iq
package to protti.
calculate_protein_abundance() now has the method
"iq" again as an option.fetch_pdb() now also retrieves information on
engineered mutations, non-standard monomers, secondary structure and
binding interfaces of ligands.extract_metal_binders() was completely redone. This was
in response to the UniProt update and rework of the binding column
provided by UniProt. This function extracts and concatenates all metal
binding information available for a protein based on the UniProt and
QuickGO databases. Therefore, this function now also takes gene ontology
(GO) information from QuickGO as input. Instead of being able to provide
column names to specific argument of the function you now only provide
the data frames. This makes the function less flexible but reduces the
amount of arguments required to achieve the same result. You just need
to make sure that the input data frames contain columns with the correct
names as stated by the function documentation.fetch_quickgo() was added. It fetches gene ontology
(GO) information from the QuickGO EBI database. The retrieved
information can either be GO annotations for provided UniProt IDs or
Taxon identifiers, a list of all GO terms or a “slims” subset of GO IDs
that can be generated based on provided GO IDs.fetch_chebi() now has the stars argument
with which one can select the evidence levels for which entries should
be retrieved.auth_seq_id column that is part of the output
of the fetch_pdb() function. Previously, the column could
contain duplicated or missing positions. This was formerly identified by
comparing the number of positions within the auth_seq_id
column and the number of residues in the deposited
pdb_sequence. Positions are now correct. The original
output can be found in the auth_seq_id_original
column.calculate_diff_abundance() function the
intensity column can now be retained with the
retain_columns argument. This was previously not possible
until now since this column was used to reduce the annotation dataset.
However, after reassessing the benefit of this filter step, it seemed
not necessary.calculate_diff_abundance() that would not duplicate the
data. However, this seems not to be the case, which can lead to wrong
p-value adjustment. p-value adjustment was originally performed after
the columns indicated in retain_columns are joined back to
the data. Now p-value adjustment is performed prior to retaining columns
as well as only on the subset of data that actually contains p-values.
Previously we (by default filter_NA_missingness = TRUE)
only filtered out NAs in the missingness
column prior to p-value adjustment. However, it is possible that
missingness is not NA but the p-value is
NA. Now for all methods except for "proDA" we
remove NA p-values before p-value adjustment. For
"proDA" data is handled as previously since p-values are
never NA.fetch_pdb() was changed to 100
(from 200). This was done since more information is retrieved now, which
slows to function down and is slightly improved when batch sizes are
smaller.try_query() now only retries to retrieve information
once if the returned message was “Timeout was reached”. In addition, a
timeout and accept argument have been
added.fetch_uniprot() and fetch_uniprot_proteome()
function accordingly. Please be aware that some columns names might have
changed and your code might throw error messages if you did not adjust
it accordingly.map_peptides_on_structure() was still using
“residue” as its input. We now use
find_peptide_in_structure() to generate the correct input
column.fetch_uniprot() and
fetch_uniprot_proteome(). As UniProt has updated their
website and their programmatic access, we now download the information
from the legacy version temporarily. A real fix will follow.fetch_kegg(). The function did not
retrieve any data after the API URL had changed.fetch_eco() was added. It fetches evidence &
conclusion ontology information from the EBI database.qc_proteome_coverage() now has the
reviewed argument that specifies if only reviewed entries
in UniProt should be considered as the proteome. The default is
TRUE and stays the same as previously.volcano_plot() now has the facet_scales
argument that specifies if the scales should be “free” or “fixed” when a
faceted plot is created. The arguments that can be provided are the same
that can be provided to the scales argument of
ggplot2::facet_wrap(). The new default is now
"fixed".pval_distribution_plot() now has the optional
facet_by variable that allows faceting of the plot.map_peptides_on_structure() that caused
an error if the column provided to the auth_seq_id argument
was called “residue”.volcano_plot() that did not calculate
the horizontal cutoff line correctly if there were multiple significance
values that have the same adjusted significance value. Now it correctly
uses the two p-values closest to the cutoff for the line position
calculation. In addition, points were not correctly displayed if no
horizontal cutoff line was created due to no significant values. Now all
values are displayed correctly.try_query() function now
returns errors as a character string if it encounters one. Functions
using try_query() however, still expected NULL
if there was an error. Also adjusted additional fetch functions that do
not use try_query() to fail gracefully and to return
informative messages upon encountering errors.calculate_go_enrichment() now has the argument
label. If TRUE labels are added to the plot
that specify how many proteins from the specific GO term are among the
significant hits. This new argument is by default
TRUE.version argument in the
analyse_functional_network() function.protti depends on and not also
packages it suggests.iq package
from protti for now since iq is currently not
available on CRAN. Once it is available again we will add the
functionalities back.fetch_metal_pdb() now gives more informative feedback
regarding the reasons resources were not fetched correctly.qc_proteome_coverage that flipped the
“Detected” and “Not detected” labels.highlight argument of the
woods_plot() function was used huge plots were generated
when more than 20 proteins were provided to the function. This is fixed
and was due to a wrong variable used in a loop.create_structure_contact_map() now takes an optional
data2 argument in which a second data frame of positions
and structures can be provided. The positions in this data frame are
used for distance calculations relative to the positions provided in the
data argument. This helps to reduce the size of the map
since only comparisons of interest are made. If a selection provided
through the data argument should be compared to the whole
structure the data2 argument should not be provided, which
is the previous default setting.parallel_create_structure_contact_map() can create
structure contact maps using parallel processing. It is also recommended
for sequential processing if a large number of contact maps should be
created. The non-parallel function should not be used if more than 50
maps should be created at the same time. In order to reduce contact maps
to domains or even only peptides, a search pattern is created that
selects only relevant regions. This pattern is shared over all maps and
would become too large if too maps are created at once.qc_cvs()
function now has a grey colour. This ensures that the other colours for
each condition match the colours of other quality control plots.run_order argument of
qc_sample_correlation() now has a gradient colour scheme
that is easier to interpret than distinct colours. The colours are
inspired by the “plasma” colour scheme of the viridis package.qc_intensity_distribution() is of type factor instead of
character the provided factor levels are used for sample ordering in the
plot. This allows for custom sample ordering. If you want this
functionality in other functions, please let us know in an issue on
GitHub.qc_ids(), which caused an error when the
optional condition argument was not provided. Also fixed a
bug that did not take the state of the
remove_na_intensities argument into account.auth_seq_id variable in structure files was handled
as a numeric variable even though it sometimes can be character. This
was fixed in all functions using it. These include:
fetch_pdb_structure(),
fetch_alphafold_prediction() (output is now numeric),
fetch_pdb() (provides the auth_seq_id column
not as a list vector but as character vector with semicolons as
separators), find_peptide_in_structure() (now uses the new
auth_seq_id format and returns the character vector of all
positions of each peptide as auth_seq_id in additon to
auth_seq_id_start and auth_seq_id_end),
create_structure_contact_map(),
map_peptides_on_structure() (now take the new
auth_seq_id column instead of start and end positions as
input).map_peptides_on_structure(), which
caused an error when file names larger than 256 characters were
generated. This was the case if the number of proteins that are part of
one structure is very large (e.g. ribosome). If the number of proteins
is too large to fit into a normal length file name, they are abbreviated
as for example “51_proteins”.fetch_alphafold_prediction() and
fetch_pdb_structure() now return more informative messages
if individual IDs have not been retrieved correctly (e.g. internet
connection problem or outdated ID).calculate_diff_abundance(), comparison names starting with
numbers were not supported. This has been fixed. There are no more
restrictions for condition names.fetch_pdb() was added. It fetches PDB structure
metadata from RCSB.fetch_pdb_structure() was added. It fetches atom level
data for a PDB structure from RCSB.fetch_metal_pdb() was added. It fetches information
about protein-metal binding sites from the MetalPDB database.find_peptide_in_structure() was added. It returns the
positions of a protein region, peptide or amino acid within a protein
structure using UniProt positions as input. This is necessary because
often amino acid positions in a protein structure vary from their
positions in the UniProt protein sequence.map_peptides_on_structure() was added. It can map
peptides onto PDB structures based on their positions. This is
accomplished by replacing the B-factor information in the structure file
with values that allow highlighting peptides when the structure is
coloured by B-factor.create_structure_contact_map() was added. It creates a
contact map of a subset or of all atom or residue distances in a
structure file.calculate_aa_scores() was added. It calculates a score
for each amino acid of a protein based on the product of the
-log10(adjusted p-value) and the absolute log2 fold change per
peptide.woods_plot() recieved the highlight
argument, which allows to highlight peptides in the plot with an
asterisk based on a logical column. It also got export and
export_name arguments which now makes it possible to
directly export plots from the function. The new target
argument allows the user to specify one or multiple proteins from their
data frame for which a plot should be returned, however the default
option is to return plots for all proteins in the provided data frame.
Now it is also possible to provide more than 20 proteins at a time while
the facet argument is TRUE. For every 20
proteins a new plot is created and all of them are returned together in
a list.assign_missingness() and
calculate_diff_abundance() now also take "all"
as input to their ref_condition argument. This will create
all pairwise condition pairs. Previously only one reference condition
could be provided.volcano_plot() can now plot unadjusted p-values while
the horizontal cutoff line is based on the adjusted p-value. the
significance_cutoff argument was modified to also take a
second value which specifies the adjusted p-value column name. In that
case the y-axis of the plot could display p-values that are provided to
the significance argument, while the horizontal cutoff line
is on the scale of adjusted p-values transformed to the scale of
p-values. The provided vector can be
e.g. c(0.05, "adj_pval"). In that case the function looks
for the closest adjusted p-value above and below 0.05 and takes the mean
of the corresponding p-values as the cutoff line. If there is no
adjusted p-value in the data that is below 0.05 no line is displayed.
This allows the user to display volcano plots using p-values while using
adjusted p-values for the cutoff criteria. This is often preferred
because adjusted p-values are related to unadjusted p-values often in a
complex way that makes them hard to be interpret when plotted.Multiple functions have been renamed. More function follow the convention that they should be verbs. Other functions are more similar to each other in their naming. The old functions still work but they are deprecated and will be removed in the future. Please use the new versions instead.
diff_abundance() has been renamed to
calculate_diff_abundance().peptide_type() has been renamed to
assign_peptide_type().median_normalisation() has been renamed to
normalise(). The normalisation method is now defined
through an argument called method.sequence_coverage() has been renamed to
calculate_sequence_coverage().network_analysis() has been renamed to
analyse_functional_network().treatment_enrichment() has been renamed to
calculate_treatment_enrichment().go_enrichment() has been renamed to
calculate_go_enrichment().kegg_enrichment() has been renamed to
calculate_kegg_enrichment().volcano_protti() has been renamed to
volcano_plot().plot_drc_4p() has been renamed to
drc_4p_plot().plot_peptide_profiles() has been renamed to
peptide_profile_plot().plot_pval_distribution() has been renamed to
pval_distribution_plot().calculate_diff_abundance() had a bug that could not
correctly deal with condition names containing spaces when a moderated
t-test or the proDA algorithm was used. This has been fixed.scale_protti() can now deal with the case that a vector
of only equal numbers (e.g. c(1, 1, 1)) is provided. In
this case it always scales all values to 1 for
method = "01" and to 0 for
method = "center".pval_distribution_plot() had a bug centered each bin
around values, which meant that the first and last bin were smaller.
This has been fixed.header argument was removed from
try_query(). It can now be directly supplied to the elipsis
(…) of this function depending on which read method is used. For
type = "text/tab-separated-values" the argument
col_names of the corresponding readr function
read_tsv() can be supplied.split_metal_name() removes more non-metal specific
words that lead to false identifications. Also many metal names
containing a “-” are now considered. A bug was resolved that did not
deal with iron-sulfur cluster information correctly due to a wrong
variable name.extract_metal_binders() now also correctly identifies
monovalent inorganic cations as metals.passed_filter
example column that is used in functions afterwards.diff_abundance() function. Previously, the p-values in the
whole result table were adjusted together. Now adjustments are made by
comparisons. This change only affects analyses that have more than one
comparison (two conditions).fetch_uniprot(),
fetch_uniprot_proteome() and fetch_kegg().
They now fail gracefully with informative messages if there is no
internet connection, the database times out or the data resource has
changed. Specifically the underlying try_query() function
was changed.