Differential Expression Analysis via Command Line Interface • ExpreSEd

Designed for RNA-seq workflows, ExpreSEd provides a streamlined pipeline to differential expression results. Use supply raw count matrices and sample metadata (TSV/CSV) directly via the command-line interface.

Step 1: Install ExpreSEd from GitHub

Rscript -e "Rapp::install_pkg_cli_apps('ExpreSEd')"

Step 2: Load example dataset and set paths

ex_counts_path <- system.file("testdata", "example_counts.tsv", package = "ExpreSEd")
ex_meta_path   <- system.file("testdata", "example_meta.tsv", package = "ExpreSEd")

Sys.setenv(EX_COUNTS = ex_counts_path)
Sys.setenv(EX_META   = ex_meta_path)

Step 3: Start the Differential Gene Expression Analysis

Step 3.1: Determine the Low Expression Filter Threshold

Evaluate model performance across different threshold values and select the best one.

ExpreSEd determine_filter_threshold --count $EX_COUNTS --meta $EX_META --output ./results/

Results output a filtering_analysis.tsv table in results folder. Success confirmation will read: “Saved as RDS. Done.”

Step 3.2: Filter Low Expression Genes

Using the threshold value determined in Step 1, manually replace the min_count_per_gene variable and filter. Default min_count_per_gene = 10.

ExpreSEd filter_low_exp_genes --count $EX_COUNTS --meta $EX_META --output ./results/

Success confirmation will read: “Saved as RDS. Done.” Output se_filtered.rds will be save in “./results/”

Step 3.3: Differential Gene Expression Analysis via DESeq2

With the remaining filtered genes, preform DESeq2 to analyze the gene expression. Replace group_var with the column to be categorized. Default group_var = “cell-type”. Replace ref_level with a specific cell type to be reference. Default re_level = “Tconv”

ExpreSEd run_DESeq2 --input ./results/se_filtered.rds --output ./results/

Success confirmation will read: “Saved as RDS. Done.” Output se_dge.rds will be save in “./results/”

Step 3.4: Apply log2_shrinkage on DESeq2 results to improve estimates.

Replace shrinkage with the appropriate GLM estimator. Default shrinkage = “apeglm”

ExpreSEd log2_shrinkage  --input ./results/se_dge.rds --output ./results/

Success confirmation will read: “Saved as RDS. Done.” Results output dge_shrink.rds and a dge_shrink.tsv table saved in “./results/”

Step 3.5: Intrepret the Gene Regulation

Summarize the non-significant and up and down regulated genes in the data set. Replace p_threshold with the appropriate adjusted p-value threshold. Default p_threshold = 0.05. Replace fc_threshold with the appropriate fold-change threshold. Default fc_threshold = 0.5.

ExpreSEd gene_regulation_summary  --input ./results/dge_shrink.rds --output ./results/

Success confirmation will read: “Saved as RDS. Done.” Results output gene_reg_summary.rds and a gene_reg_summary.tsv table saved in “./results/”

Step 3.6: Visualize Expression

Generate a volcano plot to visualize the gene_reg_summary results. Use the same p_threshold and fc_threshold values utilized in Step 5. Replace set_title with the correct title. Default set_title = “Volcano Plot - Lymph Node Treg vs Tconv”. Replace xlab with the correct x-axis title. Default xlab = “log2 Fold Change (Treg vs Tconv)”.

ExpreSEd generate_volcano --input ./results/dge_shrink.rds --output ./results/

Success confirmation will read: “Saved as RDS. Done.” Results output volcano_plot.rds, volcano_plot.pdf, and volcano_plot.png saved in “./results/”