Package: hermes 1.7.2

Daniel Sabanés Bové

hermes: Preprocessing, analyzing, and reporting of RNA-seq data

Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available.

Authors:Daniel Sabanés Bové [aut, cre], Namrata Bhatia [aut], Stefanie Bienert [aut], Benoit Falquet [aut], Haocheng Li [aut], Jeff Luong [aut], Lyndsee Midori Zhang [aut], Alex Richardson [aut], Simona Rossomanno [aut], Tim Treis [aut], Mark Yan [aut], Naomi Chang [aut], Chendi Liao [aut], Carolyn Zhang [aut], Joseph N. Paulson [aut], F. Hoffmann-La Roche AG [cph, fnd]

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NEWS

# Install 'hermes' in R:
install.packages('hermes', repos = c('https://insightsengineering.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/insightsengineering/hermes/issues

Datasets:

On BioConductor:hermes-1.9.1(bioc 3.20)hermes-1.8.1(bioc 3.19)

rnaseqdifferentialexpressionnormalizationpreprocessingqualitycontrolrna-seqstatistical-engineering

8.04 score 10 stars 1 packages 49 scripts 92 exports 124 dependencies

Last updated 5 months agofrom:fdf4ec82e3 (on v1.7.2). Checks:OK: 7. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-winOKOct 25 2024
R-4.5-linuxOKOct 25 2024
R-4.4-winOKOct 25 2024
R-4.4-macOKOct 25 2024
R-4.3-winOKOct 25 2024
R-4.3-macOKOct 25 2024

Exports:.ConnectionBiomart.row_data_annotation_cols%>%add_quality_flagsannotationannotation<-assert_proportionautoplotcalc_pcacat_with_newlinecheck_proportioncol_data_with_genescolMeanZscorescolPrinComp1connect_biomartcontrol_normalizecontrol_qualitycorrelatecountscounts<-cut_quantiledf_cols_to_factordiff_expressiondraw_barplotdraw_boxplotdraw_genes_barplotdraw_heatmapdraw_libsize_densitiesdraw_libsize_histdraw_libsize_qqdraw_nonzero_boxplotdraw_scatterplotexpect_proportionextraColDataNamesextraRowDataNamesfiltergene_specgenesGeneSpecget_low_depthget_low_expressionget_tech_failureh_all_duplicatedh_cpmh_df_factors_with_explicit_nah_diff_expr_deseq2h_diff_expr_voomh_draw_boxplot_dfh_ensembl_to_entrez_idsh_get_annotation_biomarth_get_granges_by_idh_get_size_biomarth_has_req_annotationsh_low_depth_flagh_low_expression_flagh_map_posh_parensh_pca_df_r2_matrixh_pca_var_rsquaredh_rlogh_rpkmh_short_listh_strip_prefixh_tech_failure_flagh_tpmh_unique_labelsh_voomh_vstHermesDataHermesDataFromMatrixinner_join_cdiscis_classis_constantis_counts_vectoris_hermes_datais_list_withisEmptylapplymetadatametadata<-normalizeone_providedprefixqueryrenamesamplesset_tech_failureshowsummarytest_proportiontop_geneswrap_in_mae

Dependencies:abindAnnotationDbiaskpassassertthatbackportsBHBiobaseBiocBaseUtilsBiocFileCacheBiocGenericsBiocParallelbiomaRtBiostringsbitbit64blobcachemcheckmatecirclizecliclueclustercodetoolscolorspaceComplexHeatmapcpp11crayoncurlDBIdbplyrDelayedArrayDESeq2digestdoParalleldplyredgeREnvStatsfansifarverfastmapfilelockforcatsforeachformatRfutile.loggerfutile.optionsgenericsGenomeInfoDbGenomeInfoDbDataGenomicRangesGetoptLongggfortifyggplot2ggrepelGlobalOptionsgluegridExtragtablehmshttrhttr2IRangesisobanditeratorsjsonliteKEGGRESTlabelinglambda.rlatticelifecyclelimmalocfitmagrittrMASSMatrixMatrixGenericsmatrixStatsmemoisemgcvmimeMultiAssayExperimentmunsellnlmenortestopensslpillarpkgconfigplogrpngprettyunitsprogresspurrrR6rappdirsrbibutilsRColorBrewerRcppRcppArmadilloRdpackrjsonrlangRSQLiteS4ArraysS4VectorsscalesshapesnowSparseArraystatmodstringistringrSummarizedExperimentsystibbletidyrtidyselectUCSC.utilsutf8vctrsviridisLitewithrxml2XVectorzlibbioc

Introduction to hermes

Rendered fromhermes.Rmdusingknitr::rmarkdownon Oct 25 2024.

Last update: 2024-06-27
Started: 2022-04-07

Readme and manuals

Help Manual

Help pageTopics
'hermes' Packagehermes-package hermes
Add Quality Flagsadd_quality_flags get_low_depth get_low_expression get_tech_failure h_low_depth_flag h_low_expression_flag h_tech_failure_flag
Checks Whether All Missingall_na
Annotation Accessor and Setter.row_data_annotation_cols annotation annotation,AnyHermesData-method annotation<-,AnyHermesData,DataFrame-method
Standard Assertion Argumentsassertion_arguments
Additional Assertions for 'assert_that'assertions is_class is_constant is_counts_vector is_hermes_data is_list_with one_provided
All Standard Plots in Default Settingautoplot,AnyHermesData-method plot_all
Principal Components Analysis Calculation.HermesDataPca calc_pca HermesDataPca HermesDataPca-class
Concatenate and Print with Newlinecat_with_newline
Column Binding of 'AnyHermesData' Objectscbind
Check for proportionassert_proportion check_proportion expect_proportion test_proportion
Sample Variables with Selected Gene Informationcol_data_with_genes
Mean Z-score Gene SignaturecolMeanZscores
First Principal Component (PC1) Gene SignaturecolPrinComp1
Connection to 'BioMart'.ConnectionBiomart ConnectionBiomart ConnectionBiomart-class connect_biomart
Control Settings for Counts Normalizationcontrol_normalize
Control for Specified Quality Flagscontrol_quality
Generic Function for Correlation Calculationscorrelate
Correlation between Sample Counts of 'AnyHermesData'.HermesDataCor autoplot,HermesDataCor-method calc_cor correlate,AnyHermesData-method HermesDataCor HermesDataCor-class
Correlation of Principal Components with Sample Variables.HermesDataPcaCor autoplot,HermesDataPcaCor-method correlate,HermesDataPca-method HermesDataPcaCor HermesDataPcaCor-class pca_cor_samplevar
Counts Accessor and Settercounts counts,AnyHermesData-method counts<-,AnyHermesData,matrix-method
Cutting a Numeric Vector into a Factor of Quantile Binscut_quantile
Conversion of Eligible Columns to Factor Variables in a 'DataFrame'df_cols_to_factor
Differential Expression Analysis.HermesDataDiffExpr autoplot,HermesDataDiffExpr-method diff_expression HermesDataDiffExpr HermesDataDiffExpr-class
Barplot for Gene Expression Percentilesdraw_barplot
Boxplot for Gene Expression Valuesdraw_boxplot h_draw_boxplot_df
Stacked Barplot of Low Expression Genes by Chromosomedraw_genes_barplot
Heatmap for Gene Expression Countsdraw_heatmap
Density Plot of (Log) Counts Distributionsdraw_libsize_densities
Histogram of Library Sizesdraw_libsize_hist
Q-Q Plot of Library Sizesdraw_libsize_qq
Boxplot of Non-Zero Genesdraw_nonzero_boxplot
Scatterplot for Gene Expression Valuesdraw_scatterplot
Example 'ExpressionSet' Dataexpression_set
Extra Variable Names Accessor MethodsextraColDataNames extraColDataNames,AnyHermesData-method extraRowDataNames extraRowDataNames,AnyHermesData-method extra_data_names
Filter 'AnyHermesData' on Subset Passing Default QC Flagsfilter filter,AnyHermesData-method filter,data.frame-method filter,ts-method
'GeneSpec' Constructorgene_spec
Gene IDs Accessorgenes genes,AnyHermesData-method
R6 Class Representing a Gene (Signature) SpecificationGeneSpec
Finding All Duplicates in Vectorh_all_duplicated
Conversion to Factors with Explicit Missing Level in a 'data.frame'h_df_factors_with_explicit_na
'DESeq2' Differential Expression Analysish_diff_expr_deseq2
'limma'/voom Differential Expression Analysish_diff_expr_voom
Translation of 'Ensembl' to 'Entrez' Gene IDsh_ensembl_to_entrez_ids
Get Annotations from 'BioMart'h_get_annotation_biomart
Conversion of 'BioMart' Coordinates into 'GRanges'h_get_granges_by_id
Total Length of All Exons for Genesh_get_size_biomart
Predicate for Required Annotationsh_has_req_annotations
Helper Function For Matching Map Values to Namesh_map_pos
Parenthesize a Character Vectorh_parens
Calculation of R2 Matrix between Sample Variables and Principal Componentsh_pca_df_r2_matrix
Calculation of R2 between Sample Variable and Principal Componentsh_pca_var_rsquared
Make a Short List of a Character Vectorh_short_list
Stripping Prefix from Gene IDsh_strip_prefix
Creation of Unique Labelsh_unique_labels
Example 'HermesData' Datahermes_data
'HermesData' and 'RangedHermesData'.HermesData .RangedHermesData AnyHermesData AnyHermesData-class HermesData HermesData-class HermesDataFromMatrix RangedHermesData RangedHermesData-class
Inner Joining a Genes with a CDISC Data Setinner_join_cdisc
Checking for Empty 'SummarizedExperiment'isEmpty isEmpty,SummarizedExperiment-method
'lapply' method for 'MultiAssayExperiment'lapply lapply,MultiAssayExperiment-method
Metadata Accessor and Settermetadata
Example 'MultiAssayExperiment' Datamulti_assay_experiment
Normalization of 'AnyHermesData' Objectsh_cpm h_rlog h_rpkm h_tpm h_voom h_vst normalize normalize,AnyHermesData-method
Prefix Accessorprefix
Query Gene Annotations from a Connectionquery query,character,ConnectionBiomart-method
Row Binding of 'AnyHermesData' Objectsrbind
Renaming Contents of 'SummarizedExperiment' Objectsrename rename,data.frame-method rename,SummarizedExperiment-method
Sample IDs Accessorsamples samples,AnyHermesData-method
Set Technical Failure Flagsset_tech_failure
Show Method for 'AnyHermesData' Objectsshow show,HermesData-method show,RangedHermesData-method
Subsetting 'AnyHermesData' Objectssubset
Example 'SummarizedExperiment' Datasummarized_experiment
Summary Method for 'AnyHermesData' Objects.HermesDataSummary HermesDataSummary HermesDataSummary-class show,HermesDataSummary-method summary summary,AnyHermesData-method
Derivation of Top Genes.HermesDataTopGenes autoplot,HermesDataTopGenes-method HermesDataTopGenes HermesDataTopGenes-class top_genes
Internal Helper Functions for Validation of 'AnyHermesData' Objectsvalidate validate_cols validate_col_data validate_counts validate_names validate_prefix validate_row_data
Wrap in MAEwrap_in_mae