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All functions

aggregate_by_de()
Collapse Replicates by Differential Expression
compute_chem_descriptors()
Compute chemical descriptors from SMILES
compute_de_umap()
Prepare DE-based umap
compute_hyper_enrich_bg()
Calculate enrichment of DE genes in a gene set Internal function
compute_multi_de()
Perform DE of multiple samples in a screen vs control
compute_multi_enrichr()
Perform enrichR-style analysis on a screen
compute_multi_screen_profile()
Find similarities between expression profiles with fgsea. Mitochondrial/ribosomal genes are filtered from the analysis
compute_multiple_dose_response()
Model Gene Dose-Response Curves Across Treatments
compute_normalised_counts()
Retrieve normalised counts of MAC-seq data
compute_qc_metrics()
Calculate QC metrics
compute_single_de()
Retrieve normalised counts of MAC-seq data
compute_single_dose_response()
Model Gene Dose-Response Curve Using drc
compute_single_enrichr()
Pathway enrichment analysis
compute_smiles()
Annotate tidyseurat object with SMILES
convert_human_to_mouse()
For a given gene set of human symbols return mouse symbols
download_geneset()
Title
filter_genes_by_expression()
Filter genes by expression and grouping
find_clusters_de_umap()
Calculate clusters for umap based on DE analysis
genesets
Pathway gene‐sets for PMMSq033
macpie_colours
Color palettes used by macpie
macpie_theme()
colour theme for macpie plots
mini_mac
A small example MACseq plate
plot_counts()
Generate a box plot to show gene expression (CPM) This is the function to generate a box plot to show CPM levels of DE genes among selected treatment samples and control samples.
plot_de_umap()
Plot UMAP dimensionality reduction on DE genes
plot_distance()
Create a distance heatmap
plot_gene_ranks()
Generate a knee plot A knee plot to show total number of total read counts for each gene in a given treatment group
plot_mds()
Plot MDS dimensionality reduction
plot_metadata_heatmap()
Generate Heatmaps of Metadata Function
plot_multi_de()
Generate heatmap of DE genes from multiple treatments This is the function to generate a heatmap of DE genes from running compute_multi_DE that shared by more than one treatment group. There are a few options available to help you to extract shared DE genes.
plot_multi_screen_profile()
Plot multi-screen profile from fgsea results
plot_plate_layout()
Plot MAC-seq data on a plate layout
plot_qc_metrics()
Create a lollipop chart
plot_qc_metrics_heatmap()
Create a heatmap for multiple QC metrics
plot_rle()
Create an RLE Plot
plot_volcano()
Volcano plot of differentially expressed genes
read_metadata()
Read Metadata from a File
summarise_de()
Generate a table to summarise gene numbers from a differential expression test.
validate_metadata()
Check and clean the metadata file generated from findmetadata function