Reference

All functions

DGE_analysis()

Perform differential expression analysis across cell types based on single cell data with Pseudobulk approach. Returns the results of this differential expression analysis

compute_downsampled_corr()

For a given down-sampled DGE analysis output, computes the correlation of the log-foldchange of the DEGs (at specified p-value) for a given dataset (celltype)

correlation_analysis()

Runs correlation analysis pipeline

correlation_boxplots()

Obtain box plots for the correlations of all celltypes, and the mean correlations at a specified cutoff p-value

differential_expression()

Differential Expression Analysis using edgeR LRT on pseudobulk data

downsampling_DEanalysis()

Downsample the dataset, based either on the individuals or cells, and run DE analysis on each downsampled output. Save results in a dataframe

downsampling_corrplots()

Create correlation plots of the effect sizes of the top 1000 and top 500 DEGs

downsampling_range()

Obtain the range of values to downsample at (either for individuals, or mean number of cells per individual)

gather_celltype_DEGs()

Collate DEGs detected in DGE analysis outputs, across all celltypes in a dataset (datasets/DGE analysis outputs should have common celltypes as specified below)

make_pseudobulk()

Calculate the summed pseudobulk values for an SCE object based on one single cell type only. Ensure to filter SCE to pass one cell type's data.

plot_celltype_correlation()

Computes the correlation of the log-foldchange of the DEGs (at specified p-value) for a given celltype, across all datasets

plot_de_analysis()

Create differential expression analysis plots. Run by DGE_analysis()

plot_mean_correlation()

Obtain the average correlation (across celltypes) at a specified cutoff p-value

power_analysis()

Runs entire power analysis pipeline

power_plots()

Create plots for power analysis, with down-sampling based either on the individuals or cells

preliminary_plots()

Create preliminary plots for data exploration

prop_bulk_DEGs_sc()

Obtain overall percentage overlap between DEGs from bulk data (DE across all tissues) and various scRNA-seq datasets, across all cell types

prop_bulk_DEGs_sc_celltype()

Obtain percentage overlap between DEGs from bulk data (DE across all tissues) and various scRNA-seq datasets, for a specified cell type

random_permutations()

Obtain highly randomised permutations of a specified dataset (based on sex labels), while maintaining consistency with the sample ID

sample_cells()

Sample the data for a specified mean number of cells (per sample) from the dataset

sample_individuals()

Sample the data for a specified number of individuals from the dataset

sex_chromosome_DEGs()

Given a DGE analysis output, this outputs a subset of the analysis with the genes laying only on the sex chromosomes (X/Y)

subset_pairs()

Obtain independent pairs of subsets of a specified dataset, based on sample ID

validate_input_parameters_bulk()

Tests input parameters for functions

validate_input_parameters_correlation()

Tests input parameters for functions

validate_input_parameters_de()

Validate that there are no user input errors for the differential expression analysis - sc.cell.type.de

validate_input_parameters_power()

Tests input parameters for functions

within_data_correlation()

Create correlation plots of the effect sizes, between random permutations and subsets of a given dataset