Draws a heatmap of single cell feature expression with cells ordered by their mixscape ko probabilities.
MixscapeHeatmap(
object,
ident.1 = NULL,
ident.2 = NULL,
balanced = TRUE,
logfc.threshold = 0.25,
assay = "RNA",
max.genes = 100,
test.use = "wilcox",
max.cells.group = NULL,
order.by.prob = TRUE,
group.by = NULL,
mixscape.class = "mixscape_class",
prtb.type = "KO",
fc.name = "avg_log2FC",
pval.cutoff = 0.05,
...
)A ggplot object.
An object
Identity class to define markers for; pass an object of class
phylo or 'clustertree' to find markers for a node in a cluster tree;
passing 'clustertree' requires BuildClusterTree to have been run
A second identity class for comparison; if NULL,
use all other cells for comparison; if an object of class phylo or
'clustertree' is passed to ident.1, must pass a node to find markers for
Plot an equal number of genes with both groups of cells.
Limit testing to genes which show, on average, at least X-fold difference (log-scale) between the two groups of cells. Default is 0.25 Increasing logfc.threshold speeds up the function, but can miss weaker signals.
Assay to use in differential expression testing
Total number of DE genes to plot.
Denotes which test to use. Available options are:
"wilcox" : Identifies differentially expressed genes between two groups of cells using a Wilcoxon Rank Sum test (default)
"bimod" : Likelihood-ratio test for single cell gene expression, (McDavid et al., Bioinformatics, 2013)
"roc" : Identifies 'markers' of gene expression using ROC analysis. For each gene, evaluates (using AUC) a classifier built on that gene alone, to classify between two groups of cells. An AUC value of 1 means that expression values for this gene alone can perfectly classify the two groupings (i.e. Each of the cells in cells.1 exhibit a higher level than each of the cells in cells.2). An AUC value of 0 also means there is perfect classification, but in the other direction. A value of 0.5 implies that the gene has no predictive power to classify the two groups. Returns a 'predictive power' (abs(AUC-0.5) * 2) ranked matrix of putative differentially expressed genes.
"t" : Identify differentially expressed genes between two groups of cells using the Student's t-test.
"negbinom" : Identifies differentially expressed genes between two groups of cells using a negative binomial generalized linear model. Use only for UMI-based datasets
"poisson" : Identifies differentially expressed genes between two groups of cells using a poisson generalized linear model. Use only for UMI-based datasets
"LR" : Uses a logistic regression framework to determine differentially expressed genes. Constructs a logistic regression model predicting group membership based on each feature individually and compares this to a null model with a likelihood ratio test.
"MAST" : Identifies differentially expressed genes between two groups of cells using a hurdle model tailored to scRNA-seq data. Utilizes the MAST package to run the DE testing.
"DESeq2" : Identifies differentially expressed genes between two groups of cells based on a model using DESeq2 which uses a negative binomial distribution (Love et al, Genome Biology, 2014).This test does not support pre-filtering of genes based on average difference (or percent detection rate) between cell groups. However, genes may be pre-filtered based on their minimum detection rate (min.pct) across both cell groups. To use this method, please install DESeq2, using the instructions at https://bioconductor.org/packages/release/bioc/html/DESeq2.html
Number of cells per identity to plot.
Order cells on heatmap based on their mixscape knockout probability from highest to lowest score.
(Deprecated) Option to split densities based on mixscape classification. Please use mixscape.class instead
metadata column with mixscape classifications.
specify type of CRISPR perturbation expected for labeling mixscape classifications. Default is KO.
Name of the fold change, average difference, or custom function column in the output data.frame. Default is avg_log2FC
P-value cut-off for selection of significantly DE genes.
Arguments passed to other methods and to specific DE methods