Detection method for highly and lowly variable genes
BASiCS_DetectHVG(Data, object, VarThreshold, EviThreshold = NULL,
OrderVariable = "Prob", Plot = FALSE, ...)BASiCS_DetectLVG(Data, object, VarThreshold, EviThreshold = NULL,
OrderVariable = "Prob", Plot = FALSE, ...)
an object of class BASiCS_Data-class
an object of class BASiCS_Chain-class
Variance contribution threshold (must be a positive value, between 0 and 1)
Optional parameter. Evidence threshold (must be a positive value, between 0 and 1)
Ordering variable for output. Must take values in c("GeneIndex", "Mu", "Delta", "Sigma", "Prob")
.
If Plot = T
a plot of the gene specific expression level against HVG or LVG is generated.
Graphical parameters (see par
).
Vector of characters containing gene names (must be in the same order as in the Counts matrix).
BASiCS_DetectHVG
returns a list of 4 elements:
Table
Matrix whose columns contain
Mu
q.bio
. For each biological gene, posterior median of gene-specific expression levels \(\mu[i]\)Delta
q.bio
. For each biological gene, posterior median of gene-specific biological cell-to-cell heterogeneity hyper-parameter \(\delta[i]\)Sigma
q.bio
. For each biological gene, proportion of the total variability that is due to a cell-to-cell biological heterogeneity component. Prob
q.bio
. For each biological gene, probability of being highly variable according to the given thresholds.HVG
q.bio
. For each biological gene, indicator of being detected as highly variable according to the given thresholds. EviThreshold
Evidence threshold.
EFDR
Expected false discovery rate for the given thresholds.
EFNR
Expected false negative rate for the given thresholds.
BASiCS_DetectLVG
produces a similar output, replacing the element HVG
by LVG
, an indicator of a gene being detected as lowly variable according to the given thresholds.
See vignette
Vallejos, Marioni and Richardson (2015). Bayesian Analysis of Single-Cell Sequencing data.
# NOT RUN {
# See
help(BASiCS_MCMC)
# }
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