the expression of known marker genes) of scRNAbox's cluster annotation module (Step 7). ScRNAbox allows users to visualize the individual and aggregated expression of known marker genes. For the midbrain dataset, we visualized the individual expression of the marker genes used by Smajic et al. to define their clusters. Feature plots visualizing the expression of each individual gene at the cell level are shown. Additionally, violin plots and dot plots are produced to visualize the expression of individual genes at the cluster level.

---

Next, we are going to use a csv file to defineline multiple lists of cell type marker genes:

da_neurons	NPC_orStemLike	mature_neurons	excitatory_neurons	inhbitory_neurons	astrocytes	oligodendrocytes	radial_glia	epithelial	microglia
TH	DCX	RBFOX3	GRIA2	GAD1	GFAP	MBP	PTPRC	HES1	IBA1
SLC6A3	NEUROD1	SYP	GRIA1	GAD2	S100B	MOG	AIF1	HES5	P2RY12
SLC18A2	TBR1	VAMP1	GRIA4	GAT1	AQP4	OLIG1	ADGRE1	SOX2	P2RY13
SOX6	PCNA	VAMP2	GRIN1	PVALB	APOE	OLIG2	VIM	SOX10	TREM119
NDNF	MKI67	TUBB3	GRIN2B	GABR2	SOX9	SOX10	TNC	NES	GPR34
SNCG	SOX2	SYT1	GRIN2A	GABR1	SLC1A3		PTPRZ1	CDH1	SIGLECH
ALDH1A1	NES	BSN	GRIN3A	GBRR1			FAM107A	NOTCH1	TREM2
CALB1	PAX6	HOMER1	GRIN3	GABRB2			HOPX		CX3CR1
TACR2		SLC17A6	GRIP1	GABRB1			LIFR		FCRLS
SLC17A6			CAMK2A	GABRB3			ITGB5		OLFML3
SLC32A1				GABRA6			IL6ST		HEXB
OTX2				GABRA1			SLC1A3		TGFBR1
GRP				GABRA4					SALL1
LPL				TRAK2					MERTK
CCK									PROS1
VIP

Note: This is csv file is available here.

We will use this csv file to visualize the individual expression of each gene and calculate the module score, which will allow us to visualize the aggregated expression of each gene set.

To visualize the unique and aggregated expression of these features, we set the following execution parameters for Step 7 (step7_par.txt):

Annotation tool	Parameter	Value
General	par_save_RNA	No
General	par_save_metadata	No
General	par_seurat_object	NULL
General	par_level_cluster	integrated_snn_res.1.5
Tool 2	par_run_module_score	Yes
Tool 2	par_run_visualize_markers	Yes
Tool 2	par_module_score	~/scrnabox/tutorial/Midbrain_dataset_example_files/module_score.csv
Tool 2	par_select_features_list	NULL
Tool 2	par_select_features_csv	~/scrnabox/tutorial/Midbrain_dataset_example_files/module_score.csv

We can visualize the individual and aggregated expression of these features using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 7 \
--knownmarkers T

The above code produces the following outputs:

step7
├── figs7
│   ├── module_score
│   │   ├── module_score_astrocytes.pdf
│   │   ├── module_score_da_neurons.pdf
│   │   ├── module_score_epithelial.pdf
│   │   ├── module_score_excitatory_neurons.pdf
│   │   ├── module_score_inhbitory_neurons.pdf
│   │   ├── module_score_mature_neurons.pdf
│   │   ├── module_score_microglia.pdf
│   │   ├── module_score_NPC_orStemLike.pdf
│   │   ├── module_score_oligodendrocytes.pdf
│   │   └── module_score_radial_glia.pdf
│   └── visualize_features
│       ├── astrocytes_dot_plot.pdf
│       ├── astrocytes_feature_plot.pdf
│       ├── astrocytes_violin_plot.pdf
│       ├── da_neurons_dot_plot.pdf
│       ├── da_neurons_feature_plot.pdf
│       ├── da_neurons_violin_plot.pdf
│       ├── epithelial_dot_plot.pdf
│       ├── epithelial_feature_plot.pdf
│       ├── epithelial_violin_plot.pdf
│       ├── excitatory_neurons_dot_plot.pdf
│       ├── excitatory_neurons_feature_plot.pdf
│       ├── excitatory_neurons_violin_plot.pdf
│       ├── inhbitory_neurons_dot_plot.pdf
│       ├── inhbitory_neurons_feature_plot.pdf
│       ├── inhbitory_neurons_violin_plot.pdf
│       ├── mature_neurons_dot_plot.pdf
│       ├── mature_neurons_feature_plot.pdf
│       ├── mature_neurons_violin_plot.pdf
│       ├── microglia_dot_plot.pdf
│       ├── microglia_feature_plot.pdf
│       ├── microglia_violin_plot.pdf
│       ├── NPC_orStemLike_dot_plot.pdf
│       ├── NPC_orStemLike_feature_plot.pdf
│       ├── NPC_orStemLike_violin_plot.pdf
│       ├── oligodendrocytes_dot_plot.pdf
│       ├── oligodendrocytes_feature_plot.pdf
│       ├── oligodendrocytes_violin_plot.pdf
│       ├── radial_glia_dot_plot.pdf
│       ├── radial_glia_feature_plot.pdf
│       └── radial_glia_violin_plot.pdf
└── info7
    └── module_score
        └── geneset_by_cluster.csv

Figure 9. Figures produced by Tool 2 (profiling the expression of known marker genes) of scRNAbox's cluster annotation module (Step 7). ScRNAbox allows users to visualize the individual and aggregated expression of known marker genes. For the midbrain dataset, we used gene sets of well known marker genes for cell types in the human midbrain. The microglia gene set is shown as an example. A) Dot plot visualizing the individual expression of marker genes in the microglia gene set. B) Uniform manifold approximation and projection (UMAP) plot visualizing the module score of the microglia gene set at the cell level. The module score allows for profiling the aggregated expression of multiple genes.

---

Tool 3: Reference-based annotation

Using Method 3, we are going to leverage the cell-type annotations from a reference Seurat object and generate annotation predictions for the query dataset. For reference-based annotation we must define the path to a our reference Seurat object and the column of the reference Seurat object's metadata that contains the cell type annotations. For the midbrain dataset, we are going to use a reference Seurat object from Kamath et al.. The reference Seurat object was obtain from the Broad Institute Single Cell Portal

To perform reference-based annotations, we set the following execution parameters for Step 7 (step7_par.txt):

Annotation tool	Parameter	Value
General	par_save_RNA	Yes
General	par_save_metadata	Yes
General	par_seurat_object	NULL
General	par_level_cluster	integrated_snn_res.1.5
Tool 3	par_reference	/path/to/Kamath/reference/seurat/object
Tool 3	par_reference_name	Kamath
Tool 3	par_level_celltype	Cell_Type
Tool 3	par_FindTransferAnchors_dim	10
Tool 3	par_futureglobalsmaxSize	60000 * 1024^2

We can perform reference-based annotations using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 7 \
--referenceannotation T

The above code produces the following outputs:

step7
├── figs7
│   └── reference_based_annotation
│       └── Kammath_UMAP_transferred_labels.pdf
├── info7
│   ├── reference_based_annotation
│   │   └── Kammath_prediction_summary.xlsx
│   ├── sessionInfo_annotate.txt
│   ├── seu_MetaData.txt
│   └── seu_RNA.txt
└── objs7
    └── seu_step7.rds

Figure 10. Figure produced by Tool 3 (reference-based annotation) of scRNAbox's cluster annotation module (Step 7). ScRNAbox allows users to predict cell type annotations of their query dataset based on the predictions of a reference dataset. Left: Uniform Manifold Approximation and Projections (UMAP) plot of clustered and annotated reference Seurat object; single-nucleus RNA sequencing (scRRNAseq) of midbrain tissue produced by Kamath et al., coloured by cell type. Right: UMAP of the label transfer predictions for the midbrain dataset, coloured by predicted cell type. Abbreviations: astro, astrocytes; da, dopaminergic neurons; endo, endothelial cells; nonda, non-dopaminergic neurons; mg, microglia; olig, oligodendrocytes; opc, oligodendrocyte precursor cells.

---

Annotate

Now that we have used the three cluster annotation tools available in the scRNAbox pipeline, we are going to manually curate the results and add cluster annotations to the midbrain dataset.

To add annotations, we set the following execution parameters for Step 7 (step7_par.txt):

Annotation tool	Parameter	Value
General	par_save_RNA	Yes
General	par_save_metadata	Yes
General	par_seurat_object	NULL
General	par_level_cluster	integrated_snn_res.1.5
Annotate	par_annotate_resolution	integrated_snn_res.1.5
Annotate	par_name_metadata	clustering_annotation
Annotate	par_annotate_labels	Oligodendrocyte, Oligodendrocyte, Excitatory, Oligodendrocyte, Oligodendrocyte, Microglia, OPC, Oligodendrocyte, Oligodendrocyte , Inhibitory, Atrocyte, Endothlial, Oligodendrocyte, Microglia, Oligodendrocyte, Astrocyte, Oligodendrocyte, Oligodendrocyte, Atrocyte, Oligodendrocyte, Pericyte, Ependymal, OPC, Pericyte, GABA, Inhibitory, Oligodendrocyte, Microglia, Astrocyte, Endothelial, DaN, CADPS2, Microglia

We can add our annotations using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 7 \
--annotate T

The above code produces the following outputs:

step7
├── figs7
│   └── annotate
│       ├── clustering_annotation_cluster_annotation.pdf
│       └── clustering_annotation_split_cluster_annotation.pdf
├── info7
│   ├── meta_info_seu_step7.txt
│   ├── sessionInfo_annotate.txt
│   ├── seu_MetaData.txt
│   └── seu_RNA.txt
└── objs7
    └── seu_step7.rds

Figure 11. Annotated Seurat object produced in Step 7 of the scRNAbox pipeline. Uniform Manifold Approximation and Projections (UMAP) plots showing the final cluster annotations for the midbrain dataset. Abbreviations: astro, astrocytes; DaN, dopaminergic neurons; endo, endothelial cells; excit, excitatory neurons; GABA, GABAergic neurons; inhib, inhibitory neurons; mg, microglia; olig, oligodendrocytes; opc, oligodendrocyte precursor cells; peri, pericytes.

---

Step 8: Differential gene expression analysis

In Step 8, we are going to perform differential gene expression (DGE) analysis between PD and controls. Prior to computing DGE, we are going to add additional metadata to the Seurat object.

---

Add metadata

We are going to add metadata to the Seurat object using a csv file containing the additional metadata:

Sample_ID	DiseaseStatus
Parkinson1	PD
Parkinson2	PD
Parkinson3	PD
Parkinson4	PD
Parkinson5	PD
Control1	HC
Control2	HC
Control3	HC
Control4	HC
Control5	HC
Control6	HC

Note: This is csv file is available here.

To add metadata, we set the following execution parameters for Step 8 (step8_par.txt):

DGE method	Parameter	Value
General	par_save_RNA	Yes
General	par_save_metadata	Yes
General	par_seurat_object	NULL
Add metadata	par_merge_meta	Sample_ID
Add metadata	par_metadata	~/scrnabox/tutorial/Midbrain_dataset_example_files/metadata.csv

We can add metadata to the Seurat object using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 8 \
--addmeta T

The above code produces the following outputs:

step8
├── info8
│   ├── sessionInfo_add_metadata.txt
│   ├── seu_MetaData.txt
│   └── seu_RNA.txt
└── objs8
    └── seu_step8.rds

---

Next, we can perform DGE analysis. ScRNAbox can compute DGE between two condition conditions (in our case PD vs control) using all cells or cell type groups. Furthermore, scRNAbox provides two frameworks for computing DGE:

1) Cell-based DGE
Cells are used as replicates and DGE is computed using the Seurat FindMarkers (Macosko et al. 2015). While FindMarkers supports several statistical frameworks to compute DGE, we set the default method in our implementation to MAST, which is tailored for scRNAseq data (Finak et al. 2015)

2) Sample-based DGE
Samples are used as replicates by applying a pseudo-bulk analysis. The Seurat AggregateExpression function is used to compute the sum of RNA counts for each gene across all cells from a particular sample (Cao et al. 2022). The DESq2 statistical framework is then used to compute DGE between conditions using the aggregated counts. (Love et al. 2014)

---

Cell-based DGE using all cells

To perform cell-based DGE using all cells we must begin by preparing the contrast matrix (step8_contrast_cell_based_all_cells.txt):

contrast_name meta_data_variable group1 group2
HCvPD DiseaseStatus HC PD

To perform cell-based DGE using all cells, we set the following execution parameters for Step 8 (step8_par.txt):

DGE method	Parameter	Value
General	par_save_RNA	No
General	par_save_metadata	No
General	par_seurat_object	NULL
Cell-based DGE with all cells	par_run_cell_based_all_cells	Yes
Cell-based DGE	par_statistical_method	MAST

We can perform cell-based DGE using all cells using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 8 \
--rundge T

The above code produces the following outputs:

step8
└── Cell_based_all_cells
    └── HCvPD
        ├── HCvPD_DEG.csv
        └── HCvPD_volcano_plot.pdf

---

Cell-based DGE using cell type groups

To perform cell-based DGE using cell type groups we must begin by preparing the contrast matrix (step8_contrast_sample_based_celltype_groups.txt):

contrast_name meta_data_celltype cell_type meta_data_variable group1 group2
OligocendrocytesPDvHC clustering_annotation Oligodendrocyte DiseaseStatus HC PD
MicrogliaPDvHC clustering_annotation Microglia DiseaseStatus HC PD
ExcitatoryPDvHC clustering_annotation Excitatory DiseaseStatus HC PD
InhibitoryPDvHC clustering_annotation Inhibitory DiseaseStatus HC PD
GABAPDvHC clustering_annotation GABA DiseaseStatus HC PD
OPCPDvHC clustering_annotation OPC DiseaseStatus HC PD
AstrocytesPDvHC clustering_annotation Atrocyte DiseaseStatus HC PD
EndothelialPDvHC clustering_annotation Endothelial DiseaseStatus HC PD
PericytesPDvHC clustering_annotation Pericyte DiseaseStatus HC PD
EpendymalPDvHC clustering_annotation Ependymal DiseaseStatus HC PD
DaNPDvHC clustering_annotation DaN DiseaseStatus HC PD

To perform cell-based DGE using all cells, we set the following execution parameters for Step 8 (step8_par.txt):

DGE method	Parameter	Value
General	par_save_RNA	No
General	par_save_metadata	No
General	par_seurat_object	NULL
Cell-based DGE with cell type groups	par_run_cell_based_cell_type_groups	Yes
Cell-based DGE	par_statistical_method	MAST

We can perform cell-based DGE using all cells using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 8 \
--rundge T

The above code produces the following outputs:

step8
└── Cell_based_celltype_groups
    ├── AstrocytesPDvHC
    │   ├── AstrocytesPDvHC_DEG.csv
    │   └── AstrocytesPDvHC_volcano_plot.pdf
    ├── DaNPDvHC
    │   ├── DaNPDvHC_DEG.csv
    │   └── DaNPDvHC_volcano_plot.pdf
    ├── EndothelialPDvHC
    │   ├── EndothelialPDvHC_DEG.csv
    │   └── EndothelialPDvHC_volcano_plot.pdf
    ├── EpendymalPDvHC
    │   ├── EpendymalPDvHC_DEG.csv
    │   └── EpendymalPDvHC_volcano_plot.pdf
    ├── ExcitatoryPDvHC
    │   ├── ExcitatoryPDvHC_DEG.csv
    │   └── ExcitatoryPDvHC_volcano_plot.pdf
    ├── GABAPDvHC
    │   ├── GABAPDvHC_DEG.csv
    │   └── GABAPDvHC_volcano_plot.pdf
    ├── InhibitoryPDvHC
    │   ├── InhibitoryPDvHC_DEG.csv
    │   └── InhibitoryPDvHC_volcano_plot.pdf
    ├── MicrogliaPDvHC
    │   ├── MicrogliaPDvHC_DEG.csv
    │   └── MicrogliaPDvHC_volcano_plot.pdf
    ├── OligocendrocytesPDvHC
    │   ├── OligocendrocytesPDvHC_DEG.csv
    │   └── OligocendrocytesPDvHC_volcano_plot.pdf
    ├── OPCPDvHC
    │   ├── OPCPDvHC_DEG.csv
    │   └── OPCPDvHC_volcano_plot.pdf
    └── PericytesPDvHC
        ├── PericytesPDvHC_DEG.csv
        └── PericytesPDvHC_volcano_plot.pdf

---

Sample-based DGE using all cells

To perform sample-based DGE using all cells we must begin by preparing the contrast matrix (step8_contrast_sample_based_all_cells.txt):

ContrastName MainContrast SampleID
PDvControl DiseaseStatus Sample_ID

To perform sample-based DGE using all cells, we set the following execution parameters for Step 8 (step8_par.txt):

DGE method	Parameter	Value
General	par_save_RNA	No
General	par_save_metadata	No
General	par_seurat_object	NULL
Sample-based DGE with all cells	par_run_sample_based_all_cells	Yes

We can perform sample-based DGE using all cells using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 8 \
--rundge T

The above code produces the following outputs:

step8
└── Sample_based_all_cells
    └── PDvControl
        ├── Aggregated_expression_summary.csv
        ├── DGE_AllCellsMainContrast HC vs PD.csv
        ├── DGE_AllCellsMainContrast HC vs PD.pdf
        └── SampleBased_DGEsummarytable.csv

---

Sample-based DGE using cell type groups

To perform sample-based DGE using cell type groups we must begin by preparing the contrast matrix (step8_contrast_sample_based_celltype_groups.txt):

ContrastName CellType MainContrast SampleID
PDvControlbulk clustering_annotation DiseaseStatus Sample_ID

To perform sample-based DGE using cell type groups, we set the following execution parameters for Step 8 (step8_par.txt):

DGE method	Parameter	Value
General	par_save_RNA	No
General	par_save_metadata	No
General	par_seurat_object	NULL
Sample-based DGE with cell type groups	par_run_sample_based_cell_type_groups	Yes

We can perform sample-based DGE using cell type groups using the following code:

export SCRNABOX_HOME=~/scrnabox/scrnabox.slurm
export SCRNABOX_PWD=~/pipeline

bash $SCRNABOX_HOME/launch_scrnabox.sh \
-d ${SCRNABOX_PWD} \
--steps 8 \
--rundge T

The above code produces the following outputs:

step8
└── sample_based_celltype_groups
    └── PDvControlbulk
        ├── Aggregated_expression_summary.csv
        ├── figs
        │   ├── DGE_AstrocytesMainContrast HC vs PD.pdf
        │   ├── DGE_DaNMainContrast HC vs PD.pdf
        │   ├── DGE_EndothelialMainContrast HC vs PD.pdf
        │   ├── DGE_EpendymalMainContrast HC vs PD.pdf
        │   ├── DGE_ExcitatoryMainContrast HC vs PD.pdf
        │   ├── DGE_GABAMainContrast HC vs PD.pdf
        │   ├── DGE_InhibitoryMainContrast HC vs PD.pdf
        │   ├── DGE_MicrogliaMainContrast HC vs PD.pdf
        │   ├── DGE_OligodendrocytesMainContrast HC vs PD.pdf
        │   ├── DGE_OPCMainContrast HC vs PD.pdf
        │   └── DGE_PericytesMainContrast HC vs PD.pdf
        ├── info
        │   ├── DGE_AstrocytesMainContrast HC vs PD.csv
        │   ├── DGE_DaNMainContrast HC vs PD.csv
        │   ├── DGE_EndothelialMainContrast HC vs PD.csv
        │   ├── DGE_EpendymalMainContrast HC vs PD.csv
        │   ├── DGE_ExcitatoryMainContrast HC vs PD.csv
        │   ├── DGE_GABAMainContrast HC vs PD.csv
        │   ├── DGE_InhibitoryMainContrast HC vs PD.csv
        │   ├── DGE_MicrogliaMainContrast HC vs PD.csv
        │   ├── DGE_OligodendrocytesMainContrast HC vs PD.csv
        │   ├── DGE_OPCMainContrast HC vs PD.csv
        │   └── DGE_PericytesMainContrast HC vs PD.csv
        └── SampleBased_DGEsummarytable.csv

---

Figure 12. Figures produced by Step 8 of the scRNAbox pipeline. Step 8 of the scRNAbox pipeline allows users to compute DGE between groups by two methods: 1) using cells as replicates with MAST (cell-based) and 2) using samples as replicates with DESeq2 (sample-based). Volcano plots are produced for each user-defined DGE contrast. For the midbrain dataset, we leveraged both methods to compute DGE between Parkinson's disease (PD) subjects and controls across A) all cells, B) astrocytes, C) dopaminergic neurons (DaN), D) endothelial cells, E) Ependymal cells, F) excitatory neurons, G) GABAergic neurons, H) Inhibitory neurons, I) microglia, J) oligodendrocytes, K) oligodendrocyte precursor cells (OPC), and H) Pericytes.

---

Analysis of differential gene expression outputs

For the code used to perform downstream analysis of the differentially expressed genes presented in our pre-print manuscript see here.

---

Publication-ready figures

The code used to produce the publication-ready figures used in our pre-print manuscript is avaliable here here.

---

Job Configurations

The following job configurations were used for our analysis of the midbrain dataset. Job Configurations can be modified for each analytical step in the scrnabox_config.ini file in ~/pipeline/job_info/configs

Step	THREADS_ARRAY	MEM_ARRAY	WALLTIME_ARRAY
Step2	4	40g	00-05:00
Step3	4	40g	00-05:00
Step4	4	40g	00-05:00
Step5	4	100g	00-05:00
Step6	4	100g	00-05:00
Step7 MarkerGSEA	4	40g	00-05:00
Step7 KnownMarkers	4	40g	00-02:00
Step7 ReferenceAnnotation	4	200g	00-12:00
Step7 Annotate	4	40g	00-01:00
Step8 AddMeta	4	40g	00-02:00
Step8 RunDGE	4	40g	00-12:00