Function reference • sageseqr

All functions
`association_statistic_for_factors()`	Compute the association statistic for factor variables
`association_statistics_for_both()`	Find intraclass correlation (ICC) between factor and continuous covariates
`biomart_obj()`	Get available Ensembl dataset
`biomart_stats()`	Get Gene Length and GC: biomart
`boxplot_vars()`	Explore metadata by variable.
`build_formula()`	Formula Builder
`check_mismatch()`	Check for consistent identifiers
`clean_covariates()`	Create covariate matrix from tidy metadata data frame.
`coerce_continuous()`	Coerce objects to type numeric.
`coerce_factors()`	Coerce objects to type factors.
`collapse_duplicate_hgnc_symbol()`	Duplicate HGNC
`complete_path()`	Create complete URL to Synapse entity
`compute_residuals()`	Compute residualized counts matrix
`conditional_plot_sexcheck()`	Conditionally wrap plot_sexcheck for targets
`convert_geneids()`	Get gene Ids
`correlate_and_plot()`	Calculate correlation between covariates and principal components (PCs)
`cqn()`	Conditional Quantile Normalization (CQN)
`differential_expression()`	Differential Expression with Dream
`dropped_genes()`	Dropped Genes Finder
`filter_genes()`	Filter genes
`get_association_statistics()`	Visualize relationship between variables
`get_biomart()`	Get Ensembl biomaRt object
`get_data()`	Detect file type and download data
`gtf_stats()`	Get Gene Length and GC: GTF/Fasta
`identify_outliers()`	Explore samples that are outliers
`parse_counts()`	Subset counts data frame
`plot_coexpression()`	Co-expression histogram generation
`plot_pcs_with_covariates()`	Explore relationship between principal components (PCs) and covariates
`plot_sexcheck()`	Explore metadata by gene expression on the sex chromosomes.
`plot_sexcheck_pca()`	Explore metadata by gene expression on the sex chromosomes (PCA)
`plot_volcano()`	Volcano plot differential expression results'
`prepare_results()`	Prepare output
`provenance_helper()`	Provenance helper
`run_pca()`	Principal Component Analysis (PCA) of samples
`run_pca_and_plot_correlations()`	Compute principal component analysis (PCA) and plot correlations
`simple_filter()`	Filter genes with low expression
`start_de()`	Initialize differential expression analysis workflow
`stepwise_regression()`	Stepwise Regression
`store_results()`	Store output to Synapse
`summarize_biotypes()`	Summarize Biotypes
`threshold_fdr()`	Apply false discovery rate (FDR) threshold to correlation matrix.
`wrap_de()`	Wrapper for Differential Expression Analysis

Reference

All functions