Commit c78bb2e3 authored by dmattek's avatar dmattek

Added tooltips for normalisation section

parent 82458782
......@@ -165,9 +165,13 @@ help.text.short = c(
'Select columns to group data according to treatment, condition, etc.',
'Select math operation to perform on a single or two columns,',
'Select range of time for further processing.',
'Normalise time series to a selected region.',
'Divide measurments by the mean/median or calculate z-score with respect to selected time span.',
'Download time series after modification in this section.',
'Long format: a row is a single data point. Wide format: a row contains entire time series with columns as time points.'
'Long format: a row is a single data point. Wide format: a row is a time series with columns as time points.',
'Fold-change or z-score with respect to selected time span.',
'Normalise with respect to this time span.',
'Calculate fold-change and z-score using the median and Median Absolute Deviation, instead of the mean and sd.',
'Normalise to mean/median of selected time calculated globally, per group, or for individual time series.'
)
# Functions for data processing ----
......
......@@ -327,19 +327,28 @@ shinyServer(function(input, output, session) {
})
# UI-side-panel-normalization ----
# select normalisation method
# - fold-change calculates fold change with respect to the mean
# - z-score calculates z-score of the selected regione of the time series
output$uiChBnorm = renderUI({
if (DEB)
cat(file = stdout(), 'server:uiChBnorm\n')
if (input$chBnorm) {
tagList(
radioButtons(
'rBnormMeth',
label = 'Select method',
choices = list('fold-change' = 'mean', 'z-score' = 'z.score')
choices = list('fold-change' = 'mean', 'z-score' = 'z.score'),
width = "40%"
),
bsTooltip('rBnormMeth', help.text.short[11], placement = "right", trigger = "hover", options = NULL)
)
}
})
# select the region of the time series for normalisation
output$uiSlNorm = renderUI({
if (DEB)
cat(file = stdout(), 'server:uiSlNorm\n')
......@@ -353,36 +362,49 @@ shinyServer(function(input, output, session) {
locRTmin = min(locTpts)
locRTmax = max(locTpts)
tagList(
sliderInput(
'slNormRtMinMax',
label = 'Time range for norm.',
label = 'Time span',
min = locRTmin,
max = locRTmax,
value = c(locRTmin, 0.1 * locRTmax),
step = 1
),
bsTooltip('slNormRtMinMax', help.text.short[12], placement = "right", trigger = "hover", options = NULL)
)
}
})
# use robust stats (median instead of mean, mad instead of sd)
output$uiChBnormRobust = renderUI({
if (DEB)
cat(file = stdout(), 'server:uiChBnormRobust\n')
if (input$chBnorm) {
tagList(
checkboxInput('chBnormRobust',
label = 'Robust stats',
FALSE)
FALSE,
width = "40%"),
bsTooltip('chBnormRobust', help.text.short[13], placement = "right", trigger = "hover", options = NULL)
)
}
})
# choose whether normalisation should be calculated for the entire dataset, group, or trajectory
output$uiChBnormGroup = renderUI({
if (DEB)
cat(file = stdout(), 'server:uiChBnormGroup\n')
if (input$chBnorm) {
tagList(
radioButtons('chBnormGroup',
label = 'Normalisation grouping',
choices = list('Entire dataset' = 'none', 'Per facet' = 'group', 'Per trajectory' = 'id'))
choices = list('Entire dataset' = 'none', 'Per group' = 'group', 'Per trajectory' = 'id'),
width = "40%"),
bsTooltip('chBnormGroup', help.text.short[14], placement = "right", trigger = "hover", options = NULL)
)
}
})
......
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