In fmicompbio/TabulaMurisSenisData: Bulk and single-cell RNA-seq data from the Tabula Muris Senis project
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(SingleCellExperiment)
library(TabulaMurisSenisData)
library(ggplot2)
Introduction
This package provides access to the processed bulk and single-cell RNA-seq data
from the Tabula Muris Senis data set
[@Schaum2019-nf;@Tabula_Muris_Consortium2020-um]. The processed bulk RNA-seq
data was downloaded from GEO (accession number
GSE132040). The
single-cell data (droplet and FACS) was obtained from FigShare (see below for links).
All data sets are provided as SingleCellExperiment objects for easy access
and use within the Bioconductor ecosystem.
Bulk RNA-seq data
The bulk RNA-seq data can be accessed via the TabulaMurisSenisBulk()
function. By setting the argument infoOnly to TRUE, we can get information
about the size of the data set before downloading it.
tmp <- TabulaMurisSenisBulk(infoOnly = TRUE)
tms_bulk <- TabulaMurisSenisBulk()
tms_bulk
We list the available tissues.
table(colData(tms_bulk)$organ)
Single-cell RNA-seq data
Droplet data
The data files for the droplet single-cell RNA-seq data were downloaded from FigShare:
We list the available tissues.
listTabulaMurisSenisTissues(dataset = "Droplet")
As for the bulk data, we can print the size of the data set before
downloading it.
tmp <- TabulaMurisSenisDroplet(tissues = "All", infoOnly = TRUE)
tms_droplet <- TabulaMurisSenisDroplet(tissues = "All")
tms_droplet
We plot the UMAP of the entire data set and color by tissue, to re-create the plot from here.
# tissue colors
tissue_cols <- c(Pancreas = "#3182bd", Thymus = "#31a354",
Trachea = "#636363", Bladder = "#637939",
Lung = "#7b4173", Large_Intestine = "#843c39",
Fat = "#969696", Tongue = "#a1d99b",
Mammary_Gland = "#ce6dbd", Limb_Muscle = "#d6616b",
Marrow = "#de9ed6", Skin = "#e6550d",
Liver = "#e7969c", Heart_and_Aorta = "#e7ba52",
Kidney = "#e7cb94", Spleen = "#fd8d3c")
# get dataset with all tissues
se <- tms_droplet$All
se
# prepare data set for ggplot
ds <- as.data.frame(reducedDim(se, "UMAP"))
ds <- cbind(ds, tissue = colData(se)$tissue)
head(ds)
# plot
ggplot(ds, aes(x = UMAP1, y = UMAP2, color = tissue)) +
geom_point(size = 0.05) +
scale_color_manual(values = tissue_cols) +
theme_classic() +
guides(colour = guide_legend(override.aes = list(size = 5)))
FACS data
The data files for the FACS single-cell RNA-seq data were downloaded from FigShare:
We list the available tissues.
listTabulaMurisSenisTissues(dataset = "FACS")
Also here, we can print the size of the data set before downloading it.
tmp <- TabulaMurisSenisFACS(tissues = "All", infoOnly = TRUE)
tms_facs <- TabulaMurisSenisFACS(tissues = "All")
tms_facs
We plot the UMAP of the entire data set and color by tissue, to re-create the plot from here.
# tissue colors
tissue_cols <- c(Skin = "#e6550d", Pancreas = "#3182bd",
Limb_Muscle = "#d6616b", Heart = "#e7ba52",
Spleen = "#fd8d3c", Diaphragm = "#8c6d31",
Trachea = "#636363", Tongue = "#a1d99b",
Thymus = "#31a354", `Brain_Non-Myeloid` = "#cedb9c",
Brain_Myeloid = "#b5cf6b", Bladder = "#637939",
Large_Intestine = "#843c39", BAT = "#9c9ede",
GAT = "#bd9e39", MAT = "#a55194", SCAT = "#6baed6",
Lung = "#7b4173", Liver = "#e7969c",
Marrow = "#de9ed6", Kidney = "#e7cb94",
Aorta = "#393b79", Mammary_Gland = "#ce6dbd")
# get dataset with all tissues
se <- tms_facs$All
se
# prepare data set for ggplot
ds <- as.data.frame(reducedDim(se, "UMAP"))
ds <- cbind(ds, tissue = colData(se)$tissue)
head(ds)
# plot
ggplot(ds, aes(x = UMAP1, y = UMAP2, color = tissue)) +
geom_point(size = 0.05) +
scale_color_manual(values = tissue_cols) +
theme_classic() +
guides(colour = guide_legend(override.aes = list(size = 5)))
Explore data with iSEE
The Tabula Muris Senis datasets are provided in the form of SingleCellExperiment objects.
A natural companion to this data structure is the r Biocpkg("iSEE") package, which can be used for interactive and reproducible data exploration.
Any analysis steps should be performed in advance before calling iSEE, and since these datasets can be quite big, the operations can be time consuming, and/or require a considerable amount of resources.
sce_all_facs <- TabulaMurisSenisFACS(tissues = "All", processedCounts = TRUE)$All
sce_all_facs
We can see that the sce_all_facs contains both raw and processed counts (in the assays slots counts and logcounts), but also the PCA and UMAP embeddings as provided in the original publication.
assayNames(sce_all_facs)
reducedDimNames(sce_all_facs)
If desired, additional processing steps can be performed on the sce_all_facs - e.g., storing signature scores (computed via r Biocpkg("AUCell")) as colData elements.
Once these steps are completed, launching iSEE is as easy as
library(iSEE)
library(iSEE)
iSEE(sce_all_facs)
This will launch iSEE with a standard default set of panels.
Optionally, we can configure the initial set to start the app in the desired configuration - below, we show how to start iSEE with:
- a
ReducedDimensionPlot displaying the UMAP, colored by the expression of a feature;
- a
RowDataTable, for selecting a feature to be transmitted to the other panels (showing all genes whose name contains "Col1");
- a
FeatureAssayPlot, for showing the expression of the gene selected in the RowDataTable, split by tissue.
initial <- list()
################################################################################
# (Compact) Settings for Reduced dimension plot 1
################################################################################
initial[["ReducedDimensionPlot1"]] <- new(
"ReducedDimensionPlot",
DataBoxOpen = TRUE,
Type = "UMAP",
VisualBoxOpen = TRUE,
ColorBy = "Feature name",
ColorByFeatureName = "Col1a1",
ColorByFeatureSource = "RowDataTable1",
ColorByFeatureDynamicSource = FALSE
)
################################################################################
# (Compact) Settings for Row data table 1
################################################################################
initial[["RowDataTable1"]] <- new(
"RowDataTable",
Selected = "Col1a1",
Search = "Col1"
)
################################################################################
# (Compact) Settings for Feature assay plot 1
################################################################################
initial[["FeatureAssayPlot1"]] <- new(
"FeatureAssayPlot",
DataBoxOpen = TRUE,
Assay = "logcounts",
XAxis = "Column data",
XAxisColumnData = "tissue",
YAxisFeatureName = "Col1a1",
YAxisFeatureSource = "RowDataTable1"
)
Click here to display the complete configuration chunk
initial <- list()
################################################################################
# Settings for Reduced dimension plot 1
################################################################################
initial[["ReducedDimensionPlot1"]] <- new("ReducedDimensionPlot", Type = "UMAP", XAxis = 1L, YAxis = 2L,
FacetRowByColData = "FACS.selection", FacetColumnByColData = "FACS.selection",
ColorByColumnData = "FACS.selection", ColorByFeatureNameAssay = "logcounts",
ColorBySampleNameColor = "#FF0000", ShapeByColumnData = "FACS.selection",
SizeByColumnData = "n_genes", FacetRowBy = "None", FacetColumnBy = "None",
ColorBy = "Feature name", ColorByDefaultColor = "#000000",
ColorByFeatureName = "Col1a1", ColorByFeatureSource = "RowDataTable1",
ColorByFeatureDynamicSource = FALSE, ColorBySampleName = "A10_B000497_B009023_S10.mm10-plus-0-0",
ColorBySampleSource = "---", ColorBySampleDynamicSource = FALSE,
ShapeBy = "None", SizeBy = "None", SelectionAlpha = 0.1,
ZoomData = numeric(0), BrushData = list(), VisualBoxOpen = TRUE,
VisualChoices = "Color", ContourAdd = FALSE, ContourColor = "#0000FF",
PointSize = 1, PointAlpha = 1, Downsample = FALSE, DownsampleResolution = 200,
CustomLabels = FALSE, CustomLabelsText = "A10_B000497_B009023_S10.mm10-plus-0-0",
FontSize = 1, LegendPointSize = 1, LegendPosition = "Bottom",
HoverInfo = TRUE, LabelCenters = FALSE, LabelCentersBy = "FACS.selection",
LabelCentersColor = "#000000", VersionInfo = list(iSEE = structure(list(
c(2L, 5L, 1L)), class = c("package_version", "numeric_version"
))), PanelId = c(ReducedDimensionPlot = 1L), PanelHeight = 500L,
PanelWidth = 4L, SelectionBoxOpen = FALSE, RowSelectionSource = "---",
ColumnSelectionSource = "---", DataBoxOpen = FALSE, RowSelectionDynamicSource = FALSE,
ColumnSelectionDynamicSource = FALSE, RowSelectionRestrict = FALSE,
ColumnSelectionRestrict = FALSE, SelectionHistory = list())
################################################################################
# Settings for Row data table 1
################################################################################
initial[["RowDataTable1"]] <- new("RowDataTable", Selected = "Col1a1", Search = "Col1", SearchColumns = c("",
"", "", "", ""), HiddenColumns = character(0), VersionInfo = list(
iSEE = structure(list(c(2L, 5L, 1L)), class = c("package_version",
"numeric_version"))), PanelId = c(RowDataTable = 1L), PanelHeight = 500L,
PanelWidth = 4L, SelectionBoxOpen = FALSE, RowSelectionSource = "---",
ColumnSelectionSource = "---", DataBoxOpen = FALSE, RowSelectionDynamicSource = FALSE,
ColumnSelectionDynamicSource = FALSE, RowSelectionRestrict = FALSE,
ColumnSelectionRestrict = FALSE, SelectionHistory = list())
################################################################################
# Settings for Feature assay plot 1
################################################################################
initial[["FeatureAssayPlot1"]] <- new("FeatureAssayPlot", Assay = "logcounts", XAxis = "Column data",
XAxisColumnData = "tissue", XAxisFeatureName = "0610005C13Rik",
XAxisFeatureSource = "---", XAxisFeatureDynamicSource = FALSE,
YAxisFeatureName = "Col1a1", YAxisFeatureSource = "RowDataTable1",
YAxisFeatureDynamicSource = FALSE, FacetRowByColData = "FACS.selection",
FacetColumnByColData = "FACS.selection", ColorByColumnData = "age",
ColorByFeatureNameAssay = "logcounts", ColorBySampleNameColor = "#FF0000",
ShapeByColumnData = "FACS.selection", SizeByColumnData = "n_genes",
FacetRowBy = "None", FacetColumnBy = "None", ColorBy = "None",
ColorByDefaultColor = "#000000", ColorByFeatureName = "0610005C13Rik",
ColorByFeatureSource = "---", ColorByFeatureDynamicSource = FALSE,
ColorBySampleName = "A10_B000497_B009023_S10.mm10-plus-0-0",
ColorBySampleSource = "---", ColorBySampleDynamicSource = FALSE,
ShapeBy = "None", SizeBy = "None", SelectionAlpha = 0.1,
ZoomData = numeric(0), BrushData = list(), VisualBoxOpen = FALSE,
VisualChoices = "Color", ContourAdd = FALSE, ContourColor = "#0000FF",
PointSize = 1, PointAlpha = 1, Downsample = FALSE, DownsampleResolution = 200,
CustomLabels = FALSE, CustomLabelsText = "A10_B000497_B009023_S10.mm10-plus-0-0",
FontSize = 1, LegendPointSize = 1, LegendPosition = "Bottom",
HoverInfo = TRUE, LabelCenters = FALSE, LabelCentersBy = "FACS.selection",
LabelCentersColor = "#000000", VersionInfo = list(iSEE = structure(list(
c(2L, 5L, 1L)), class = c("package_version", "numeric_version"
))), PanelId = c(FeatureAssayPlot = 1L), PanelHeight = 500L,
PanelWidth = 4L, SelectionBoxOpen = FALSE, RowSelectionSource = "---",
ColumnSelectionSource = "---", DataBoxOpen = FALSE, RowSelectionDynamicSource = FALSE,
ColumnSelectionDynamicSource = FALSE, RowSelectionRestrict = FALSE,
ColumnSelectionRestrict = FALSE, SelectionHistory = list())
initial
iSEE can then be launched with the following command:
iSEE(sce_all_facs, initial = initial)
knitr::include_graphics("ss_iSEE_facsdataset.jpg")
Note that these lengthy configuration options can be readily exported by clicking on the dedicated button "Display panel settings" in iSEE - we can do that anytime after we are done designing the configuration using the app interface.
Session info {-}
sessionInfo()
References
fmicompbio/TabulaMurisSenisData documentation built on May 2, 2024, 7:22 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
library(SingleCellExperiment) library(TabulaMurisSenisData) library(ggplot2)
Introduction
This package provides access to the processed bulk and single-cell RNA-seq data
from the Tabula Muris Senis data set
[@Schaum2019-nf;@Tabula_Muris_Consortium2020-um]. The processed bulk RNA-seq
data was downloaded from GEO (accession number
GSE132040). The
single-cell data (droplet and FACS) was obtained from FigShare (see below for links).
All data sets are provided as SingleCellExperiment objects for easy access
and use within the Bioconductor ecosystem.
Bulk RNA-seq data
The bulk RNA-seq data can be accessed via the TabulaMurisSenisBulk()
function. By setting the argument infoOnly to TRUE, we can get information
about the size of the data set before downloading it.
tmp <- TabulaMurisSenisBulk(infoOnly = TRUE) tms_bulk <- TabulaMurisSenisBulk() tms_bulk
We list the available tissues.
table(colData(tms_bulk)$organ)
Single-cell RNA-seq data
Droplet data
The data files for the droplet single-cell RNA-seq data were downloaded from FigShare:
We list the available tissues.
listTabulaMurisSenisTissues(dataset = "Droplet")
As for the bulk data, we can print the size of the data set before downloading it.
tmp <- TabulaMurisSenisDroplet(tissues = "All", infoOnly = TRUE) tms_droplet <- TabulaMurisSenisDroplet(tissues = "All") tms_droplet
We plot the UMAP of the entire data set and color by tissue, to re-create the plot from here.
# tissue colors tissue_cols <- c(Pancreas = "#3182bd", Thymus = "#31a354", Trachea = "#636363", Bladder = "#637939", Lung = "#7b4173", Large_Intestine = "#843c39", Fat = "#969696", Tongue = "#a1d99b", Mammary_Gland = "#ce6dbd", Limb_Muscle = "#d6616b", Marrow = "#de9ed6", Skin = "#e6550d", Liver = "#e7969c", Heart_and_Aorta = "#e7ba52", Kidney = "#e7cb94", Spleen = "#fd8d3c") # get dataset with all tissues se <- tms_droplet$All se # prepare data set for ggplot ds <- as.data.frame(reducedDim(se, "UMAP")) ds <- cbind(ds, tissue = colData(se)$tissue) head(ds) # plot ggplot(ds, aes(x = UMAP1, y = UMAP2, color = tissue)) + geom_point(size = 0.05) + scale_color_manual(values = tissue_cols) + theme_classic() + guides(colour = guide_legend(override.aes = list(size = 5)))
FACS data
The data files for the FACS single-cell RNA-seq data were downloaded from FigShare:
We list the available tissues.
listTabulaMurisSenisTissues(dataset = "FACS")
Also here, we can print the size of the data set before downloading it.
tmp <- TabulaMurisSenisFACS(tissues = "All", infoOnly = TRUE) tms_facs <- TabulaMurisSenisFACS(tissues = "All") tms_facs
We plot the UMAP of the entire data set and color by tissue, to re-create the plot from here.
# tissue colors tissue_cols <- c(Skin = "#e6550d", Pancreas = "#3182bd", Limb_Muscle = "#d6616b", Heart = "#e7ba52", Spleen = "#fd8d3c", Diaphragm = "#8c6d31", Trachea = "#636363", Tongue = "#a1d99b", Thymus = "#31a354", `Brain_Non-Myeloid` = "#cedb9c", Brain_Myeloid = "#b5cf6b", Bladder = "#637939", Large_Intestine = "#843c39", BAT = "#9c9ede", GAT = "#bd9e39", MAT = "#a55194", SCAT = "#6baed6", Lung = "#7b4173", Liver = "#e7969c", Marrow = "#de9ed6", Kidney = "#e7cb94", Aorta = "#393b79", Mammary_Gland = "#ce6dbd") # get dataset with all tissues se <- tms_facs$All se # prepare data set for ggplot ds <- as.data.frame(reducedDim(se, "UMAP")) ds <- cbind(ds, tissue = colData(se)$tissue) head(ds) # plot ggplot(ds, aes(x = UMAP1, y = UMAP2, color = tissue)) + geom_point(size = 0.05) + scale_color_manual(values = tissue_cols) + theme_classic() + guides(colour = guide_legend(override.aes = list(size = 5)))
Explore data with iSEE
The Tabula Muris Senis datasets are provided in the form of SingleCellExperiment objects.
A natural companion to this data structure is the r Biocpkg("iSEE") package, which can be used for interactive and reproducible data exploration.
Any analysis steps should be performed in advance before calling iSEE, and since these datasets can be quite big, the operations can be time consuming, and/or require a considerable amount of resources.
sce_all_facs <- TabulaMurisSenisFACS(tissues = "All", processedCounts = TRUE)$All sce_all_facs
We can see that the sce_all_facs contains both raw and processed counts (in the assays slots counts and logcounts), but also the PCA and UMAP embeddings as provided in the original publication.
assayNames(sce_all_facs) reducedDimNames(sce_all_facs)
If desired, additional processing steps can be performed on the sce_all_facs - e.g., storing signature scores (computed via r Biocpkg("AUCell")) as colData elements.
Once these steps are completed, launching iSEE is as easy as
library(iSEE)
library(iSEE) iSEE(sce_all_facs)
This will launch iSEE with a standard default set of panels.
Optionally, we can configure the initial set to start the app in the desired configuration - below, we show how to start iSEE with:
- a
ReducedDimensionPlotdisplaying the UMAP, colored by the expression of a feature; - a
RowDataTable, for selecting a feature to be transmitted to the other panels (showing all genes whose name contains "Col1"); - a
FeatureAssayPlot, for showing the expression of the gene selected in theRowDataTable, split by tissue.
initial <- list() ################################################################################ # (Compact) Settings for Reduced dimension plot 1 ################################################################################ initial[["ReducedDimensionPlot1"]] <- new( "ReducedDimensionPlot", DataBoxOpen = TRUE, Type = "UMAP", VisualBoxOpen = TRUE, ColorBy = "Feature name", ColorByFeatureName = "Col1a1", ColorByFeatureSource = "RowDataTable1", ColorByFeatureDynamicSource = FALSE ) ################################################################################ # (Compact) Settings for Row data table 1 ################################################################################ initial[["RowDataTable1"]] <- new( "RowDataTable", Selected = "Col1a1", Search = "Col1" ) ################################################################################ # (Compact) Settings for Feature assay plot 1 ################################################################################ initial[["FeatureAssayPlot1"]] <- new( "FeatureAssayPlot", DataBoxOpen = TRUE, Assay = "logcounts", XAxis = "Column data", XAxisColumnData = "tissue", YAxisFeatureName = "Col1a1", YAxisFeatureSource = "RowDataTable1" )
Click here to display the complete configuration chunk
initial <- list() ################################################################################ # Settings for Reduced dimension plot 1 ################################################################################ initial[["ReducedDimensionPlot1"]] <- new("ReducedDimensionPlot", Type = "UMAP", XAxis = 1L, YAxis = 2L, FacetRowByColData = "FACS.selection", FacetColumnByColData = "FACS.selection", ColorByColumnData = "FACS.selection", ColorByFeatureNameAssay = "logcounts", ColorBySampleNameColor = "#FF0000", ShapeByColumnData = "FACS.selection", SizeByColumnData = "n_genes", FacetRowBy = "None", FacetColumnBy = "None", ColorBy = "Feature name", ColorByDefaultColor = "#000000", ColorByFeatureName = "Col1a1", ColorByFeatureSource = "RowDataTable1", ColorByFeatureDynamicSource = FALSE, ColorBySampleName = "A10_B000497_B009023_S10.mm10-plus-0-0", ColorBySampleSource = "---", ColorBySampleDynamicSource = FALSE, ShapeBy = "None", SizeBy = "None", SelectionAlpha = 0.1, ZoomData = numeric(0), BrushData = list(), VisualBoxOpen = TRUE, VisualChoices = "Color", ContourAdd = FALSE, ContourColor = "#0000FF", PointSize = 1, PointAlpha = 1, Downsample = FALSE, DownsampleResolution = 200, CustomLabels = FALSE, CustomLabelsText = "A10_B000497_B009023_S10.mm10-plus-0-0", FontSize = 1, LegendPointSize = 1, LegendPosition = "Bottom", HoverInfo = TRUE, LabelCenters = FALSE, LabelCentersBy = "FACS.selection", LabelCentersColor = "#000000", VersionInfo = list(iSEE = structure(list( c(2L, 5L, 1L)), class = c("package_version", "numeric_version" ))), PanelId = c(ReducedDimensionPlot = 1L), PanelHeight = 500L, PanelWidth = 4L, SelectionBoxOpen = FALSE, RowSelectionSource = "---", ColumnSelectionSource = "---", DataBoxOpen = FALSE, RowSelectionDynamicSource = FALSE, ColumnSelectionDynamicSource = FALSE, RowSelectionRestrict = FALSE, ColumnSelectionRestrict = FALSE, SelectionHistory = list()) ################################################################################ # Settings for Row data table 1 ################################################################################ initial[["RowDataTable1"]] <- new("RowDataTable", Selected = "Col1a1", Search = "Col1", SearchColumns = c("", "", "", "", ""), HiddenColumns = character(0), VersionInfo = list( iSEE = structure(list(c(2L, 5L, 1L)), class = c("package_version", "numeric_version"))), PanelId = c(RowDataTable = 1L), PanelHeight = 500L, PanelWidth = 4L, SelectionBoxOpen = FALSE, RowSelectionSource = "---", ColumnSelectionSource = "---", DataBoxOpen = FALSE, RowSelectionDynamicSource = FALSE, ColumnSelectionDynamicSource = FALSE, RowSelectionRestrict = FALSE, ColumnSelectionRestrict = FALSE, SelectionHistory = list()) ################################################################################ # Settings for Feature assay plot 1 ################################################################################ initial[["FeatureAssayPlot1"]] <- new("FeatureAssayPlot", Assay = "logcounts", XAxis = "Column data", XAxisColumnData = "tissue", XAxisFeatureName = "0610005C13Rik", XAxisFeatureSource = "---", XAxisFeatureDynamicSource = FALSE, YAxisFeatureName = "Col1a1", YAxisFeatureSource = "RowDataTable1", YAxisFeatureDynamicSource = FALSE, FacetRowByColData = "FACS.selection", FacetColumnByColData = "FACS.selection", ColorByColumnData = "age", ColorByFeatureNameAssay = "logcounts", ColorBySampleNameColor = "#FF0000", ShapeByColumnData = "FACS.selection", SizeByColumnData = "n_genes", FacetRowBy = "None", FacetColumnBy = "None", ColorBy = "None", ColorByDefaultColor = "#000000", ColorByFeatureName = "0610005C13Rik", ColorByFeatureSource = "---", ColorByFeatureDynamicSource = FALSE, ColorBySampleName = "A10_B000497_B009023_S10.mm10-plus-0-0", ColorBySampleSource = "---", ColorBySampleDynamicSource = FALSE, ShapeBy = "None", SizeBy = "None", SelectionAlpha = 0.1, ZoomData = numeric(0), BrushData = list(), VisualBoxOpen = FALSE, VisualChoices = "Color", ContourAdd = FALSE, ContourColor = "#0000FF", PointSize = 1, PointAlpha = 1, Downsample = FALSE, DownsampleResolution = 200, CustomLabels = FALSE, CustomLabelsText = "A10_B000497_B009023_S10.mm10-plus-0-0", FontSize = 1, LegendPointSize = 1, LegendPosition = "Bottom", HoverInfo = TRUE, LabelCenters = FALSE, LabelCentersBy = "FACS.selection", LabelCentersColor = "#000000", VersionInfo = list(iSEE = structure(list( c(2L, 5L, 1L)), class = c("package_version", "numeric_version" ))), PanelId = c(FeatureAssayPlot = 1L), PanelHeight = 500L, PanelWidth = 4L, SelectionBoxOpen = FALSE, RowSelectionSource = "---", ColumnSelectionSource = "---", DataBoxOpen = FALSE, RowSelectionDynamicSource = FALSE, ColumnSelectionDynamicSource = FALSE, RowSelectionRestrict = FALSE, ColumnSelectionRestrict = FALSE, SelectionHistory = list()) initial
iSEE can then be launched with the following command:
iSEE(sce_all_facs, initial = initial)
knitr::include_graphics("ss_iSEE_facsdataset.jpg")
Note that these lengthy configuration options can be readily exported by clicking on the dedicated button "Display panel settings" in iSEE - we can do that anytime after we are done designing the configuration using the app interface.
Session info {-}
sessionInfo()
References
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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