Package index • macpie

All functions

aggregate_by_de(): Collapse Replicates by Differential Expression

check_zeroinflation(): Quick group-aware zero-inflation check (Negative Binomial baseline via edgeR)

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 Compute one‐sided hypergeometric p-values, z-scores, and combined scores for a list of differentially expressed genes (deg) against each entry in a list of gene sets (genesets). 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)

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

select_robust_controls(): Select high-quality control replicates via TMMwsp log-CPM correlation

subsample_genes(): Subsample genes (fast helper function for zero-inflation checks)

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