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add_colour_vector_to_concordance()
|
Add colour vector to concordance data frame |
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apply_eln2015_cyto()
|
Apply ELN2015 stratification based on cytogenetics |
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apply_eln2017_cyto()
|
Apply ELN2017 stratification based on cytogenetics |
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apply_eln2017_rna()
|
Apply ELN2017-RNA stratification |
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apply_expression_signature_reclassification()
|
Update an existing stratification by applying a gene expression score |
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aps_volcano_plot()
|
Draw Volcano Plot for APS DE Comparison |
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calculate_reference_set_coefficient_of_variation()
|
Calculate reference set coefficient of variation |
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calculate_reference_set_mean_expression()
|
Calculate reference set mean expression |
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connect_to_aml_project_orm()
|
Connect to AML Project ORM Database |
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construct_rna_qc_dataframe()
|
Construct a data frame of RNA-SeQC results |
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construct_sailfish_gene_dataframe()
|
Construct a data frame of Sailfish gene-level results |
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convert_sailfish_df_to_matrix()
|
From a data frame of Sailfish gene-level results, construct an expression matrix |
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density_plotter()
|
Generate a density-plot of expression of selected genes, faceted by selected feature |
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downregulated_genes()
|
Pull down-regulated genes |
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eng_log_breaks()
|
Generate a set of breaks using the 'Engineer's log scale' |
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expression_matrix_to_long_df()
|
Transform expression matrix to long data frame |
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expression_summary()
|
Summarize distribution characteristics for a column of interest |
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fit_coxph()
|
Fit a Cox proportional hazards model |
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gather_expression_matrix_to_tidy_df()
|
Convert an expression matrix to a tidy DF |
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get_cluster_assignments_for_k()
|
Retrieve cluster assignments for a given value of k |
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ggsave_pdf_and_png()
|
Save ggplot to PDF and PNG |
|
grid_arrange_shared_legend()
|
Generate a grid of plots with a shared legend |
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group_tally_table()
|
Group / Tally / Table |
|
hit_plotter()
|
Generate a dot-plot of expression of selected genes, facetted by selected feature |
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limma_logFC_to_signed_foldchange()
|
Convert limma's logFC value to signed fold-change |
|
log2_plus1_transform()
|
Transform data by taking the log2(count+1) |
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long_df_to_ranked_matrix()
|
Transform long data frame to sorted matrix |
|
matrix_glimpse()
|
# Report basic information about a matrix |
|
munge_voom_to_ipa()
|
Munge differential expression hits in voom format to IPA-friendly format |
|
nest_libraries_by_patient()
|
Add nested library information to a patient-level data frame |
|
num_to_text()
|
Convert a small number to a text representation |
|
parse_pavfinder_0.2.0()
|
Parse PAVfinder 0.2.0 output |
|
parse_pavfinder_0.3.0()
|
Parse PAVfinder 0.3.0 output |
|
pathway_enrichment_plot()
|
Generate a plot of enriched pathways |
|
plot_heatmap_for_k()
|
Plot a pheatmap for a single value of k |
|
plot_rf_predictions()
|
Plot predictions from a randomForest model |
|
plot_survival()
|
Plot survival curves |
|
plot_vars_by_coverage()
|
This function takes the long and wide data frames, and plots all variants by coverage,
colouring them by concordance status |
|
plot_vars_by_vaf()
|
Join to the plotting frame and re-plot for VAF |
|
pretty_kable()
|
Set a function for pretty-printing kables |
|
pretty_num()
|
Set a function for pretty-printing numbers in the text |
|
pull_enrichr_enriched_terms()
|
Pull EnrichR enriched terms for a single pathway source |
|
pull_enrichr_terms_to_plot()
|
Identify enriched terms across cohorts |
|
random_forest_summary()
|
From a random forest model generated by caret, generate summary plots |
|
read_filled_variant_tsv()
|
This function reads in the 'long' TSV variant sheets generated by the makefile |
|
rescale_genes_by_reference()
|
Rescale all genes by reference expression levels |
|
retrieve_db_comments()
|
Retrieve DB Comments |
|
retrieve_db_curated_results()
|
Retrieve DB Curated Results |
|
retrieve_db_libraries()
|
Retrieve DB Libraries |
|
retrieve_db_paths()
|
Retrieve DB Paths |
|
retrieve_db_specimens()
|
Retrieve DB Specimens |
|
rf_predictions()
|
Extract predictions from a random forest model |
|
rna_seqc_parser()
|
Parse output from RNA-SeQC v1.1.8 |
|
run_cna_workflow()
|
Run the CNA workflow |
|
run_gage_for_feature()
|
Run GAGE gene-set enrichment analysis for a given feature |
|
run_voom_for_feature()
|
Run voom for a given feature comparison |
|
sailfish_gene_parser_post_0.7.0()
|
Parse gene-level output from Sailfish >=0.7.0 |
|
sailfish_isoform_parser_post_0.7.0()
|
Parse isoform-level output from Sailfish >=0.7.0 |
|
sailfish_isoform_parser_pre_0.7.0()
|
Parse isoform-level output from Sailfish <0.7.0 |
|
scale_by_rolling_mean()
|
Scale expression estimates by rolling mean |
|
select_by_position()
|
Quick function for mapping to select out list items |
|
split_gatk_format_vals()
|
Split a FORMAT string from GATK-haplotype to get simpler statistics |
|
spread_expression_df_to_matrix()
|
Convert a 'tidy' expression DF to a matrix |
|
spread_filled_snv_df_to_wide()
|
This function takes the 'long, filled' data frames with variant observations, and converts them to 'wide' format |
|
subset_genes_by_thresholds()
|
Subset expression matrix by thresholds |
|
subset_genes_for_plots()
|
Subset the TPM matrix to the genes of interest |
|
subset_heatmap()
|
Generate a heatmap for a subset of genes |
|
subset_pc_plot()
|
Generate a prinicple component plot for a subset of genes, coloured by a given feature |
|
subtype_to_disease()
|
From a subtype indication, return a simplified disease type |
|
tfl_split_sort()
|
A function to sort TFL IDs into ascending order |
|
train_caret_rf()
|
Train a random forest model with caret |
|
upregulated_genes()
|
Pull up-regulated genes |
|
volcano_plotter()
|
Generate a volcano plot of expression hits |