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shiny-timecourse-inspector
Commit c8a2e909 authored by dmattek's avatar dmattek
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Rebuilt section for creating internal dt. Added ObjectNumber to labels... 

Rebuilt section for creating internal dt. Added ObjectNumber to labels displayd in interactive trajectory plotting.
parent 0f2d73ff
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Showing with 82 additions and 88 deletions
modules/auxfunc.R
View file @ c8a2e909
......@@ -175,6 +175,7 @@ myGgplotTraj = function(dt.arg, # data table
stim.bar.width.arg = 0.5,
aux.label1 = NULL, # 1st point label; used for interactive plotting; displayed in the tooltip; typically used to display values of column holding x & y coordinates
aux.label2 = NULL,
aux.label3 = NULL,
stat.arg = c('', 'mean', 'CI', 'SE')) {
# match arguments for stat plotting
......@@ -187,7 +188,8 @@ myGgplotTraj = function(dt.arg, # data table
y = y.arg,
group = group.arg,
label = aux.label1,
label2 = aux.label2))
label2 = aux.label2,
label3 = aux.label3))
if (is.null(line.col.arg)) {
p.tmp = p.tmp +
......
modules/trajPlot.R
View file @ c8a2e909
......@@ -122,7 +122,7 @@ modTrajPlot = function(input, output, session, in.data, in.facet = 'group', in.f
# Future: change such that a column with colouring status is chosen by the user
# colour trajectories, if dataset contains mi.din column
# colour trajectories, if dataset contains mid.in column
# with filtering status of trajectory
if (sum(names(loc.dt) %in% 'mid.in') > 0)
loc.line.col.arg = 'mid.in'
......@@ -138,6 +138,14 @@ modTrajPlot = function(input, output, session, in.data, in.facet = 'group', in.f
else
locPos = FALSE
# check if column with ObjectNumber is present
if (sum(names(loc.dt) %like% 'obj.num') == 1)
locObjNum = TRUE
else
locObjNum = FALSE
# If in.facet.color present,
# make sure to include the same number of colours in the palette,
# as the number of groups in dt.
......@@ -172,6 +180,7 @@ modTrajPlot = function(input, output, session, in.data, in.facet = 'group', in.f
line.col.arg = loc.line.col.arg,
aux.label1 = if (locPos) 'pos.x' else NULL,
aux.label2 = if (locPos) 'pos.y' else NULL,
aux.label3 = if (locObjNum) 'obj.num' else NULL,
stat.arg = input$chBPlotTrajStat
)
......
server.R
View file @ c8a2e909
......@@ -489,10 +489,14 @@ shinyServer(function(input, output, session) {
# realtime - selected from input
# y - measurement selected from input
# (can be a single column or result of an operation on two cols)
# id - trackObjectsLabelUni (created in dataMod)
# id - trackObjectsLabelUni; created in dataMod based on TrackObjects_Label
# and FOV column such as Series or Site (if TrackObjects_Label not unique across entire dataset)
# group - grouping variable for facetting from input
# mid.in - column with trajectory selection status from the input file or
# highlight status from UI
# highlight status from UI
# (column created if mid.in present in uploaded data or tracks are selected in the UI)
# obj.num - created if ObjectNumber column present in the input data
# pos.x,y - created if columns with x and y positions present in the input data
data4trajPlot <- reactive({
cat(file = stderr(), 'data4trajPlot\n')
......@@ -500,7 +504,7 @@ shinyServer(function(input, output, session) {
if (is.null(loc.dt))
return(NULL)
# create expression for 'y' column based on measurements and math operations selected in UI
if (input$inSelMath == '')
loc.s.y = input$inSelMeas1
else if (input$inSelMath == '1 / ')
......@@ -508,7 +512,7 @@ shinyServer(function(input, output, session) {
else
loc.s.y = paste0(input$inSelMeas1, input$inSelMath, input$inSelMeas2)
# create expression for parsing
# create expression for 'group' column
# creates a merged column based on other columns from input
# used for grouping of plot facets
if (input$chBgroup) {
......@@ -529,7 +533,7 @@ shinyServer(function(input, output, session) {
# Assign tracks selected for highlighting in UI
loc.tracks.highlight = input$inSelHighlight
locBut = input$chBhighlightTraj
locButHighlight = input$chBhighlightTraj
# Find column names with position
......@@ -541,56 +545,66 @@ shinyServer(function(input, output, session) {
else
locPos = FALSE
# Find column names with ObjectNumber
# This is different from TrackObject_Label and is handy to keep
# because labels on segmented images are typically ObjectNumber
loc.s.objnum = names(loc.dt)[names(loc.dt) %like% c('ObjectNumber')]
if (length(loc.s.objnum) == 1)
locObjNum = TRUE
else
locObjNum = FALSE
# if dataset contains column mid.in with trajectory filtering status,
# then, include it in plotting
if (sum(names(loc.dt) %in% 'mid.in') > 0) {
if (locPos) # position columns present
loc.out = loc.dt[, .(
y = eval(parse(text = loc.s.y)),
id = trackObjectsLabelUni,
group = eval(parse(text = loc.s.gr)),
realtime = eval(parse(text = loc.s.rt)),
pos.x = get(loc.s.pos.x),
pos.y = get(loc.s.pos.y),
mid.in = mid.in
)] else
loc.out = loc.dt[, .(
y = eval(parse(text = loc.s.y)),
id = trackObjectsLabelUni,
group = eval(parse(text = loc.s.gr)),
realtime = eval(parse(text = loc.s.rt)),
mid.in = mid.in
)]
# add 3rd level with status of track selection
# to a column with trajectory filtering status
if (locBut) {
loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', mid.in)]
}
} else {
if (locPos) # position columns present
loc.out = loc.dt[, .(
y = eval(parse(text = loc.s.y)),
id = trackObjectsLabelUni,
group = eval(parse(text = loc.s.gr)),
realtime = eval(parse(text = loc.s.rt)),
pos.x = get(loc.s.pos.x),
pos.y = get(loc.s.pos.y)
)] else
loc.out = loc.dt[, .(
y = eval(parse(text = loc.s.y)),
id = trackObjectsLabelUni,
group = eval(parse(text = loc.s.gr)),
realtime = eval(parse(text = loc.s.rt))
)]
if (sum(names(loc.dt) %in% 'mid.in') > 0)
locMidIn = TRUE
else
locMidIn = FALSE
## Build expression for selecting columns from loc.dt
# Core columns
s.colexpr = paste0('.(y = ', loc.s.y,
', id = trackObjectsLabelUni',
', group = ', loc.s.gr,
', realtime = ', loc.s.rt)
# account for the presence of 'mid.in' column in uploaded data
if(locMidIn)
s.colexpr = paste0(s.colexpr,
', mid.in = mid.in')
# include position x,y columns in uploaded data
if(locPos)
s.colexpr = paste0(s.colexpr,
', pos.x = ', loc.s.pos.x,
', pos.y = ', loc.s.pos.y)
# include ObjectNumber column
if(locObjNum)
s.colexpr = paste0(s.colexpr,
', obj.num = ', loc.s.objnum)
# close bracket, finish the expression
s.colexpr = paste0(s.colexpr, ')')
# create final dt for output based on columns selected above
loc.out = loc.dt[, eval(parse(text = s.colexpr))]
# if track selection ON
if (locButHighlight){
# add a 3rd level with status of track selection
# to a column with trajectory filtering status in the uploaded file
if(locMidIn)
loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', mid.in)]
else
# add a column with status of track selection
if (locBut) {
loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', 'NOT SEL')]
}
loc.out[, mid.in := ifelse(id %in% loc.tracks.highlight, 'SELECTED', 'NOT SEL')]
}
## Interpolate NA's and data points not include
# From: https://stackoverflow.com/questions/28073752/r-how-to-add-rows-for-missing-values-for-unique-group-sequences
......@@ -598,35 +612,6 @@ shinyServer(function(input, output, session) {
setkey(loc.out, group, id, realtime)
loc.out = loc.out[setkey(loc.out[, .(min(realtime):max(realtime)), by = .(group, id)], group, id, V1)]
# # dt with a full span of realtime for every group and cell id
# # (here id is already unique across entire dataset) combination
# loc.dt.IdRt = CJ(id = loc.out[['id']],
# realtime = loc.out[['realtime']],
# unique = TRUE, sorted = TRUE )
#
# print('loc.dt.IdRt:')
# print(loc.dt.IdRt)
#
# # dt with all cell id's and their associated group names
# loc.dt.GrId = loc.out[, .(group = first(group)), by = id]
#
# print('loc.dt.GrId:')
# print(loc.dt.GrId)
#
# # merge the 2 above to have all id~rt combinations with associated group names
# loc.dt.GrIdRt = merge(loc.dt.IdRt, loc.dt.GrId, by = 'id')
#
# print('loc.dt.GrIdRt:')
# print(loc.dt.GrIdRt)
#
# # join with the original to expand it and create NA's for non-existing group-id-rt combinations
# loc.out = merge(loc.dt.GrIdRt, loc.out, all.x = TRUE, by = c('group', 'id', 'realtime'))
#
# print('loc.out:')
# print(loc.out)
#
# x-check: print all rows with NA's
print('Rows with NAs:')
print(loc.out[rowSums(is.na(loc.out)) > 0, ])
......@@ -634,14 +619,12 @@ shinyServer(function(input, output, session) {
# Merge will create NA's where a realtime is missing.
# Also, NA's may be already present in the dataset'.
# Interpolate (linear) them with na.interpolate
if(locPos) {
if(locPos)
s.cols = c('y', 'pos.x', 'pos.y')
loc.out[, (s.cols) := lapply(.SD, na.interpolation), by = id, .SDcols = s.cols]
}
else {
else
s.cols = c('y')
loc.out[, (s.cols) := lapply(.SD, na.interpolation), by = id, .SDcols = s.cols]
}
loc.out[, (s.cols) := lapply(.SD, na.interpolation), by = id, .SDcols = s.cols]
# !!! Current issue with interpolation:
......
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