BASiCS_DetectHVG {BASiCS} | R Documentation |
Functions to detect highly and lowly variable genes
BASiCS_DetectHVG(Chain, VarThreshold, ProbThreshold = NULL, EFDR = 0.1, OrderVariable = "Prob", Plot = FALSE, ...) BASiCS_DetectLVG(Chain, VarThreshold, ProbThreshold = NULL, EFDR = 0.1, OrderVariable = "Prob", Plot = FALSE, ...)
Chain |
an object of class |
VarThreshold |
Variance contribution threshold (must be a positive value, between 0 and 1) |
ProbThreshold |
Optional parameter. Posterior probability threshold (must be a positive value, between 0 and 1) |
EFDR |
Target for expected false discovery rate related to HVG/LVG detection (default = 0.10) |
OrderVariable |
Ordering variable for output.
Possible values: |
Plot |
If |
... |
Graphical parameters (see |
See vignette
BASiCS_DetectHVG
returns a list of 4 elements:
Table
Matrix whose columns contain
GeneIndex
Vector of length q.bio
.
Gene index as in the order present in the analysed
SingleCellExperiment
GeneName
Vector of length q.bio
.
Gene name as in the order present in the analysed
SingleCellExperiment
Mu
Vector of length q.bio
. For each biological gene,
posterior median of gene-specific mean expression
parameters μ_i
Delta
Vector of length q.bio
. For each biological
gene, posterior median of gene-specific biological
over-dispersion parameter δ_i
Sigma
Vector of length q.bio
.
For each biological gene, proportion of the total variability
that is due to a biological heterogeneity component.
Prob
Vector of length q.bio
.
For each biological gene, probability of being highly variable
according to the given thresholds.
HVG
Vector of length q.bio
.
For each biological gene, indicator of being detected as highly
variable according to the given thresholds.
ProbThreshold
Posterior probability 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 column HVG
by
LVG
, an indicator of a gene being detected as
lowly variable according to the given thresholds.
Catalina A. Vallejos cnvallej@uc.cl
Vallejos, Marioni and Richardson (2015). PLoS Computational Biology.
# See help(BASiCS_MCMC)