Package index • MOSuite

multiOmicDataSet

create_multiOmicDataSet_from_dataframes(): Construct a multiOmicDataSet object from data frames

create_multiOmicDataSet_from_files(): Construct a multiOmicDataSet object from tsv files.

multiOmicDataSet(): multiOmicDataSet class

Main functions

See vignette('intro') for recommended usage

batch_correct_counts(): Perform batch correction

clean_raw_counts(): Clean Raw Counts

diff_counts(): Differential expression analysis

extract_counts(): Extract count data

filter_counts(): Filter low counts

normalize_counts(): Normalize counts

filter_diff(): Filter features from differential analysis based on statistical significance

Visualization

plot_corr_heatmap(): Plot correlation heatmap

plot_expr_heatmap(): Plot expression heatmap

plot_histogram(): Plot histogram

plot_pca(): Perform and plot a Principal Components Analysis

plot_read_depth(): Plot read depth as a bar plot

plot_venn_diagram(): Plot a venn diagram, UpSet plot, or table of intersections

plot_volcano_enhanced(): Enhanced Volcano Plot

plot_volcano_summary(): Volcano Plot - Summary [CC Produces one volcano plot for each tested contrast in the input DEG table.

print_or_save_plot(): Print and/or save a ggplot

Color selectors

get_colors_lst(): Create named list of default colors for plotting

get_colors_vctr(): Get vector of colors for observations in one column of a data frame

set_color_pal(): Set color palette for a single group/column

Misc. helpers & utilities

calc_cpm(): Calculate counts-per-million (CPM) on raw counts in a multiOmicDataSet

calc_pca(): Perform principal components analysis

bind_dfs_long(): Bind dataframes in named list to long dataframe

join_dfs_wide(): Join dataframes in named list to wide dataframe

Datasets

gene_counts: RSEM expected gene counts

nidap_batch_corrected_counts: Batch-corrected counts for the NIDAP test dataset.

nidap_batch_corrected_counts_2: Batch-corrected counts for the NIDAP test dataset. The result of running batch_correct_counts() on nidap_norm_counts.

nidap_clean_raw_counts: Clean raw counts for the NIDAP test dataset. The result of running clean_raw_counts() on nidap_raw_counts.

nidap_deg_analysis: Differential gene expression analysis for the NIDAP test dataset.

nidap_deg_analysis_2: Differential gene expression analysis for the NIDAP test dataset. The result of running diff_counts() on nidap_filtered_counts.

nidap_deg_gene_list: List of differentially expressed genes from the NIDAP test dataset using default parameters with filter_diff().

nidap_filtered_counts: Filtered counts for the NIDAP test dataset. The result of running filter_counts() on nidap_clean_raw_counts.

nidap_norm_counts: Normalized counts for the NIDAP test dataset. The result of running normalize_counts() on nidap_filtered_counts.

nidap_raw_counts: Raw counts for the NIDAP test dataset Pairs with nidap_sample_metadata.

nidap_sample_metadata: Sample metadata for the NIDAP test dataset

nidap_venn_diagram_dat: Output data from venn diagram. The result of running plot_venn_diagram() on nidap_volcano_summary_dat

nidap_volcano_summary_dat: Summarized differential expression analysis for input to venn diagram

Options

options: MOSuite Options

Low-level plot helpers

These functions are called by the main visualization functions.

plot_corr_heatmap_dat: Plot correlation heatmap for counts dataframe

plot_corr_heatmap_moo: Plot correlation heatmap for multiOmicDataSet

plot_expr_heatmap_dat: Plot expression heatmap for counts dataframe

plot_expr_heatmap_moo: Plot expression heatmap for multiOmicDataSet

plot_histogram_dat: Plot histogram for counts dataframe

plot_histogram_moo: Plot histogram for multiOmicDataSet

plot_pca_2d(): Perform and plot a 2D Principal Components Analysis

plot_pca_3d(): 3D PCA for counts dataframe

plot_pca_dat: Plot 2D or 3D PCA for counts dataframe

plot_pca_moo: Plot 2D or 3D PCA for multiOmicDataset

plot_read_depth_dat: Plot read depth for data.frame

plot_read_depth_moo: Plot read depth for multiOmicDataSet