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Small clarifications to the report
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@csoneson
csoneson committed May 4, 2024
1 parent 3359934 commit fbe7a21
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15 changes: 10 additions & 5 deletions R/textSnippets.R
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Original file line number Diff line number Diff line change
Expand Up @@ -74,7 +74,8 @@ testText <- function(testType, minlFC = 0, samSignificance = TRUE) {
"feature, see section 13.2 in the [limma user guide]",
"(https://www.bioconductor.org/packages/devel/bioc/vignettes",
"/limma/inst/doc/usersguide.pdf). ",
"In addition to the feature-wise tests, we apply the camera ",
"If requested, in addition to the feature-wise tests, we ",
"apply the camera ",
"method [@Wu2012camera] to test for significance of each ",
"included feature collection. These tests are based on the ",
"t-statistics returned from limma.")
Expand All @@ -91,7 +92,8 @@ testText <- function(testType, minlFC = 0, samSignificance = TRUE) {
"feature, see section 13.2 in the [limma user guide]",
"(https://www.bioconductor.org/packages/devel/bioc/vignettes",
"/limma/inst/doc/usersguide.pdf). ",
"In addition to the feature-wise tests, we apply the camera ",
"If requested, in addition to the feature-wise tests, we ",
"apply the camera ",
"method [@Wu2012camera] to test for significance of each ",
"included feature collection. These tests are based on the ",
"t-statistics returned from limma.")
Expand All @@ -103,7 +105,8 @@ testText <- function(testType, minlFC = 0, samSignificance = TRUE) {
"statistic [@Tusher2001sam], and estimate the false ",
"discovery rate at different thresholds using permutations, ",
"mimicking the approach used by Perseus [@Tyanova2016perseus]. ",
"In addition to the feature-wise tests, we apply the camera ",
"If requested, in addition to the feature-wise tests, we ",
"apply the camera ",
"method [@Wu2012camera] to test for significance of each ",
"included feature collection. These tests are based on the ",
"SAM statistics calculated from the t-statistics and the ",
Expand All @@ -115,7 +118,8 @@ testText <- function(testType, minlFC = 0, samSignificance = TRUE) {
"features show significant changes, we calculate ",
"adjusted p-values using the Benjamini-Hochberg method ",
"[@BenjaminiHochberg1995fdr]. ",
"In addition to the feature-wise tests, we apply the camera ",
"If requested, in addition to the feature-wise tests, we ",
"apply the camera ",
"method [@Wu2012camera] to test for significance of each ",
"included feature collection. These tests are based on the ",
"t-statistics.")
Expand All @@ -125,7 +129,8 @@ testText <- function(testType, minlFC = 0, samSignificance = TRUE) {
"For this, we use the ",
"[proDA](https://bioconductor.org/packages/proDA/) ",
"R/Bioconductor package [@AhlmannEltze2020proda]. ",
"In addition to the feature-wise tests, we apply the camera ",
"If requested, in addition to the feature-wise tests, we ",
"apply the camera ",
"method [@Wu2012camera] to test for significance of each ",
"included feature collection. These tests are based on the ",
"t-statistics returned from proDA.")
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42 changes: 29 additions & 13 deletions inst/extdata/process_basic_template.Rmd
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Expand Up @@ -171,6 +171,9 @@ cat("\n````\n\n")

# Settings {#settings-table}

The table below provides a summary of the settings that were specified when
running `einprot`.

```{r settings-table}
settingsList <- list(
"Include only samples (if applicable)" = paste(includeOnlySamples,
Expand Down Expand Up @@ -356,7 +359,9 @@ DT::datatable(as.data.frame(colData(sce)),
# Overview of the workflow

We already now define the names of the assays we will be generating
and using later in the workflow.
and using later in the workflow. The first column in the table below
contains generic names representing the 'stage' that each assay
corresponds to. The second column contains the actual assay names.

```{r define-assaynames}
aNames <- defineAssayNames(aName = aName, normMethod = normMethod,
Expand Down Expand Up @@ -633,7 +638,7 @@ plotImputationDistribution(sce, assayToPlot = aNames$assayImputed,

# Overall distribution of log2 feature intensities

Next we consider the overall distribution of log2-intensities among the
The boxplots below show the overall distribution of log2-intensities among the
samples (after imputation).

```{r intensity-distribution-imputed, fig.width = min(14, max(7, 0.5 * ncol(sce))), fig.height = 5/7 * min(14, max(7, 0.5 * ncol(sce)))}
Expand Down Expand Up @@ -892,12 +897,12 @@ to generate STRING networks [@Szklarczyk2021string] (separately for the
up- and downregulated ones), which are included in the pdf file. Any features
explicitly requested (see the [table above](#settings-table)) are also labeled
in the volcano plots.
In addition to these pdf files, if "complexes" is specified to be included in
the feature collections (and tested for significance using camera), we also
In addition to these pdf files, if any feature collection is specified
(and tested for significance using camera), we also
generate a multi-page pdf file showing the position of the features of each
significantly differentially abundant complex in the volcano plot, as well
significantly differentially abundant collection in the volcano plot, as well
as bar plots of the features' abundance values in the compared samples. This pdf
file is only generated if there is at least one significant complex (with
file is only generated if there is at least one significant collection (with
adjusted p-value below the specified complexFDRThr=`r complexFDRThr`).


Expand Down Expand Up @@ -1089,9 +1094,13 @@ for (nm in names(testres$topsets)) {

# Table with direct database links to sequences, functional information and predicted structures {#linktable}

The table below provides autogenerated links to the UniProt and
AlphaFold pages (as well as selected organism-specific databases) for the
majority protein IDs corresponding to each feature in the data set.
The table below provides autogenerated links to the
[UniProt](https://www.uniprot.org/),
[AlphaFold](https://alphafold.ebi.ac.uk/),
[Complex Portal](https://www.ebi.ac.uk/complexportal/home) and
[BioGRID pages](https://thebiogrid.org/) (as well as selected
organism-specific databases) for the
protein IDs corresponding to each feature in the data set.
The 'pid' column represents the unique feature ID used by `einprot`, and
the `einprotLabel` column contains the user-defined feature labels.
UniProt is a resource of protein sequence and functional information
Expand All @@ -1101,7 +1110,8 @@ predictions for the human proteome and other key proteins of interest.
Note that (depending on the species) many proteins are not yet covered in
AlphaFold (in this case, the link below will lead to a non-existent page), and
that numeric values are rounded to four significant digits to increase
readability.
readability. The table can be filtered and searched, and exported to either
csv or Excel format.

```{r linktable, warning = FALSE}
linkTable <- makeDbLinkTable(
Expand Down Expand Up @@ -1227,11 +1237,11 @@ interactivePCAs
# Heatmap with hierarchical clustering

For another birds-eye view of the data, we represent it using a heatmap of
the (imputed and normalized) log intensities, and cluster the samples and
the log intensities (imputed and normalized using the methods defined above),
and cluster the samples and
proteins using hierarchical clustering. In the first heatmap below, the values
represent the normalized log intensities directly. In the second heatmap,
the values for each protein have been centered to mean 0. The latter is also
exported to a pdf file with row labels for further exploration.
the values for each protein have been centered to mean 0.

```{r heatmap, message=FALSE, fig.width = min(14, max(7, 0.5 * ncol(sce))), fig.height = 8/7 * min(14, max(7, 0.5 * ncol(sce)))}
if (addHeatmaps) {
Expand All @@ -1246,6 +1256,12 @@ if (addHeatmaps) {
}
```

A mean-centered heatmap is also exported to a pdf file with row labels for
further exploration. In this heatmap, no row dendrogram is displayed, and
samples are split by the `group` annotation and clustered within each such
group. In addition, an annotation column with the fraction of missing values for
each feature is added.

```{r save-heatmap, results="hide"}
## Save to pdf (show row names, but no row dendrogram, order samples by group)
if (addHeatmaps) {
Expand Down

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