diffSCEs | R Documentation |
Combine the data from several SingleCellExperiment objects and produce some basic plots comparing them to a reference.
diffSCEs(
sces,
ref,
point.size = 0.1,
point.alpha = 0.1,
fits = TRUE,
colours = NULL
)
sces |
named list of SingleCellExperiment objects to combine and compare. |
ref |
string giving the name of the SingleCellExperiment to use as the reference |
point.size |
size of points in scatter plots. |
point.alpha |
opacity of points in scatter plots. |
fits |
whether to include fits in scatter plots. |
colours |
vector of colours to use for each dataset. |
This function aims to look at the differences between a reference SingleCellExperiment and one or more others. It requires each SingleCellExperiment to have the same dimensions. Properties are compared by ranks, for example when comparing the means the values are ordered and the differences between the reference and another dataset plotted. A series of Q-Q plots are also returned.
The returned list has five items:
Reference
The SingleCellExperiment used as the reference.
RowData
Combined feature data from the provided SingleCellExperiments.
ColData
Combined column data from the provided SingleCellExperiments.
Plots
Difference plots
Means
Boxplot of mean differences.
Variances
Boxplot of variance differences.
MeanVar
Scatter plot showing the difference from the reference variance across expression ranks.
LibraeySizes
Boxplot of the library size differences.
ZerosGene
Boxplot of the differences in the percentage of each gene that is zero.
ZerosCell
Boxplot of the differences in the percentage of each cell that is zero.
MeanZeros
Scatter plot showing the difference from the reference percentage of zeros across expression ranks.
QQPlots
Quantile-Quantile plots
Means
Q-Q plot of the means.
Variances
Q-Q plot of the variances.
LibrarySizes
Q-Q plot of the library sizes.
ZerosGene
Q-Q plot of the percentage of zeros per gene.
ZerosCell
Q-Q plot of the percentage of zeros per cell.
The plots returned by this function are created using
ggplot
and are only a sample of the kind of plots you
might like to consider. The data used to create these plots is also returned
and should be in the correct format to allow you to create further plots
using ggplot
.
List containing the combined datasets and plots.
sim1 <- splatSimulate(nGenes = 1000, batchCells = 20)
sim2 <- simpleSimulate(nGenes = 1000, nCells = 20)
difference <- diffSCEs(list(Splat = sim1, Simple = sim2), ref = "Simple")
names(difference)
names(difference$Plots)
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