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Commit 17a85ab0 authored by Maciej Dobrzynski's avatar Maciej Dobrzynski
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Fixed NA removal due to outliers

parent bc10b4d1
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modules/auxfunc.R
View file @ 17a85ab0
......@@ -14,6 +14,10 @@ require(Hmisc) # for CI calculation
# Global parameters ----
# if true, additional output printed to R console
DEB = T
# font sizes in pts for plots
PLOTFONTBASE = 12
PLOTFONTAXISTEXT = 12
......
modules/selOutliers.R
View file @ 17a85ab0
......@@ -20,7 +20,8 @@ modSelOutliersUI = function(id, label = "Outlier Selection") {
min = 0,
max = 100,
value = 0,
step = 0.05, width = '100px')
step = 0.05, width = '100px'),
checkboxInput(ns('chBtrajInter'), 'Interpolate gaps?', value = F)
),
column(2,
radioButtons(ns('rbOutliersType'),
......@@ -62,7 +63,7 @@ modSelOutliers = function(input, output, session, in.data) {
# Display number of tracks and outliers
output$txtOutliersPerc <- renderText({
cat(file = stderr(), 'modSelOutliers: txtOutliersPerc\n')
cat(file = stdout(), 'modSelOutliers: txtOutliersPerc\n')
sprintf('<b>%d total track(s)<br>%d outlier track(s)<br>%d outlier point(s)</b><br>',
nCellsCounter[['nCellsOrig']],
......@@ -85,7 +86,7 @@ modSelOutliers = function(input, output, session, in.data) {
# Identify outliers and remove them from dt
dtReturn = reactive({
cat(file = stderr(), 'modSelOutliers: dtReturn\n')
cat(file = stdout(), 'modSelOutliers: dtReturn\n')
loc.out = in.data()
......@@ -93,13 +94,13 @@ modSelOutliers = function(input, output, session, in.data) {
return(NULL)
}
# Remove outliers if the slider with percentage of data is smaller than 100
if (input$numOutliersPerc < 100) {
# Remove outliers if the field with percentage of data to remove is greater than 0
if (input$numOutliersPerc > 0) {
# store the number of trajectories before prunning
nCellsCounter[['nCellsOrig']] = length(unique(loc.out[['id']]))
# scale all points (independently per track)
# scale all measurement points
loc.out[, y.sc := scale(get(COLY))]
# Identify outlier points
......@@ -112,18 +113,22 @@ modSelOutliers = function(input, output, session, in.data) {
'bot' = {loc.outpts = loc.out[ y.sc < quantile(y.sc, input$numOutliersPerc * 0.01, na.rm = T)]}
)
if (DEB) {
cat(file = stdout(), 'selOutliers.dtReturn: Outlier points:\n')
print(loc.outpts)
}
if (input$slOutliersGapLen > 1) {
# remove tracks with gaps longer than the value set in slOutliersGapLen
# shorter gaps are interpolated linearly
# add index column per trajecory
loc.out[, (COLIDX) := 1:.N, by = c(COLID)]
# remove single outlier points (anti-join)
# From: https://stackoverflow.com/a/46333620/1898713
loc.out = loc.out[!loc.outpts, on = names(loc.outpts)]
# calculate diff on index column to see the length of gaps due to removed points
# the value of that column corresponds to the gap length (hence the "-1")
loc.out[, (COLIDXDIFF) := c(1, diff(get(COLIDX))) - 1, by = c(COLID)]
......@@ -131,23 +136,50 @@ modSelOutliers = function(input, output, session, in.data) {
# get track ids where the max gap is equal to or longer than the threshold
loc.idgaps = loc.out[, max(get(COLIDXDIFF)), by = c(COLID)][V1 >= input$slOutliersGapLen, get(COLID)]
# remove outlier tracks with gaps longer than the value set in slOutliersGapLen
loc.out = loc.out[!(get(COLID) %in% unique(loc.idgaps))]
# fill removed outliers with NA's
loc.out = loc.out[setkeyv(loc.out[, .(seq(min(get(COLIDX), na.rm = T), max(get(COLIDX), na.rm = T), 1)), by = c(COLGR, COLID)], c(COLGR, COLID, 'V1'))]
# interpolate gaps with NAs
if( (COLPOSX %in% names(loc.out)) & (COLPOSY %in% names(loc.out)) )
s.cols = c(COLY, COLPOSX, COLPOSY)
else
s.cols = c(COLY)
# Here, the missing part in interpolation of mid.in column (for highlighting trajectories)
loc.out[, (s.cols) := lapply(.SD, na.interpolation), by = c(COLID), .SDcols = s.cols]
if (DEB) {
cat(file = stdout(), '\nselOutliers.dtReturn: Track IDs with max gap >= threshold:\n')
if (length(loc.idgaps) > 0)
print(loc.idgaps) else
cat("none\n")
}
# remove outlier tracks with gaps longer than the value set in slOutliersGapLen
if (length(loc.idgaps) > 0)
loc.out = loc.out[!(get(COLID) %in% unique(loc.idgaps))]
# clean
loc.out[, c(COLIDX, COLIDXDIFF) := NULL]
# interpolate gaps due to outliers
if (input$chBtrajInter) {
# fill removed outliers with NA's
setkeyv(loc.out, c(COLGR, COLID, COLRT))
loc.out = loc.out[setkeyv(loc.out[, .(seq(min(get(COLRT), na.rm = T), max(get(COLRT), na.rm = T), 1)), by = c(COLGR, COLID)], c(COLGR, COLID, 'V1'))]
# x-check: print all rows with NA's
if (DEB) {
cat(file = stdout(), '\nselOutliers.dtReturn: Rows with NAs due to outliers:\n')
print(loc.out[rowSums(is.na(loc.out)) > 0, ])
}
# NA's may be already present in the dataset'.
# Interpolate (linear) them with na.interpolate as well
if( (COLPOSX %in% names(loc.out)) & (COLPOSY %in% names(loc.out)) )
s.cols = c(COLY, COLPOSX, COLPOSY)
else
s.cols = c(COLY)
# Apparently the loop is faster than lapply+SDcols
for(col in s.cols) {
# Interpolated columns should be of type numeric (float)
# This is to ensure that interpolated columns are of porper type.
data.table::set(loc.out, j = col, value = as.numeric(loc.out[[col]]))
loc.out[, (col) := na.interpolation(get(col)), by = c(COLID)]
}
}
} else {
# remove outlier tracks with gaps of length 1 time point
......@@ -156,7 +188,7 @@ modSelOutliers = function(input, output, session, in.data) {
# clean
loc.out[, y.sc := NULL]
# count number of trajectories after removing outlier tracks
nCellsCounter[['nCellsAfter']] = length(unique(loc.out[[COLID]]))
......@@ -169,7 +201,9 @@ modSelOutliers = function(input, output, session, in.data) {
}
# return cleaned dt
return(loc.out)
if (nrow(loc.out) < 1)
return(NULL) else
return(loc.out)
})
......
server.R
View file @ 17a85ab0
......@@ -52,14 +52,17 @@ shinyServer(function(input, output, session) {
# Generate random dataset
dataGen1 <- eventReactive(input$inDataGen1, {
cat("dataGen1\n")
if (DEB)
cat("dataGen1\n")
return(LOCgenTraj(in.nwells = 3, in.addout = 3))
})
# Load main data file
dataLoadNuc <- eventReactive(input$inButLoadNuc, {
cat("dataLoadNuc\n")
if (DEB)
cat("dataLoadNuc\n")
locFilePath = input$inFileLoadNuc$datapath
counter$dataLoadNuc <- input$inButLoadNuc - 1
......@@ -78,7 +81,9 @@ shinyServer(function(input, output, session) {
# Load data with trajectories to remove
dataLoadTrajRem <- eventReactive(input$inButLoadTrajRem, {
cat(file = stderr(), "dataLoadTrajRem\n")
if (DEB)
cat(file = stdout(), "dataLoadTrajRem\n")
locFilePath = input$inFileLoadTrajRem$datapath
counter$dataLoadTrajRem <- input$inButLoadTrajRem - 1
......@@ -92,7 +97,9 @@ shinyServer(function(input, output, session) {
# Load data with stimulation pattern
dataLoadStim <- eventReactive(input$inButLoadStim, {
cat(file = stderr(), "dataLoadStim\n")
if (DEB)
cat(file = stdout(), "dataLoadStim\n")
locFilePath = input$inFileLoadStim$datapath
counter$dataLoadStim <- input$inButLoadStim - 1
......@@ -107,7 +114,8 @@ shinyServer(function(input, output, session) {
# UI for loading csv with cell IDs for trajectory removal
output$uiFileLoadTrajRem = renderUI({
cat(file = stderr(), 'UI uiFileLoadTrajRem\n')
if (DEB)
cat(file = stdout(), 'UI uiFileLoadTrajRem\n')
if(input$chBtrajRem)
fileInput(
......@@ -118,7 +126,8 @@ shinyServer(function(input, output, session) {
})
output$uiButLoadTrajRem = renderUI({
cat(file = stderr(), 'UI uiButLoadTrajRem\n')
if (DEB)
cat(file = stdout(), 'UI uiButLoadTrajRem\n')
if(input$chBtrajRem)
actionButton("inButLoadTrajRem", "Load Data")
......@@ -126,7 +135,8 @@ shinyServer(function(input, output, session) {
# UI for loading csv with stimulation pattern
output$uiFileLoadStim = renderUI({
cat(file = stderr(), 'UI uiFileLoadStim\n')
if (DEB)
cat(file = stdout(), 'UI uiFileLoadStim\n')
if(input$chBstim)
fileInput(
......@@ -137,7 +147,8 @@ shinyServer(function(input, output, session) {
})
output$uiButLoadStim = renderUI({
cat(file = stderr(), 'UI uiButLoadStim\n')
if (DEB)
cat(file = stdout(), 'UI uiButLoadStim\n')
if(input$chBstim)
actionButton("inButLoadStim", "Load Data")
......@@ -147,7 +158,9 @@ shinyServer(function(input, output, session) {
# UI-side-panel-column-selection ----
output$varSelTrackLabel = renderUI({
cat(file = stderr(), 'UI varSelTrackLabel\n')
if (DEB)
cat(file = stdout(), 'UI varSelTrackLabel\n')
locCols = getDataNucCols()
locColSel = locCols[grep('(T|t)rack|ID|id', locCols)[1]] # index 1 at the end in case more matches; select 1st; matches TrackLabel, tracklabel, Track Label etc
......@@ -161,7 +174,9 @@ shinyServer(function(input, output, session) {
})
output$varSelTime = renderUI({
cat(file = stderr(), 'UI varSelTime\n')
if (DEB)
cat(file = stdout(), 'UI varSelTime\n')
locCols = getDataNucCols()
locColSel = locCols[grep('(T|t)ime|Metadata_T', locCols)[1]] # index 1 at the end in case more matches; select 1st; matches RealTime, realtime, real time, etc.
......@@ -175,7 +190,8 @@ shinyServer(function(input, output, session) {
})
output$varSelTimeFreq = renderUI({
cat(file = stderr(), 'UI varSelTimeFreq\n')
if (DEB)
cat(file = stdout(), 'UI varSelTimeFreq\n')
if (input$chBtrajInter) {
numericInput(
......@@ -194,14 +210,15 @@ shinyServer(function(input, output, session) {
# e.g.1 Stim_All_Ch or Stim_All_S.
# e.g.2 a combination of 3 columns called Stimulation_...
output$varSelGroup = renderUI({
cat(file = stderr(), 'UI varSelGroup\n')
if (DEB)
cat(file = stdout(), 'UI varSelGroup\n')
if (input$chBgroup) {
locCols = getDataNucCols()
if (!is.null(locCols)) {
locColSel = locCols[grep('(G|g)roup|(S|s)tim_All|(S|s)timulation|(S|s)ite', locCols)[1]]
locColSel = locCols[grep('(G|g)roup|(S|s)tim|(S|s)timulation|(S|s)ite', locCols)[1]]
#cat('UI varSelGroup::locColSel ', locColSel, '\n')
selectInput(
......@@ -219,11 +236,12 @@ shinyServer(function(input, output, session) {
# UI for selecting grouping to add to track ID to make
# the track ID unique across entire dataset
output$varSelSite = renderUI({
cat(file = stderr(), 'UI varSelSite\n')
if (DEB)
cat(file = stdout(), 'UI varSelSite\n')
if (input$chBtrackUni) {
locCols = getDataNucCols()
locColSel = locCols[grep('(S|s)ite|(S|s)eries', locCols)[1]] # index 1 at the end in case more matches; select 1st
locColSel = locCols[grep('(S|s)ite|(S|s)eries|(F|f)ov', locCols)[1]] # index 1 at the end in case more matches; select 1st
selectInput(
'inSelSite',
......@@ -238,11 +256,12 @@ shinyServer(function(input, output, session) {
output$varSelMeas1 = renderUI({
cat(file = stderr(), 'UI varSelMeas1\n')
if (DEB)
cat(file = stdout(), 'UI varSelMeas1\n')
locCols = getDataNucCols()
if (!is.null(locCols)) {
locColSel = locCols[grep('objCyto_Intensity_MeanIntensity_imErkCor|(R|r)atio|(I|i)ntensity|y', locCols)[1]] # index 1 at the end in case more matches; select 1st
locColSel = locCols[grep('(R|r)atio|(I|i)ntensity|y|Meas', locCols)[1]] # index 1 at the end in case more matches; select 1st
selectInput(
'inSelMeas1',
......@@ -256,12 +275,14 @@ shinyServer(function(input, output, session) {
output$varSelMeas2 = renderUI({
cat(file = stderr(), 'UI varSelMeas2\n')
if (DEB)
cat(file = stdout(), 'UI varSelMeas2\n')
locCols = getDataNucCols()
if (!is.null(locCols) &&
!(input$inSelMath %in% c('', '1 / '))) {
locColSel = locCols[grep('objNuc_Intensity_MeanIntensity_imErkCor', locCols)[1]] # index 1 at the end in case more matches; select 1st
locColSel = locCols[grep('(R|r)atio|(I|i)ntensity|y|Meas', locCols)[1]] # index 1 at the end in case more matches; select 1st
selectInput(
'inSelMeas2',
......@@ -275,7 +296,8 @@ shinyServer(function(input, output, session) {
# UI-side-panel-trim x-axis (time) ----
output$uiSlTimeTrim = renderUI({
cat(file = stderr(), 'UI uiSlTimeTrim\n')
if (DEB)
cat(file = stdout(), 'UI uiSlTimeTrim\n')
if (input$chBtimeTrim) {
locTpts = getDataTpts()
......@@ -300,7 +322,8 @@ shinyServer(function(input, output, session) {
# UI-side-panel-normalization ----
output$uiChBnorm = renderUI({
cat(file = stderr(), 'UI uiChBnorm\n')
if (DEB)
cat(file = stdout(), 'UI uiChBnorm\n')
if (input$chBnorm) {
radioButtons(
......@@ -312,7 +335,8 @@ shinyServer(function(input, output, session) {
})
output$uiSlNorm = renderUI({
cat(file = stderr(), 'UI uiSlNorm\n')
if (DEB)
cat(file = stdout(), 'UI uiSlNorm\n')
if (input$chBnorm) {
locTpts = getDataTpts()
......@@ -335,7 +359,8 @@ shinyServer(function(input, output, session) {
})
output$uiChBnormRobust = renderUI({
cat(file = stderr(), 'UI uiChBnormRobust\n')
if (DEB)
cat(file = stdout(), 'UI uiChBnormRobust\n')
if (input$chBnorm) {
checkboxInput('chBnormRobust',
......@@ -345,7 +370,8 @@ shinyServer(function(input, output, session) {
})
output$uiChBnormGroup = renderUI({
cat(file = stderr(), 'UI uiChBnormGroup\n')
if (DEB)
cat(file = stdout(), 'UI uiChBnormGroup\n')
if (input$chBnorm) {
radioButtons('chBnormGroup',
......@@ -371,6 +397,7 @@ shinyServer(function(input, output, session) {
locInLoadNuc = input$inButLoadNuc
#locInLoadStim = input$inButLoadStim
# Don't wrap around if(DEB)
cat(
"dataInBoth\ninGen1: ",
locInGen1,
......@@ -408,7 +435,9 @@ shinyServer(function(input, output, session) {
# return column names of the main dt
getDataNucCols <- reactive({
cat(file = stderr(), 'getDataNucCols: in\n')
if (DEB)
cat(file = stdout(), 'getDataNucCols: in\n')
loc.dt = dataInBoth()
if (is.null(loc.dt))
......@@ -419,7 +448,9 @@ shinyServer(function(input, output, session) {
# return dt with an added column with unique track object label
dataMod <- reactive({
cat(file = stderr(), 'dataMod\n')
if (DEB)
cat(file = stdout(), 'dataMod\n')
loc.dt = dataInBoth()
if (is.null(loc.dt))
......@@ -437,7 +468,8 @@ shinyServer(function(input, output, session) {
# remove trajectories based on uploaded csv
if (input$chBtrajRem) {
cat(file = stderr(), 'dataMod: trajRem not NULL\n')
if (DEB)
cat(file = stdout(), 'dataMod: trajRem not NULL\n')
loc.dt.rem = dataLoadTrajRem()
loc.dt = loc.dt[!(trackObjectsLabelUni %in% loc.dt.rem[[1]])]
......@@ -449,7 +481,9 @@ shinyServer(function(input, output, session) {
# return all unique track object labels (created in dataMod)
# This will be used to display in UI for trajectory highlighting
getDataTrackObjLabUni <- reactive({
cat(file = stderr(), 'getDataTrackObjLabUni\n')
if (DEB)
cat(file = stdout(), 'getDataTrackObjLabUni\n')
loc.dt = dataMod()
if (is.null(loc.dt))
......@@ -463,7 +497,9 @@ shinyServer(function(input, output, session) {
# This will be used to display in UI for box-plot
# These timepoints are from the original dt and aren't affected by trimming of x-axis
getDataTpts <- reactive({
cat(file = stderr(), 'getDataTpts\n')
if (DEB)
cat(file = stdout(), 'getDataTpts\n')
loc.dt = dataMod()
if (is.null(loc.dt))
......@@ -488,7 +524,8 @@ shinyServer(function(input, output, session) {
# 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')
if (DEB)
cat(file = stdout(), 'data4trajPlot\n')
loc.dt = dataMod()
if (is.null(loc.dt))
......@@ -530,7 +567,8 @@ shinyServer(function(input, output, session) {
loc.s.pos.x = names(loc.dt)[grep('(L|l)ocation.*X|(P|p)os.x|(P|p)osx', names(loc.dt))[1]]
loc.s.pos.y = names(loc.dt)[grep('(L|l)ocation.*Y|(P|p)os.y|(P|p)osy', names(loc.dt))[1]]
cat('Position columns: ', loc.s.pos.x, loc.s.pos.y, '\n')
if (DEB)
cat('Position columns: ', loc.s.pos.x, loc.s.pos.y, '\n')
if (!is.na(loc.s.pos.x) & !is.na(loc.s.pos.y))
locPos = TRUE
......@@ -608,15 +646,17 @@ shinyServer(function(input, output, session) {
# or the frame number metadata can be missing, as is the case for tCourseSelected files that already have realtime column.
# Therefore, we cannot rely on that info to get time frequency; user must provide this number!
setkey(loc.out, group, id, realtime)
setkeyv(loc.out, c(COLGR, COLID, COLRT))
if (input$chBtrajInter) {
# here we fill missing data with NA's
loc.out = loc.out[setkeyv(loc.out[, .(seq(min(get(COLRT), na.rm = T), max(get(COLRT), na.rm = T), input$inSelTimeFreq)), by = c(COLGR, COLID)], c(COLGR, COLID, 'V1'))]
# x-check: print all rows with NA's
print('Rows with NAs:')
print(loc.out[rowSums(is.na(loc.out)) > 0, ])
if (DEB) {
cat(file = stdout(), 'Rows with NAs:\n')
print(loc.out[rowSums(is.na(loc.out)) > 0, ])
}
# NA's may be already present in the dataset'.
# Interpolate (linear) them with na.interpolate as well
......@@ -686,7 +726,8 @@ shinyServer(function(input, output, session) {
# cells as columns
# time points as rows
data4clust <- reactive({
cat(file = stderr(), 'data4clust\n')
if (DEB)
cat(file = stdout(), 'data4clust\n')
loc.dt = data4trajPlotNoOut()
if (is.null(loc.dt))
......@@ -716,15 +757,19 @@ shinyServer(function(input, output, session) {
# prepare data with stimulation pattern
# this dataset is displayed underneath of trajectory plot (modules/trajPlot.R) as geom_segment
data4stimPlot <- reactive({
cat(file = stderr(), 'data4stimPlot\n')
if (DEB)
cat(file = stdout(), 'data4stimPlot\n')
if (input$chBstim) {
cat(file = stderr(), 'data4stimPlot: stim not NULL\n')
if (DEB)
cat(file = stdout(), 'data4stimPlot: stim not NULL\n')
loc.dt.stim = dataLoadStim()
return(loc.dt.stim)
} else {
cat(file = stderr(), 'data4stimPlot: stim is NULL\n')
if (DEB)
cat(file = stdout(), 'data4stimPlot: stim is NULL\n')
return(NULL)
}
})
......@@ -743,7 +788,8 @@ shinyServer(function(input, output, session) {
# UI for selecting trajectories
# The output data table of data4trajPlot is modified based on inSelHighlight field
output$varSelHighlight = renderUI({
cat(file = stderr(), 'UI varSelHighlight\n')
if (DEB)
cat(file = stdout(), 'UI varSelHighlight\n')
locBut = input$chBhighlightTraj
if (!locBut)
......
ui.R
View file @ 17a85ab0
......@@ -53,7 +53,7 @@ shinyUI(fluidPage(
uiOutput('uiButLoadStim'),
tags$hr(),
checkboxInput('chBtrajInter', 'Interpolate NAs and missing data?', value = T),
checkboxInput('chBtrajInter', 'Interpolate NAs and missing data?', value = F),
helpPopup(
title = 'Interpolation of NAs and missing data',
content = help.text[3],
......@@ -62,7 +62,7 @@ shinyUI(fluidPage(
),
uiOutput('varSelTimeFreq'),
checkboxInput('chBtrackUni', 'Create unique TrackLabel', T),
checkboxInput('chBtrackUni', 'Create unique TrackLabel', F),
helpPopup(
title = 'Create unique cell ID',
content = help.text[2],
......@@ -73,7 +73,7 @@ shinyUI(fluidPage(
uiOutput('varSelTrackLabel'),
tags$hr(),
checkboxInput('chBgroup', 'Dataset contains grouping column (e.g. treatment, condition)', TRUE),
checkboxInput('chBgroup', 'Dataset contains grouping column (e.g. treatment, condition)', F),
uiOutput('varSelGroup'),
uiOutput('varSelTime'),
uiOutput('varSelMeas1'),
......
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