| Title: | Interactive Differential Expresion Analysis Browser |
|---|---|
| Description: | Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps. |
| Authors: | Alper Kucukural <[email protected]>, Onur Yukselen <[email protected]>, Manuel Garber <[email protected]> |
| Maintainer: | Alper Kucukural <[email protected]> |
| License: | GPL-3 + file LICENSE |
| Version: | 1.35.0 |
| Built: | 2024-10-30 05:43:59 UTC |
| Source: | https://github.com/bioc/debrowser |
Creates an action button whose value is initially zero, and increments by one each time it is pressed.
actionButtonDE( inputId, label, styleclass = "", size = "", block = FALSE, icon = NULL, css.class = "", ... )actionButtonDE( inputId, label, styleclass = "", size = "", block = FALSE, icon = NULL, css.class = "", ... )
inputId |
Specifies the input slot that will be used to access the value. |
label |
The contents of the button–usually a text label, but you could also use any other HTML, like an image. |
styleclass |
The Bootstrap styling class of the button–options are primary, info, success, warning, danger, inverse, link or blank |
size |
The size of the button–options are large, small, mini |
block |
Whehter the button should fill the block |
icon |
Display an icon for the button |
css.class |
Any additional CSS class one wishes to add to the action button |
... |
Other argument to feed into shiny::actionButton |
actionButtonDE("goDE", "Go to DE Analysis")actionButtonDE("goDE", "Go to DE Analysis")
add aditional data columns to de results
addDataCols(data = NULL, de_res = NULL, cols = NULL, conds = NULL)addDataCols(data = NULL, de_res = NULL, cols = NULL, conds = NULL)
data |
loaded dataset |
de_res |
de results |
cols |
columns |
conds |
inputconds |
data
x <- addDataCols()x <- addDataCols()
Adds an id to the data frame being used.
addID(data = NULL)addID(data = NULL)
data |
loaded dataset |
data
x <- addID()x <- addID()
Prepares all2all scatter plots for given datasets.
all2all(data, cex = 2)all2all(data, cex = 2)
data |
data that have the sample names in the header. |
cex |
text size |
all2all scatter plots
plot<-all2all(mtcars)plot<-all2all(mtcars)
Generates the controls in the left menu for an all2all plot
all2allControlsUI(id)all2allControlsUI(id)
id |
namespace id |
returns the controls for left menu
all2allControlsUI
x <- all2allControlsUI("bar")x <- all2allControlsUI("bar")
Applies filters based on user selected parameters to be displayed within the DEBrowser.
applyFilters(filt_data = NULL, cols = NULL, conds = NULL, input = NULL)applyFilters(filt_data = NULL, cols = NULL, conds = NULL, input = NULL)
filt_data |
loaded dataset |
cols |
selected samples |
conds |
seleced conditions |
input |
input parameters |
data
x <- applyFilters()x <- applyFilters()
Apply filters based on foldChange cutoff and padj value. This function adds a "Legend" column with "Up", "Down" or "NS" values for visualization.
applyFiltersNew(data = NULL, input = NULL)applyFiltersNew(data = NULL, input = NULL)
data |
loaded dataset |
input |
input parameters |
data
x <- applyFiltersNew()x <- applyFiltersNew()
Gathers the merged comparison data to be used within the DEBrowser.
applyFiltersToMergedComparison(dc = NULL, nc = NULL, input = NULL)applyFiltersToMergedComparison(dc = NULL, nc = NULL, input = NULL)
dc |
all data |
nc |
the number of comparisons |
input |
input params |
data
x <- applyFiltersToMergedComparison()x <- applyFiltersToMergedComparison()
Generates the controls in the left menu for a bar main plot
barMainPlotControlsUI(id)barMainPlotControlsUI(id)
id |
namespace id |
returns the controls for left menu
barMainPlotControlsUI
x <- barMainPlotControlsUI("bar")x <- barMainPlotControlsUI("bar")
batchEffectUI Creates a panel to coorect batch effect
batchEffectUI(id)batchEffectUI(id)
id |
namespace id |
panel
x <- batchEffectUI("batcheffect")x <- batchEffectUI("batcheffect")
select batch effect method
batchMethod(id)batchMethod(id)
id |
namespace id |
radio control
batchMethod
x <- batchMethod("batch")x <- batchMethod("batch")
Generates the controls in the left menu for a Box main plot
BoxMainPlotControlsUI(id)BoxMainPlotControlsUI(id)
id |
namespace id |
returns the controls for left menu
BoxMainPlotControlsUI
x <- BoxMainPlotControlsUI("box")x <- BoxMainPlotControlsUI("box")
change order of K-means clusters
changeClusterOrder(order = NULL, cld = NULL)changeClusterOrder(order = NULL, cld = NULL)
order |
order |
cld |
data |
heatmap plot area
changeClusterOrder
x <- changeClusterOrder()x <- changeClusterOrder()
Returns if there is a problem in the count data.
checkCountData(input = NULL)checkCountData(input = NULL)
input |
inputs |
error if there is a problem about the loaded data
checkCountData
x <- checkCountData()x <- checkCountData()
Returns if there is a problem in the count data.
checkMetaData(input = NULL, counttable = NULL)checkMetaData(input = NULL, counttable = NULL)
input |
input |
counttable |
counttable |
error if there is a problem about the loaded data
checkMetaData
x <- checkMetaData()x <- checkMetaData()
Gathers the Cluster analysis data to be used within the GO Term plots.
clusterData(dat = NULL)clusterData(dat = NULL)
dat |
the data to cluster |
clustered data
clusterData
mycluster <- clusterData()mycluster <- clusterData()
get cluster function parameter control
clustFunParamsUI()clustFunParamsUI()
cluster params
clustFunParamsUI
x <- clustFunParamsUI()x <- clustFunParamsUI()
Compares the clustered data to be displayed within the GO Term plots.
compareClust( dat = NULL, ont = "CC", org = "org.Hs.eg.db", fun = "enrichGO", title = "Ontology Distribution Comparison", pvalueCutoff = 0.01 )compareClust( dat = NULL, ont = "CC", org = "org.Hs.eg.db", fun = "enrichGO", title = "Ontology Distribution Comparison", pvalueCutoff = 0.01 )
dat |
data to compare clusters |
ont |
the ontology to use |
org |
the organism used |
fun |
fun |
title |
title of the comparison |
pvalueCutoff |
pvalueCutoff |
compared cluster
compareClust
x <- compareClust()x <- compareClust()
condSelectUI Creates a panel to select samples for each condition
condSelectUI()condSelectUI()
panel
x <- condSelectUI()x <- condSelectUI()
Batch effect correction
correctCombat(input = NULL, idata = NULL, metadata = NULL, method = NULL)correctCombat(input = NULL, idata = NULL, metadata = NULL, method = NULL)
input |
input values |
idata |
data |
metadata |
metadata |
method |
method: either Combat or CombatSeq |
data
x<-correctCombat ()x<-correctCombat ()
Batch effect correction
correctHarman(input = NULL, idata = NULL, metadata = NULL)correctHarman(input = NULL, idata = NULL, metadata = NULL)
input |
input values |
idata |
data |
metadata |
metadata |
data
x<-correctHarman ()x<-correctHarman ()
get Custom Color controls
customColorsUI(id)customColorsUI(id)
id |
namespace ID |
color range
getColRng
x <- customColorsUI("heatmap")x <- customColorsUI("heatmap")
Gathers the cut off selection for DE analysis
cutOffSelectionUI(id)cutOffSelectionUI(id)
id |
namespace id |
returns the left menu according to the selected tab;
cutOffSelectionUI
x <- cutOffSelectionUI("cutoff")x <- cutOffSelectionUI("cutoff")
dataLCFUI Creates a panel to filter low count genes and regions
dataLCFUI(id)dataLCFUI(id)
id |
namespace id |
panel
x <- dataLCFUI("lcf")x <- dataLCFUI("lcf")
Creates a panel to upload the data
dataLoadUI(id)dataLoadUI(id)
id |
namespace id |
panel
x <- dataLoadUI("load")x <- dataLoadUI("load")
Module for a bar plot that can be used in data prep, main plots low count removal modules or any desired module
debrowserall2all(input, output, session, data = NULL, cex = 2)debrowserall2all(input, output, session, data = NULL, cex = 2)
input |
input variables |
output |
output objects |
session |
session |
data |
a matrix that includes expression values |
cex |
the size of the dots |
all2all plot
x <- debrowserall2all()x <- debrowserall2all()
Module for a bar plot that can be used in data prep, main plots low count removal modules or any desired module
debrowserbarmainplot( input, output, session, data = NULL, cols = NULL, conds = NULL, cond_names = NULL, key = NULL )debrowserbarmainplot( input, output, session, data = NULL, cols = NULL, conds = NULL, cond_names = NULL, key = NULL )
input |
input variables |
output |
output objects |
session |
session |
data |
a matrix that includes expression values |
cols |
columns |
conds |
conditions |
cond_names |
condition names |
key |
the gene or region name |
density plot
x <- debrowserbarmainplot()x <- debrowserbarmainplot()
Module to correct batch effect
debrowserbatcheffect(input, output, session, ldata = NULL)debrowserbatcheffect(input, output, session, ldata = NULL)
input |
input variables |
output |
output objects |
session |
session |
ldata |
loaded data |
main plot
panel
x <- debrowserbatcheffect()x <- debrowserbatcheffect()
Module for a box plot that can be used in DEanalysis main part and used heatmaps
debrowserboxmainplot( input = NULL, output = NULL, session = NULL, data = NULL, cols = NULL, conds = NULL, cond_names = NULL, key = NULL )debrowserboxmainplot( input = NULL, output = NULL, session = NULL, data = NULL, cols = NULL, conds = NULL, cond_names = NULL, key = NULL )
input |
input variables |
output |
output objects |
session |
session |
data |
a matrix that includes expression values |
cols |
columns |
conds |
conditions |
cond_names |
condition names |
key |
the gene or region name |
density plot
x <- debrowserboxmainplot()x <- debrowserboxmainplot()
Condition selection This is not a module. Module construction didn't used here, just use it as functions not in a module.
debrowsercondselect( input = NULL, output = NULL, session = NULL, data = NULL, metadata = NULL )debrowsercondselect( input = NULL, output = NULL, session = NULL, data = NULL, metadata = NULL )
input |
input variables |
output |
output objects |
session |
session |
data |
count data |
metadata |
metadata |
main plot
panel
x <- debrowsercondselect()x <- debrowsercondselect()
Module to load count data and metadata
debrowserdataload( input = NULL, output = NULL, session = NULL, nextpagebutton = NULL )debrowserdataload( input = NULL, output = NULL, session = NULL, nextpagebutton = NULL )
input |
input variables |
output |
output objects |
session |
session |
nextpagebutton |
the name of the next page button after loading the data |
main plot
panel
x <- debrowserdataload()x <- debrowserdataload()
Module to perform and visualize DE results.
debrowserdeanalysis( input = NULL, output = NULL, session = NULL, data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )debrowserdeanalysis( input = NULL, output = NULL, session = NULL, data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )
input |
input variables |
output |
output objects |
session |
session |
data |
a matrix that includes expression values |
metadata |
metadata |
columns |
columns |
conds |
conditions |
params |
de parameters |
DE panel
x <- debrowserdeanalysis()x <- debrowserdeanalysis()
Module for a density plot that can be used in data prep and low count removal modules
debrowserdensityplot(input = NULL, output = NULL, session = NULL, data = NULL)debrowserdensityplot(input = NULL, output = NULL, session = NULL, data = NULL)
input |
input variables |
output |
output objects |
session |
session |
data |
a matrix that includes expression values |
density plot
x <- debrowserdensityplot()x <- debrowserdensityplot()
Heatmap module to create interactive heatmaps and get selected list from a heatmap
debrowserheatmap(input, output, session, expdata = NULL)debrowserheatmap(input, output, session, expdata = NULL)
input |
input variables |
output |
output objects |
session |
session |
expdata |
a matrix that includes expression values |
heatmapply plot
x <- debrowserheatmap()x <- debrowserheatmap()
Module for a histogram that can be used in data prep and low count removal modules
debrowserhistogram(input = NULL, output = NULL, session = NULL, data = NULL)debrowserhistogram(input = NULL, output = NULL, session = NULL, data = NULL)
input |
input variables |
output |
output objects |
session |
session |
data |
a matrix that includes expression values |
histogram
x <- debrowserhistogram()x <- debrowserhistogram()
Module for an IQR plot that can be used in data prep and low count removal modules
debrowserIQRplot(input = NULL, output = NULL, session = NULL, data = NULL)debrowserIQRplot(input = NULL, output = NULL, session = NULL, data = NULL)
input |
input variables |
output |
output objects |
session |
session |
data |
a matrix that includes expression values |
IQR
x <- debrowserIQRplot()x <- debrowserIQRplot()
Module to filter low count genes/regions
debrowserlowcountfilter( input = NULL, output = NULL, session = NULL, ldata = NULL )debrowserlowcountfilter( input = NULL, output = NULL, session = NULL, ldata = NULL )
input |
input variables |
output |
output objects |
session |
session |
ldata |
loaded data |
main plot
panel
x <- debrowserlowcountfilter()x <- debrowserlowcountfilter()
Module for a scatter, volcano and ma plots that are going to be used as a mainplot in debrowser
debrowsermainplot( input = NULL, output = NULL, session = NULL, data = NULL, cond_names = NULL )debrowsermainplot( input = NULL, output = NULL, session = NULL, data = NULL, cond_names = NULL )
input |
input variables |
output |
output objects |
session |
session |
data |
a matrix that includes expression values |
cond_names |
condition names |
main plot
panel
x <- debrowsermainplot()x <- debrowsermainplot()
Module for a pca plot with its loadings as a mainplot in debrowser
debrowserpcaplot( input = NULL, output = NULL, session = NULL, pcadata = NULL, metadata = NULL )debrowserpcaplot( input = NULL, output = NULL, session = NULL, pcadata = NULL, metadata = NULL )
input |
input variables |
output |
output objects |
session |
session |
pcadata |
a matrix that includes expression values |
metadata |
metadata to color the plots |
main plot
panel
x <- debrowserpcaplot()x <- debrowserpcaplot()
get distance metric parameters
dendControlsUI(id, dendtype = "Row")dendControlsUI(id, dendtype = "Row")
id |
module ID |
dendtype |
Row or Col |
controls
dendControlsUI
x <- dendControlsUI("heatmap")x <- dendControlsUI("heatmap")
Generates the controls in the left menu for a densityPlot
densityPlotControlsUI(id)densityPlotControlsUI(id)
id |
namespace id |
returns the left menu
densityPlotControlsUI
x <- densityPlotControlsUI("density")x <- densityPlotControlsUI("density")
Sets up shinyServer to be able to run DEBrowser interactively.
deServer(input, output, session)deServer(input, output, session)
input |
input params from UI |
output |
output params to UI |
session |
session variable |
the panel for main plots;
deServer
deServerdeServer
Creates a shinyUI to be able to run DEBrowser interactively.
deUI()deUI()
the panel for main plots;
deUI
x<-deUI()x<-deUI()
get distance metric parameters
distFunParamsUI()distFunParamsUI()
funParams
distFunParamsUI
x <- distFunParamsUI()x <- distFunParamsUI()
draw KEGG patwhay with expression values
drawKEGG(input = NULL, dat = NULL, pid = NULL)drawKEGG(input = NULL, dat = NULL, pid = NULL)
input |
input |
dat |
expression matrix |
pid |
pathway id |
enriched DO
drawKEGG
x <- drawKEGG()x <- drawKEGG()
Creates a more detailed plot using the PCA results from the selected dataset.
drawPCAExplained(explainedData = NULL)drawPCAExplained(explainedData = NULL)
explainedData |
selected data |
explained plot
x <- drawPCAExplained()x <- drawPCAExplained()
Returns fileTypes that are going to be used in creating fileUpload UI
fileTypes()fileTypes()
file types
fileTypes
x <- fileTypes()x <- fileTypes()
File upload module
fileUploadBox(id = NULL, inputId = NULL, label = NULL)fileUploadBox(id = NULL, inputId = NULL, label = NULL)
id |
namespace id |
inputId |
input file ID |
label |
label |
radio control
fileUploadBox
x <- fileUploadBox("meta", "metadata", "Metadata")x <- fileUploadBox("meta", "metadata", "Metadata")
This generates a test data that is suitable to main plots in debrowser
generateTestData(dat = NULL)generateTestData(dat = NULL)
dat |
DESeq results will be generated for loaded data |
testData
x <- generateTestData()x <- generateTestData()
Return the two set of conditions given the selection of meta select box
get_conditions_given_selection(metadata = NULL, selection = NULL)get_conditions_given_selection(metadata = NULL, selection = NULL)
metadata |
meta data table |
selection |
selection |
meta select box
x<-get_conditions_given_selection()x<-get_conditions_given_selection()
Generates and displays the message to be shown after loading data within the DEBrowser.
getAfterLoadMsg()getAfterLoadMsg()
return After Load Msg
getAfterLoadMsg
x <- getAfterLoadMsg()x <- getAfterLoadMsg()
all2all plots UI.
getAll2AllPlotUI(id)getAll2AllPlotUI(id)
id |
namespace id |
the panel for all2all plots;
getAll2AllPlotUI
x <- getAll2AllPlotUI("bar")x <- getAll2AllPlotUI("bar")
Makes Density plots
getBarMainPlot( data = NULL, cols = NULL, conds = NULL, cond_names = NULL, key = NULL, title = "", input = NULL )getBarMainPlot( data = NULL, cols = NULL, conds = NULL, cond_names = NULL, key = NULL, title = "", input = NULL )
data |
count or normalized data |
cols |
cols |
conds |
conds |
cond_names |
condition names |
key |
key |
title |
title |
input |
input |
getBarMainPlot()getBarMainPlot()
main bar plots UI.
getBarMainPlotUI(id)getBarMainPlotUI(id)
id |
namespace id |
the panel for Density plots;
getBarMainPlotUI
x <- getBarMainPlotUI("bar")x <- getBarMainPlotUI("bar")
Makes Density plots
getBoxMainPlot( data = NULL, cols = NULL, conds = NULL, cond_names = NULL, key = NULL, title = "", input = NULL )getBoxMainPlot( data = NULL, cols = NULL, conds = NULL, cond_names = NULL, key = NULL, title = "", input = NULL )
data |
count or normalized data |
cols |
cols |
conds |
conds |
cond_names |
condition names |
key |
key |
title |
title |
input |
input |
getBoxMainPlot()getBoxMainPlot()
main Box plots UI.
getBoxMainPlotUI(id)getBoxMainPlotUI(id)
id |
namespace id |
the panel for Density plots;
getBoxMainPlotUI
x <- getBoxMainPlotUI("box")x <- getBoxMainPlotUI("box")
getBSTableUI prepares a Modal to put a table
getBSTableUI( name = NULL, label = NULL, trigger = NULL, size = "large", modal = NULL )getBSTableUI( name = NULL, label = NULL, trigger = NULL, size = "large", modal = NULL )
name |
name |
label |
label |
trigger |
trigger button for the modal |
size |
size of the modal |
modal |
modal yes/no |
the modal
x<- getBSTableUI()x<- getBSTableUI()
get colors for the domains
getColors(domains = NULL)getColors(domains = NULL)
domains |
domains to be colored |
colors
x<-getColors()x<-getColors()
Generates the fill and shape selection boxes for PCA plots. metadata file has to be loaded in this case
getColorShapeSelection(metadata = NULL, input = NULL, session = NULL)getColorShapeSelection(metadata = NULL, input = NULL, session = NULL)
metadata |
metadata table |
input |
input |
session |
session |
Color and shape selection boxes
x <- getColorShapeSelection()x <- getColorShapeSelection()
Gathers the user selected comparison set to be used within the DEBrowser.
getCompSelection(name = NULL, count = NULL)getCompSelection(name = NULL, count = NULL)
name |
the name of the selectInput |
count |
comparison count |
getCompSelection
x <- getCompSelection(name="comp", count = 2)x <- getCompSelection(name="comp", count = 2)
Selects user input conditions to run in DESeq.
getConditionSelector(num = NULL, choices = NULL, selected = NULL)getConditionSelector(num = NULL, choices = NULL, selected = NULL)
num |
panel that is going to be shown |
choices |
sample list |
selected |
selected smaple list |
x <- getConditionSelector()x <- getConditionSelector()
Selects user input conditions to run in DESeq from metadata
getConditionSelectorFromMeta( metadata = NULL, input = NULL, index = 1, num = 0, choices = NULL, selected = NULL )getConditionSelectorFromMeta( metadata = NULL, input = NULL, index = 1, num = 0, choices = NULL, selected = NULL )
metadata |
meta data table |
input |
input |
index |
index |
num |
num |
choices |
choices |
selected |
selected |
x <- getConditionSelectorFromMeta()x <- getConditionSelectorFromMeta()
Generates and displays the current conditions and their samples within the DEBrowser.
getCondMsg(dc = NULL, input = NULL, cols = NULL, conds = NULL)getCondMsg(dc = NULL, input = NULL, cols = NULL, conds = NULL)
dc |
columns |
input |
selected comparison |
cols |
columns |
conds |
selected conditions |
return conditions
getCondMsg
x <- getCondMsg()x <- getCondMsg()
get the covariate detail box after DE method selected
getCovariateDetails(num = NULL, input = NULL, metadata = NULL)getCovariateDetails(num = NULL, input = NULL, metadata = NULL)
num |
panel that is going to be shown |
input |
user input |
metadata |
metadata |
x <- getCovariateDetails()x <- getCovariateDetails()
Gathers the cut off selection for DE analysis
getCutOffSelection(nc = 1)getCutOffSelection(nc = 1)
nc |
total number of comparisons |
returns the left menu according to the selected tab;
getCutOffSelection
x <- getCutOffSelection()x <- getCutOffSelection()
getDataAssesmentText DataAssesment text
getDataAssesmentText()getDataAssesmentText()
help text for data assesment
x<- getDataAssesmentText()x<- getDataAssesmentText()
getDataForTables get data to fill up tables tab
getDataForTables( input = NULL, init_data = NULL, filt_data = NULL, selected = NULL, getMostVaried = NULL, mergedComp = NULL, explainedData = NULL )getDataForTables( input = NULL, init_data = NULL, filt_data = NULL, selected = NULL, getMostVaried = NULL, mergedComp = NULL, explainedData = NULL )
input |
input parameters |
init_data |
initial dataset |
filt_data |
filt_data |
selected |
selected genes |
getMostVaried |
most varied genes |
mergedComp |
merged comparison set |
explainedData |
pca gene set |
data
x <- getDataForTables()x <- getDataForTables()
getDataPreparationText DataPreparation text
getDataPreparationText()getDataPreparationText()
help text for data preparation
x<- getDataPreparationText()x<- getDataPreparationText()
getDEAnalysisText DEAnalysis text
getDEAnalysisText()getDEAnalysisText()
help text for DE Analysis
x<- getDEAnalysisText()x<- getDEAnalysisText()
Makes Density plots
getDensityPlot(data = NULL, input = NULL, title = "")getDensityPlot(data = NULL, input = NULL, title = "")
data |
count or normalized data |
input |
input |
title |
title |
getDensityPlot()getDensityPlot()
Density plot UI.
getDensityPlotUI(id)getDensityPlotUI(id)
id |
namespace id |
the panel for Density plots;
getDensityPlotUI
x <- getDensityPlotUI("density")x <- getDensityPlotUI("density")
getDEResultsUI Creates a panel to visualize DE results
getDEResultsUI(id)getDEResultsUI(id)
id |
namespace id |
panel
x <- getDEResultsUI("batcheffect")x <- getDEResultsUI("batcheffect")
Get domains for the main plots.
getDomains(filt_data = NULL)getDomains(filt_data = NULL)
filt_data |
data to get the domains |
domains
x<-getDomains()x<-getDomains()
getDown get down regulated data
getDown(filt_data = NULL)getDown(filt_data = NULL)
filt_data |
filt_data |
data
x <- getDown()x <- getDown()
download section button and dataset selection box in the menu for user to download selected data.
getDownloadSection(choices = NULL)getDownloadSection(choices = NULL)
choices |
main vs. QC section |
the panel for download section in the menu;
getDownloadSection
x<- getDownloadSection()x<- getDownloadSection()
Gathers the Enriched DO Term analysis data to be used within the GO Term plots.
getEnrichDO(genelist = NULL, pvalueCutoff = 0.01)getEnrichDO(genelist = NULL, pvalueCutoff = 0.01)
genelist |
gene list |
pvalueCutoff |
the p value cutoff |
enriched DO
getEnrichDO
x <- getEnrichDO()x <- getEnrichDO()
Gathers the Enriched GO Term analysis data to be used within the GO Term plots.
getEnrichGO( genelist = NULL, pvalueCutoff = 0.01, org = "org.Hs.eg.db", ont = "CC" )getEnrichGO( genelist = NULL, pvalueCutoff = 0.01, org = "org.Hs.eg.db", ont = "CC" )
genelist |
gene list |
pvalueCutoff |
p value cutoff |
org |
the organism used |
ont |
the ontology used |
Enriched GO
getEnrichGO
x <- getEnrichGO()x <- getEnrichGO()
Gathers the Enriched KEGG analysis data to be used within the GO Term plots.
getEnrichKEGG(genelist = NULL, pvalueCutoff = 0.01, org = "org.Hs.eg.db")getEnrichKEGG(genelist = NULL, pvalueCutoff = 0.01, org = "org.Hs.eg.db")
genelist |
gene list |
pvalueCutoff |
the p value cutoff |
org |
the organism used |
Enriched KEGG
getEnrichKEGG
x <- getEnrichKEGG()x <- getEnrichKEGG()
Gathers the gene list to use for GOTerm analysis.
getEntrezIds(genes = NULL, org = "org.Hs.eg.db")getEntrezIds(genes = NULL, org = "org.Hs.eg.db")
genes |
gene list with fold changes |
org |
orgranism for gene symbol entrez ID conversion |
ENTREZ ID list
GOTerm
getEntrezIds
symobol to ENTREZ ID conversion
x <- getEntrezIds()x <- getEntrezIds()
Gathers the entrezIds of the genes in given list and their data
getEntrezTable(genes = NULL, dat = NULL, org = "org.Hs.eg.db")getEntrezTable(genes = NULL, dat = NULL, org = "org.Hs.eg.db")
genes |
gene list |
dat |
data matrix |
org |
orgranism for gene symbol entrez ID conversion |
table with the entrez IDs in the rownames
GOTerm
getEntrezTable
symobol to ENTREZ ID conversion
x <- getEntrezTable()x <- getEntrezTable()
Gathers the gene list to use for GOTerm analysis.
getGeneList( genes = NULL, org = "org.Hs.eg.db", fromType = "SYMBOL", toType = c("ENTREZID") )getGeneList( genes = NULL, org = "org.Hs.eg.db", fromType = "SYMBOL", toType = c("ENTREZID") )
genes |
gene list |
org |
orgranism for gene symbol entrez ID conversion |
fromType |
from Type |
toType |
to Type |
ENTREZ ID list
GOTerm
getGeneList
symobol to ENTREZ ID conversion
x <- getGeneList(c('OCLN', 'ABCC2'))x <- getGeneList(c('OCLN', 'ABCC2'))
Gathers the specified gene set list to be used within the DEBrowser.
getGeneSetData(data = NULL, geneset = NULL)getGeneSetData(data = NULL, geneset = NULL)
data |
loaded dataset |
geneset |
given gene set |
data
x <- getGeneSetData()x <- getGeneSetData()
Generates the GO Left menu to be displayed within the DEBrowser.
getGOLeftMenu()getGOLeftMenu()
returns the left menu according to the selected tab;
getGOLeftMenu
x <- getGOLeftMenu()x <- getGOLeftMenu()
Creates go term analysis panel within the shiny display.
getGoPanel()getGoPanel()
the panel for go term analysis;
getGoPanel
x <- getGoPanel()x <- getGoPanel()
Go term analysis panel. Generates appropriate GO plot based on user selection.
getGOPlots(dataset = NULL, GSEARes = NULL, input = NULL)getGOPlots(dataset = NULL, GSEARes = NULL, input = NULL)
dataset |
the dataset used |
GSEARes |
GSEA results |
input |
input params |
the panel for go plots;
getGOPlots
x<- getGOPlots()x<- getGOPlots()
getGroupSelector Return the groups
getGroupSelector(metadata = NULL, input = NULL, index = 1, num = 0)getGroupSelector(metadata = NULL, input = NULL, index = 1, num = 0)
metadata |
meta data table |
input |
input params |
index |
index |
num |
num |
meta select box
x<-getGroupSelector()x<-getGroupSelector()
Gathers the Enriched KEGG analysis data to be used within the GO Term plots.
getGSEA( dataset = NULL, pvalueCutoff = 0.01, org = "org.Hs.eg.db", sortfield = "log2FoldChange" )getGSEA( dataset = NULL, pvalueCutoff = 0.01, org = "org.Hs.eg.db", sortfield = "log2FoldChange" )
dataset |
dataset |
pvalueCutoff |
the p value cutoff |
org |
the organism used |
sortfield |
sort field for GSEA |
GSEA
getGSEA
x <- getGSEA()x <- getGSEA()
Generates the left menu to be used for heatmap plots
getHeatmapUI(id)getHeatmapUI(id)
id |
module ID |
heatmap plot area
getHeatmapUI
x <- getHeatmapUI("heatmap")x <- getHeatmapUI("heatmap")
getHelpButton prepares a helpbutton for to go to a specific site in the documentation
getHelpButton(name = NULL, link = NULL)getHelpButton(name = NULL, link = NULL)
name |
name that are going to come after info |
link |
link of the help |
the info button
x<- getHelpButton()x<- getHelpButton()
hide legend
getHideLegendOnOff(id = "pca")getHideLegendOnOff(id = "pca")
id |
namespace id |
x <- getHideLegendOnOff("pca")x <- getHideLegendOnOff("pca")
Histogram plots UI.
getHistogramUI(id)getHistogramUI(id)
id |
namespace id |
the panel for PCA plots;
getHistogramUI
x <- getHistogramUI("histogram")x <- getHistogramUI("histogram")
getIntroText Intro text
getIntroText()getIntroText()
the JS for tab updates
x<- getIntroText()x<- getIntroText()
Makes IQR boxplot plot
getIQRPlot(data = NULL, input = NULL, title = "")getIQRPlot(data = NULL, input = NULL, title = "")
data |
count or normalized data |
input |
input |
title |
title |
getIQRPlot()getIQRPlot()
IQR plot UI.
getIQRPlotUI(id)getIQRPlotUI(id)
id |
namespace id |
the panel for IQR plots;
getIQRPlotUI
x <- getIQRPlotUI("IQR")x <- getIQRPlotUI("IQR")
heatmap JS code for selection functionality
getJSLine()getJSLine()
JS Code
x <- getJSLine()x <- getJSLine()
getKEGGModal prepares a modal for KEGG plots
getKEGGModal prepares a helpbutton for to go to a specific site in the documentation
getKEGGModal() getKEGGModal()getKEGGModal() getKEGGModal()
the info button
the info button
x<- getKEGGModal() x<- getKEGGModal()x<- getKEGGModal() x<- getKEGGModal()
Generates the left menu for for plots within the DEBrowser.
getLeftMenu(input = NULL)getLeftMenu(input = NULL)
input |
input values |
returns the left menu according to the selected tab;
getLeftMenu
x <- getLeftMenu()x <- getLeftMenu()
Generates colors according to the data
getLegendColors(Legend = c("up", "down", "NS"))getLegendColors(Legend = c("up", "down", "NS"))
Legend |
unique Legends |
mainPlotControls
getLegendColors
x <- getLegendColors(c("up", "down", "GS", "NS"))x <- getLegendColors(c("up", "down", "GS", "NS"))
Radio buttons for the types in the legend
getLegendRadio(id)getLegendRadio(id)
id |
namespace id |
radio control
getLegendRadio
x <- getLegendRadio("deprog")x <- getLegendRadio("deprog")
select legend
getLegendSelect(id = "pca")getLegendSelect(id = "pca")
id |
namespace id |
getLegendSelect
x <- getLegendSelect("pca")x <- getLegendSelect("pca")
Generates the order of the overlapping points
getLevelOrder(Level = c("up", "down", "NS"))getLevelOrder(Level = c("up", "down", "NS"))
Level |
factor levels shown in the legend |
order
getLevelOrder
x <- getLevelOrder(c("up", "down", "GS", "NS"))x <- getLevelOrder(c("up", "down", "GS", "NS"))
Creates and displays the loading message/gif to be displayed within the DEBrowser.
getLoadingMsg(output = NULL)getLoadingMsg(output = NULL)
output |
output message |
loading msg
getLoadingMsg
x <- getLoadingMsg()x <- getLoadingMsg()
Generates and displays the logo to be shown within DEBrowser.
getLogo()getLogo()
return logo
getLogo
x <- getLogo()x <- getLogo()
main panel for volcano, scatter and maplot. Barplot and box plots are in this page as well.
getMainPanel()getMainPanel()
the panel for main plots;
getMainPanel
x <- getMainPanel()x <- getMainPanel()
Generates the Main PLots Left menu to be displayed within the DEBrowser.
getMainPlotsLeftMenu()getMainPlotsLeftMenu()
returns the left menu according to the selected tab;
getMainPlotsLeftMenu
x <- getMainPlotsLeftMenu()x <- getMainPlotsLeftMenu()
main plot for volcano, scatter and maplot.
getMainPlotUI(id)getMainPlotUI(id)
id |
namespace id |
the panel for main plots;
getMainPlotUI
x <- getMainPlotUI("main")x <- getMainPlotUI("main")
Gathers the mean for selected condition.
getMean(data = NULL, selcols = NULL)getMean(data = NULL, selcols = NULL)
data |
dataset |
selcols |
input cols |
data
x <- getMean()x <- getMean()
Gathers the merged comparison data to be used within the DEBrowser.
getMergedComparison(dc = NULL, nc = NULL, input = NULL)getMergedComparison(dc = NULL, nc = NULL, input = NULL)
dc |
data container |
nc |
the number of comparisons |
input |
input params |
data
x <- getMergedComparison()x <- getMergedComparison()
Return the sample selection box using meta data table
getMetaSelector(metadata = NULL, input = NULL, n = 0)getMetaSelector(metadata = NULL, input = NULL, n = 0)
metadata |
meta data table |
input |
input params |
n |
the box number |
meta select box
x<-getMetaSelector()x<-getMetaSelector()
get the detail boxes after DE method selected
getMethodDetails(num = NULL, input = NULL)getMethodDetails(num = NULL, input = NULL)
num |
panel that is going to be shown |
input |
user input |
x <- getMethodDetails()x <- getMethodDetails()
Calculates the most varied genes to be used for specific plots within the DEBrowser.
getMostVariedList(datavar = NULL, cols = NULL, input = NULL)getMostVariedList(datavar = NULL, cols = NULL, input = NULL)
datavar |
loaded dataset |
cols |
selected columns |
input |
input |
data
x <- getMostVariedList()x <- getMostVariedList()
Normalizes the matrix passed to be used within various methods within DEBrowser. Requires edgeR package
getNormalizedMatrix(M = NULL, method = "TMM")getNormalizedMatrix(M = NULL, method = "TMM")
M |
numeric matrix |
method |
normalization method for edgeR. default is TMM |
normalized matrix
getNormalizedMatrix
x <- getNormalizedMatrix(mtcars)x <- getNormalizedMatrix(mtcars)
getOrganism
getOrganism(org)getOrganism(org)
org |
organism |
organism name for keg
getOrganism
x <- getOrganism()x <- getOrganism()
Get the organism Box.
getOrganismBox()getOrganismBox()
selectInput
getOrganismBox
getOrganismBox
makes the organism box
x <- getOrganismBox()x <- getOrganismBox()
getOrganismPathway
getOrganismPathway(org)getOrganismPathway(org)
org |
organism |
organism name for pathway
getOrganismPathway
x <- getOrganismPathway()x <- getOrganismPathway()
getPCAcontolUpdatesJS in the prep menu we have two PCA plots to show how batch effect correction worked. One set of PCA input controls updates two PCA plots with this JS.
getPCAcontolUpdatesJS()getPCAcontolUpdatesJS()
the JS for tab updates
x<- getTabUpdateJS()x<- getTabUpdateJS()
Creates a more detailed plot using the PCA results from the selected dataset.
getPCAexplained(datasetInput = NULL, pca_data = NULL, input = NULL)getPCAexplained(datasetInput = NULL, pca_data = NULL, input = NULL)
datasetInput |
selected data |
pca_data |
from user |
input |
input params |
explained plot
load(system.file("extdata", "demo", "demodata.Rda", package="debrowser")) input<-c() input$qcplot<-"pca" input$col_list<-colnames(demodata[,1:6]) dat <- getNormalizedMatrix(demodata[,1:6]) pca_data <- run_pca(dat) x <- getPCAexplained(dat, pca_data, input)load(system.file("extdata", "demo", "demodata.Rda", package="debrowser")) input<-c() input$qcplot<-"pca" input$col_list<-colnames(demodata[,1:6]) dat <- getNormalizedMatrix(demodata[,1:6]) pca_data <- run_pca(dat) x <- getPCAexplained(dat, pca_data, input)
PCA plots UI.
getPCAPlotUI(id)getPCAPlotUI(id)
id |
namespace id |
the panel for PCA plots;
getPCAPlotUI
x <- getPCAPlotUI("pca")x <- getPCAPlotUI("pca")
Generates the PC selection number to be used within DEBrowser.
getPCselection(id, num = 1, xy = "x")getPCselection(id, num = 1, xy = "x")
id |
namespace id |
num |
PC selection number |
xy |
x or y coordinate |
PC selection for PCA analysis
getPCselection
x <- getPCselection("pca")x <- getPCselection("pca")
returns plot area either for heatmaply or heatmap.2
getPlotArea(input = NULL, session = NULL)getPlotArea(input = NULL, session = NULL)
input |
input variables |
session |
session |
heatmapply/heatmap.2 plot area
x <- getPlotArea()x <- getPlotArea()
Generates the title of the program to be displayed within DEBrowser. If it is called in a program, the program title will be hidden
getProgramTitle(session = NULL)getProgramTitle(session = NULL)
session |
session var |
program title
getProgramTitle
title<-getProgramTitle()title<-getProgramTitle()
getQAText Some questions and answers
getQAText()getQAText()
help text for QA
x<- getQAText()x<- getQAText()
Generates the left menu to be used for QC plots within the DEBrowser.
getQCLeftMenu(input = NULL)getQCLeftMenu(input = NULL)
input |
input values |
QC left menu
getQCLeftMenu
x <- getQCLeftMenu()x <- getQCLeftMenu()
Gathers the conditional panel for QC plots
getQCPanel(input = NULL)getQCPanel(input = NULL)
input |
user input |
the panel for QC plots
getQCSection
x <- getQCPanel()x <- getQCPanel()
get sample details
getSampleDetails(output = NULL, summary = NULL, details = NULL, data = NULL)getSampleDetails(output = NULL, summary = NULL, details = NULL, data = NULL)
output |
output |
summary |
summary output name |
details |
details ouput name |
data |
data |
panel
x <- getSampleDetails()x <- getSampleDetails()
Prepares initial samples to fill condition boxes. it reads the sample names from the data and splits into two.
getSampleNames(cnames = NULL, part = 1)getSampleNames(cnames = NULL, part = 1)
cnames |
sample names in the header of a dataset |
part |
c(1,2). 1=first half and 2= second half |
sample names.
x<-getSampleNames()x<-getSampleNames()
search the geneset in the tables and return it
getSearchData(dat = NULL, input = NULL)getSearchData(dat = NULL, input = NULL)
dat |
table data |
input |
input params |
data
x <- getSearchData()x <- getSearchData()
gets selected columns
getSelectedCols(data = NULL, datasetInput = NULL, input = NULL)getSelectedCols(data = NULL, datasetInput = NULL, input = NULL)
data |
all loaded data |
datasetInput |
selected dataset |
input |
user input params |
getSelectedCols()getSelectedCols()
Gathers the user selected dataset output to be displayed.
getSelectedDatasetInput( rdata = NULL, getSelected = NULL, getMostVaried = NULL, mergedComparison = NULL, input = NULL )getSelectedDatasetInput( rdata = NULL, getSelected = NULL, getMostVaried = NULL, mergedComparison = NULL, input = NULL )
rdata |
filtered dataset |
getSelected |
selected data |
getMostVaried |
most varied data |
mergedComparison |
merged comparison data |
input |
input parameters |
data
x <- getSelectedDatasetInput()x <- getSelectedDatasetInput()
Selects user input conditions to run in DESeq.
getSelectInputBox( id = NULL, name = NULL, num = 0, choices = NULL, selected = NULL, cw = 2, multiple = FALSE )getSelectInputBox( id = NULL, name = NULL, num = 0, choices = NULL, selected = NULL, cw = 2, multiple = FALSE )
id |
input id |
name |
label of the box |
num |
panel that is going to be shown |
choices |
sample list |
selected |
selected smaple list |
cw |
column width |
multiple |
if multiple choices are available |
x <- getSelectInputBox()x <- getSelectInputBox()
heatmap selection functionality
getSelHeat(expdata = NULL, input = NULL)getSelHeat(expdata = NULL, input = NULL)
expdata |
selected genes |
input |
input params |
plot
x <- getSelHeat()x <- getSelHeat()
Generates the fill and shape selection boxes for PCA plots. metadata file has to be loaded in this case
getShapeColor(input = NULL)getShapeColor(input = NULL)
input |
input values |
Color and shape from selection boxes or defaults
x <- getShapeColor()x <- getShapeColor()
Generates and displays the starting messgae to be shown once the user has first seen the main plots page within DEBrowser.
getStartPlotsMsg()getStartPlotsMsg()
return start plot msg
getStartPlotsMsg
x <- getStartPlotsMsg()x <- getStartPlotsMsg()
Generates and displays the starting message within DEBrowser.
getStartupMsg()getStartupMsg()
return startup msg
getStartupMsg
x <- getStartupMsg()x <- getStartupMsg()
get table details To be able to put a table into two lines are necessary; into the server part; getTableDetails(output, session, "dataname", data, modal=TRUE) into the ui part; uiOutput(ns("dataname"))
getTableDetails( output = NULL, session = NULL, tablename = NULL, data = NULL, modal = NULL )getTableDetails( output = NULL, session = NULL, tablename = NULL, data = NULL, modal = NULL )
output |
output |
session |
session |
tablename |
table name |
data |
matrix data |
modal |
if it is true, the matrix is going to be in a modal |
panel
x <- getTableDetails()x <- getTableDetails()
getTableModal prepares table modal for KEGG
getTableModal()getTableModal()
the info button
x<- getTableModal()x<- getTableModal()
User defined selection that selects the style of table to display within the DEBrowser.
getTableStyle( dat = NULL, input = NULL, padj = c("padj"), foldChange = c("foldChange"), DEsection = TRUE )getTableStyle( dat = NULL, input = NULL, padj = c("padj"), foldChange = c("foldChange"), DEsection = TRUE )
dat |
dataset |
input |
input params |
padj |
the name of the padj value column in the dataset |
foldChange |
the name of the foldChange column in the dataset |
DEsection |
if it is in DESection or not |
getTableStyle
x <- getTableStyle()x <- getTableStyle()
getTabUpdateJS prepmenu tab and discovery menu tab updates
getTabUpdateJS()getTabUpdateJS()
the JS for tab updates
x<- getTabUpdateJS()x<- getTabUpdateJS()
getUp get up regulated data
getUp(filt_data = NULL)getUp(filt_data = NULL)
filt_data |
filt_data |
data
x <- getUp()x <- getUp()
getUpDown get up+down regulated data
getUpDown(filt_data = NULL)getUpDown(filt_data = NULL)
filt_data |
filt_data |
data
x <- getUpDown()x <- getUpDown()
Adds an id to the data frame being used.
getVariationData(inputdata = NULL, cols = NULL, conds = NULL, key = NULL)getVariationData(inputdata = NULL, cols = NULL, conds = NULL, key = NULL)
inputdata |
dataset |
cols |
columns |
conds |
conditions |
key |
gene or region name |
plotdata
x <- getVariationData()x <- getVariationData()
Generates the left menu to be used for heatmap plots
heatmapControlsUI(id)heatmapControlsUI(id)
id |
module ID |
HeatmapControls
heatmapControlsUI
x <- heatmapControlsUI("heatmap")x <- heatmapControlsUI("heatmap")
heatmap JS code for selection functionality
heatmapJScode()heatmapJScode()
JS Code
x <- heatmapJScode()x <- heatmapJScode()
Sets up shinyServer to be able to run heatmapServer interactively.
heatmapServer(input, output, session)heatmapServer(input, output, session)
input |
input params from UI |
output |
output params to UI |
session |
session variable |
the panel for main plots;
heatmapServer
heatmapServerheatmapServer
Creates a shinyUI to be able to run DEBrowser interactively.
heatmapUI(input, output, session)heatmapUI(input, output, session)
input |
input variables |
output |
output objects |
session |
session |
the panel for heatmapUI;
heatmapUI
x<-heatmapUI()x<-heatmapUI()
Hides a shiny object.
hideObj(btns = NULL)hideObj(btns = NULL)
btns |
hide group of objects with shinyjs |
x <- hideObj()x <- hideObj()
Generates the controls in the left menu for a histogram
histogramControlsUI(id)histogramControlsUI(id)
id |
namespace id |
returns the left menu
histogramControlsUI
x <- histogramControlsUI("histogram")x <- histogramControlsUI("histogram")
install packages if they don't exist display.
installpack(package_name = NULL)installpack(package_name = NULL)
package_name |
package name to be installed |
installpack
x <- installpack()x <- installpack()
Generates the controls in the left menu for an IQR plot#'
IQRPlotControlsUI(id)IQRPlotControlsUI(id)
id |
namespace id |
returns the left menu
IQRPlotControlsUI
x <- IQRPlotControlsUI("IQR")x <- IQRPlotControlsUI("IQR")
get kmeans controls
kmeansControlsUI(id)kmeansControlsUI(id)
id |
module ID |
controls
kmeansControlsUI
x <- kmeansControlsUI("heatmap")x <- kmeansControlsUI("heatmap")
Radio buttons for low count removal methods
lcfMetRadio(id)lcfMetRadio(id)
id |
namespace id |
radio control
lcfMetRadio
x <- lcfMetRadio("lcf")x <- lcfMetRadio("lcf")
load packages
loadpack(package_name = NULL)loadpack(package_name = NULL)
package_name |
package name to be loaded |
loadpack
x <- loadpack()x <- loadpack()
Generates the left menu to be used for main plots
mainPlotControlsUI(id)mainPlotControlsUI(id)
id |
module ID |
mainPlotControls
mainPlotControlsUI
x <- mainPlotControlsUI("main")x <- mainPlotControlsUI("main")
Creates the main scatter, volcano or MA plot to be displayed within the main panel.
mainScatterNew(input = NULL, data = NULL, cond_names = NULL, source = NULL)mainScatterNew(input = NULL, data = NULL, cond_names = NULL, source = NULL)
input |
input params |
data |
dataframe that has log2FoldChange and log10padj values |
cond_names |
condition names |
source |
for event triggering to select genes |
scatter, volcano or MA plot
x <- mainScatterNew()x <- mainScatterNew()
Generates hierarchially clustered K-means clusters
niceKmeans(df = NULL, input = NULL, iter.max = 1000, nstart = 100)niceKmeans(df = NULL, input = NULL, iter.max = 1000, nstart = 100)
df |
data |
input |
user inputs |
iter.max |
max iteration for kmeans clustering |
nstart |
n for kmeans clustering |
heatmap plot area
niceKmeans
x <- niceKmeans()x <- niceKmeans()
Select box to select normalization method prior to batch effect correction
normalizationMethods(id)normalizationMethods(id)
id |
namespace id |
radio control
normalizationMethods
x <- normalizationMethods("batch")x <- normalizationMethods("batch")
get pallete
palUI(id)palUI(id)
id |
namespace ID |
pals
palUI
x <- palUI("heatmap")x <- palUI("heatmap")
Prepares the correlations for the all2all plot.
panel.cor(x, y, prefix = "rho=", cex.cor = 2, ...)panel.cor(x, y, prefix = "rho=", cex.cor = 2, ...)
x |
numeric vector x |
y |
numeric vector y |
prefix |
prefix for the text |
cex.cor |
correlation font size |
... |
additional parameters |
all2all correlation plots
panel.cor(c(1,2,3), c(4,5,6))panel.cor(c(1,2,3), c(4,5,6))
Prepares the historgram for the all2all plot.
panel.hist(x, ...)panel.hist(x, ...)
x |
a vector of values for which the histogram is desired |
... |
any additional params |
all2all histogram plots
panel.hist(1)panel.hist(1)
Generates the PCA PLots Left menu to be displayed within the DEBrowser.
pcaPlotControlsUI(id = "pca")pcaPlotControlsUI(id = "pca")
id |
namespace id |
returns the left menu according to the selected tab;
pcaPlotControlsUI
x <- pcaPlotControlsUI("pca")x <- pcaPlotControlsUI("pca")
Plots the PCA results for the selected dataset.
plot_pca( dat = NULL, pcx = 1, pcy = 2, metadata = NULL, color = NULL, shape = NULL, size = NULL, textonoff = "On", legendSelect = "samples", input = NULL )plot_pca( dat = NULL, pcx = 1, pcy = 2, metadata = NULL, color = NULL, shape = NULL, size = NULL, textonoff = "On", legendSelect = "samples", input = NULL )
dat |
data |
pcx |
x axis label |
pcy |
y axis label |
metadata |
additional data |
color |
color for plot |
shape |
shape for plot |
size |
size of the plot |
textonoff |
text on off |
legendSelect |
select legend |
input |
input param |
pca list
load(system.file("extdata", "demo", "demodata.Rda", package="debrowser")) metadata<-cbind(colnames(demodata[,1:6]), colnames(demodata[,1:6]), c(rep("Cond1",3), rep("Cond2",3))) colnames(metadata)<-c("samples", "color", "shape") a <- plot_pca(getNormalizedMatrix( demodata[rowSums(demodata[,1:6])>10,1:6]), metadata = metadata, color = "samples", size = 5, shape = "shape")load(system.file("extdata", "demo", "demodata.Rda", package="debrowser")) metadata<-cbind(colnames(demodata[,1:6]), colnames(demodata[,1:6]), c(rep("Cond1",3), rep("Cond2",3))) colnames(metadata)<-c("samples", "color", "shape") a <- plot_pca(getNormalizedMatrix( demodata[rowSums(demodata[,1:6])>10,1:6]), metadata = metadata, color = "samples", size = 5, shape = "shape")
prepare plot data for mainplots
plotData(pdata = NULL, input = NULL)plotData(pdata = NULL, input = NULL)
pdata |
data |
input |
input |
prepdata
plotData
x <- plotData()x <- plotData()
Margins module for plotly plots
plotMarginsUI(id, t = 20, b = 100, l = 100, r = 20)plotMarginsUI(id, t = 20, b = 100, l = 100, r = 20)
id |
id |
t |
top margin |
b |
bottom margin |
l |
left margin |
r |
right margin |
size and margins controls
plotMarginsUI
x <- plotMarginsUI("heatmap")x <- plotMarginsUI("heatmap")
Size and margins module for plotly plots
plotSizeMarginsUI(id, w = 800, h = 640, t = 20, b = 100, l = 100, r = 20)plotSizeMarginsUI(id, w = 800, h = 640, t = 20, b = 100, l = 100, r = 20)
id |
id |
w |
width |
h |
height |
t |
top margin |
b |
bottom margin |
l |
left margin |
r |
right margin |
size and margins controls
plotSizeMarginsUI
x <- plotSizeMarginsUI("heatmap")x <- plotSizeMarginsUI("heatmap")
Size module for plotly plots
plotSizeUI(id, w = 800, h = 600)plotSizeUI(id, w = 800, h = 600)
id |
id |
w |
width |
h |
height |
size and margins controls
plotSizeUI
x <- plotSizeUI("heatmap")x <- plotSizeUI("heatmap")
Plot download type
plotTypeUI(id)plotTypeUI(id)
id |
id |
size and margins controls
plotTypeUI
x <- plotTypeUI("heatmap")x <- plotTypeUI("heatmap")
Prepares the data container that stores values used within DESeq.
prepDataContainer(data = NULL, counter = NULL, input = NULL, meta = NULL)prepDataContainer(data = NULL, counter = NULL, input = NULL, meta = NULL)
data |
loaded dataset |
counter |
the number of comparisons |
input |
input parameters |
meta |
loaded metadata |
data
x <- prepDataContainer()x <- prepDataContainer()
prepare group table
prepGroup(conds = NULL, cols = NULL, metadata = NULL, covariates = NULL)prepGroup(conds = NULL, cols = NULL, metadata = NULL, covariates = NULL)
conds |
inputconds |
cols |
columns |
metadata |
metadata |
covariates |
covariates |
data
x <- prepGroup()x <- prepGroup()
scales the data
prepHeatData(expdata = NULL, input = NULL)prepHeatData(expdata = NULL, input = NULL)
expdata |
a matrixthat includes expression values |
input |
input variables |
heatdata
x <- prepHeatData()x <- prepHeatData()
prepares pca data with metadata. If metadata doesn't exists it puts all the sampels into a signlge group; "Conds".
prepPCADat(pca_data = NULL, metadata = NULL, input = NULL, pcx = 1, pcy = 2)prepPCADat(pca_data = NULL, metadata = NULL, input = NULL, pcx = 1, pcy = 2)
pca_data |
pca run results |
metadata |
additional meta data |
input |
input |
pcx |
x axis label |
pcy |
y axis label |
Color and shape from selection boxes or defaults
x <- prepPCADat()x <- prepPCADat()
Push an object to the list.
push(l, ...)push(l, ...)
l |
that are going to push to the list |
... |
list object |
combined list
mylist <- list() newlist <- push ( 1, mylist )mylist <- list() newlist <- push ( 1, mylist )
remove unnecessary columns
removeCols(cols = NULL, dat = NULL)removeCols(cols = NULL, dat = NULL)
cols |
columns that are going to be removed from data frame |
dat |
data |
data
x <- removeCols()x <- removeCols()
remove extra columns for QC plots
removeExtraCols(dat = NULL)removeExtraCols(dat = NULL)
dat |
selected data |
removeExtraCols()removeExtraCols()
Plot PCA results.
round_vals(l)round_vals(l)
l |
the value |
round value
x<-round_vals(5.1323223)x<-round_vals(5.1323223)
Runs PCA on the selected dataset.
run_pca(x = NULL, retx = TRUE, center = TRUE, scale = TRUE)run_pca(x = NULL, retx = TRUE, center = TRUE, scale = TRUE)
x |
dataframe with experiment data |
retx |
specifies if the data should be returned |
center |
center the PCA (Boolean) |
scale |
scale the PCA (Boolean) |
pca list
load(system.file("extdata", "demo", "demodata.Rda", package="debrowser")) pca_data<-run_pca(getNormalizedMatrix( demodata[rowSums(demodata[,1:6])>10,1:6]))load(system.file("extdata", "demo", "demodata.Rda", package="debrowser")) pca_data<-run_pca(getNormalizedMatrix( demodata[rowSums(demodata[,1:6])>10,1:6]))
Run DE algorithms on the selected parameters. Output is to be used for the interactive display.
runDE( data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )runDE( data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )
data |
A matrix that includes all the expression raw counts, rownames has to be the gene, isoform or region names/IDs |
metadata |
metadata of the matrix of expression raw counts |
columns |
is a vector that includes the columns that are going to be analyzed. These columns has to match with the given data. |
conds |
experimental conditions. The order has to match with the column order |
params |
all params for the DE methods |
de results
x <- runDE()x <- runDE()
Run DESeq2 algorithm on the selected conditions. Output is to be used for the interactive display.
runDESeq2( data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )runDESeq2( data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )
data |
A matrix that includes all the expression raw counts, rownames has to be the gene, isoform or region names/IDs |
metadata |
metadata of the matrix of expression raw counts |
columns |
is a vector that includes the columns that are going to be analyzed. These columns has to match with the given data. |
conds |
experimental conditions. The order has to match with the column order |
params |
fitType: either "parametric", "local", or "mean" for the type of fitting of dispersions to the mean intensity. See estimateDispersions for description. betaPrior: whether or not to put a zero-mean normal prior on the non-intercept coefficients See nbinomWaldTest for description of the calculation of the beta prior. By default, the beta prior is used only for the Wald test, but can also be specified for the likelihood ratio test. testType: either "Wald" or "LRT", which will then use either Wald significance tests (defined by nbinomWaldTest), or the likelihood ratio test on the difference in deviance between a full and reduced model formula (defined by nbinomLRT) shrinkage: Adds shrunken log2 fold changes (LFC) and SE to a results table from DESeq run without LFC shrinkage. For consistency with results, the column name lfcSE is used here although what is returned is a posterior SD. Three shrinkage estimators for LFC are available via type (see the vignette for more details on the estimators). The apeglm publication demonstrates that 'apeglm' and 'ashr' outperform the original 'normal' shrinkage estimator. |
deseq2 results
x <- runDESeq2()x <- runDESeq2()
Run EdgeR algorithm on the selected conditions. Output is to be used for the interactive display.
runEdgeR( data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )runEdgeR( data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )
data |
A matrix that includes all the expression raw counts, rownames has to be the gene, isoform or region names/IDs |
metadata |
metadata of the matrix of expression raw counts |
columns |
is a vector that includes the columns that are going to be analyzed. These columns has to match with the given data. |
conds |
experimental conditions. The order has to match with the column order |
params |
normfact: Calculate normalization factors to scale the raw library sizes. Values can be "TMM","RLE","upperquartile","none". dispersion: either a numeric vector of dispersions or a character string indicating that dispersions should be taken from the data object. If a numeric vector, then can be either of length one or of length equal to the number of genes. Allowable character values are "common", "trended", "tagwise" or "auto". Default behavior ("auto" is to use most complex dispersions found in data object. testType: exactTest or glmLRT. exactTest: Computes p-values for differential abundance for each gene between two digital libraries, conditioning on the total count for each gene. The counts in each group as a proportion of the whole are assumed to follow a binomial distribution. glmLRT: Fit a negative binomial generalized log-linear model to the read counts for each gene. Conduct genewise statistical tests for a given coefficient or coefficient contrast. |
edgeR results
x <- runEdgeR()x <- runEdgeR()
Creates a heatmap based on the user selected parameters within shiny
runHeatmap(input = NULL, session = NULL, expdata = NULL)runHeatmap(input = NULL, session = NULL, expdata = NULL)
input |
input variables |
session |
session |
expdata |
a matrix that includes expression values |
heatmapply plot
x <- runHeatmap()x <- runHeatmap()
Creates a heatmap based on the user selected parameters within shiny
runHeatmap2(input = NULL, session = NULL, expdata = NULL)runHeatmap2(input = NULL, session = NULL, expdata = NULL)
input |
input variables |
session |
session |
expdata |
a matrix that includes expression values |
heatmap.2
x <- runHeatmap2()x <- runHeatmap2()
Run Limma algorithm on the selected conditions. Output is to be used for the interactive display.
runLimma( data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )runLimma( data = NULL, metadata = NULL, columns = NULL, conds = NULL, params = NULL )
data |
A matrix that includes all the expression raw counts, rownames has to be the gene, isoform or region names/IDs |
metadata |
metadata of the matrix of expression raw counts |
columns |
is a vector that includes the columns that are going to be analyzed. These columns has to match with the given data. |
conds |
experimental conditions. The order has to match with the column order |
params |
normfact: Calculate normalization factors to scale the raw library sizes. Values can be "TMM","RLE","upperquartile","none". fitType, fitting method; "ls" for least squares or "robust" for robust regression normBet: Normalizes expression intensities so that the intensities or log-ratios have similar distributions across a set of arrays. |
Limma results
x <- runLimma()x <- runLimma()
Selects user input conditions, multiple if present, to be used in DESeq.
selectConditions( Dataset = NULL, metadata = NULL, choicecounter = NULL, session = NULL, input = NULL )selectConditions( Dataset = NULL, metadata = NULL, choicecounter = NULL, session = NULL, input = NULL )
Dataset |
used dataset |
metadata |
metadatatable to select from metadata |
choicecounter |
choicecounter to add multiple comparisons |
session |
session |
input |
input params |
the panel for go plots;
selectConditions
x<- selectConditions()x<- selectConditions()
Selects user input conditions to run in DESeq.
selectedInput(id = NULL, num = 0, default = NULL, input = NULL)selectedInput(id = NULL, num = 0, default = NULL, input = NULL)
id |
input id |
num |
panel that is going to be shown |
default |
default text |
input |
input params |
x <- selectedInput()x <- selectedInput()
Group info column selection. This can be used in batch effect or coloring the groups in the plots.
selectGroupInfo( metadata = NULL, input = NULL, selectname = "groupselect", label = "Group info" )selectGroupInfo( metadata = NULL, input = NULL, selectname = "groupselect", label = "Group info" )
metadata |
metadata |
input |
input values |
selectname |
name of the select box |
label |
label of the select box |
selectGroupInfo
x <- selectGroupInfo()x <- selectGroupInfo()
Radio button for separators
sepRadio(id, name)sepRadio(id, name)
id |
module id |
name |
name |
radio control
sepRadio
x <- sepRadio("meta", "metadata")x <- sepRadio("meta", "metadata")
setBatch to skip batch effect correction batch variable set with the filter results
setBatch(fd = NULL)setBatch(fd = NULL)
fd |
filtered data |
fd data
x <- setBatch()x <- setBatch()
Displays a shiny object.
showObj(btns = NULL)showObj(btns = NULL)
btns |
show group of objects with shinyjs |
x <- showObj()x <- showObj()
Starts the DEBrowser to be able to run interactively.
startDEBrowser()startDEBrowser()
the app
startDEBrowser
startDEBrowser()startDEBrowser()
Starts the DEBrowser heatmap
startHeatmap()startHeatmap()
the app
startHeatmap
startHeatmap()startHeatmap()
Generates a text area input to be used for gene selection within the DEBrowser.
textareaInput(id, label, value, rows = 20, cols = 35, class = "form-control")textareaInput(id, label, value, rows = 20, cols = 35, class = "form-control")
id |
id of the control |
label |
label of the control |
value |
initial value |
rows |
the # of rows |
cols |
the # of cols |
class |
css class |
x <- textareaInput("genesetarea", "Gene Set", "Fgf21", rows = 5, cols = 35)x <- textareaInput("genesetarea", "Gene Set", "Fgf21", rows = 5, cols = 35)
User defined toggle to display which panels are to be shown within DEBrowser.
togglePanels(num = NULL, nums = NULL, session = NULL)togglePanels(num = NULL, nums = NULL, session = NULL)
num |
selected panel |
nums |
all panels |
session |
session info |
togglePanels
x <- togglePanels()x <- togglePanels()