- Changed maintainer email address.
- New plotting function
draw_heatmap
to produce heatmaps of (normalized) counts.
- The utility function
df_cols_to_factor
now also converts existing factors to having explicit missing levels. - Version bump on
forcats
dependency. - Removed
ggplot2
deprecation warning..count..
.
- Additional
version
argument forconnect_biomart
to specify anEnsembl
version.
- First public release of the
hermes
package. - Submission to
BioConductor
.
- Better legends on the genes barplot and the correlation heatmap.
- Improved vignette layout using the
BioConductor
style.
- New utility function
cut_quantile
for cutting a numeric vector into quantiles. - New utility function
cat_with_newline
for concatenating and printing with newline. - New check function
check_proportion
which checks for a single proportion.
- New function
draw_scatterplot
to produce scatterplots of two genes or gene signatures. - New function
draw_boxplot
for boxplots of gene expression values. - New function
draw_barplot
for barplots of dichotomized gene expression counts into two or three percentile categories. - New helper function
wrap_in_mae
that wraps a singleSummarizedExperiment
object into an MAE object. - New method
rename
that makes renaming columns ofrowData
andcolData
as well as assay names in existingSummarizedExperiment
objects much easier, as a step before converting toHermesData
. - New method
lapply
that allows user to apply a function on all experiments in aMultiAssayExperiment
. - New method
isEmpty
that checks whether aSummarizedExperiment
object is empty. - New gene filtering option
n_top
in thecalc_pca
function, which allows filtering genes with greatest variability across samples. - New class
GeneSpec
for specification of genes or gene signatures, see?gene_spec
for simple construction. Inclusion of gene signature functionscolPrinComp1
andcolMeanZscores
to supplement standard column statistics functions. - New helper function
col_data_with_genes
which extracts the sample variables saved incolData
together with selected gene information as a combined data set. - New helper function
inner_join_cdisc
which joins genetic with CDISC data sets.
normalize()
now also works when thehermes
package is not loaded, i.e. you can use it withhermes::normalize()
.correlate()
now also works when there are factor variables in the sample variables of theHermesData
object.add_quality_flags()
does no longer returnNA
as the technical failure flags for the samples if there is only a single gene contained in the input, but instead a vector ofFALSE
to ensure correct downstream functionality.
- Updated
LICENCE
andREADME
with new package references. - The
multi_assay_experiment
now containsHermesData
experiments, different patient IDs, one experiment with normalized assays, and multiple samples per patient in one experiment. - The main
HermesData
example is now saved in the package ashermes_data
, and the previoussummarized_experiment
is still available. Note that patient IDs have been changed in the new version to align with themulti_assay_experiment
. - Renaming of required
rowData
andcolData
columns to be more consistent with standards and use lowercase snake-case names. - Annotation querying and setting is now more flexible in that it also allows to query more annotations than the required ones.
- Instead of gene starts and ends, the total length of gene exons is now used as the annotation column
size
. Corresponding queries fromBioMart
are used to return this gene size. df_char_to_factor
has been deprecated (and can still be used with a warning) and replaced withdf_cols_to_factor
, which also converts logical variables to factor variables.- When providing
SummarizedExperiment
objects containingDelayedMatrix
assays to theHermesData()
constructor, these are silently converted tomatrix
assays to ensure downstream functionality.
- First internal release of the
hermes
package, which contains classes, methods and functions to import, quality-check, filter, normalize, and analyzeRNAseq
counts data for differential expression. hermes
is a successor of thernaseqTools
R package. The core functionality is built on theBioConductor
ecosystem, especially theSummarizedExperiment
class. New users should first begin by reading the "Introduction tohermes
" vignette to become familiar with thehermes
concepts.
- Import
RNAseq
count data into thehermes
ready format. - Annotate gene information from the
Ensembl
database viabiomaRt
. - Add quality control (QC) flags to genes and samples.
- Filter and subset the data set.
- Normalize the counts.
- Produce descriptive plots.
- Perform principal components analysis.
- Produce a templated QC
Rmd
report. - Perform differential expression analysis.