scTenifoldKnk 2.0.0
New features
- Direction of the response.
knockoutDirection() predicts whether each gene goes up or
down after a knockout, from the heat kernel of the WT gene-gene
correlation matrix of log1p(CPM) expression.
scTenifoldKnk() reports it by default
(dr_direction = TRUE, dr_directionT = 5) as
the direction and directionScore columns of
diffRegulation, and as perturbationDirections
in transcriptome-wide mode.
dr_directionRegressLibSize = TRUE (off by default)
regresses log library size out of each gene first, for data sets in
which nearly all genes are predicted down.
- Heat manifold alignment.
hkManifoldAlignment() computes the heat kernel of the WT
network once (heatKernel()) and reads every knockout from
it, so the cost of a transcriptome-wide screen no longer scales with the
number of knockouts. Selected with ma_method = "heat"
(ma_heatT = 10).
perturbationMap() ranks the knockouts
of a transcriptome-wide run by the similarity of their signed profiles
to a reference signature, such as a disease-vs-control comparison of the
same cell type, and draws them on a map.
Changes in behaviour
diffRegulation has eight columns instead of six
(direction, directionScore). Set
dr_direction = FALSE for the 1.x table.- Transcriptome-wide mode (
transcriptomeWide = TRUE) uses
the heat manifold alignment by default, so its distances differ from
1.x. Set ma_method = "manifold" to recover them. Single and
multi-gene knockouts still use the manifold alignment by default and
give the same distances, Z-scores and p-values as 1.1.5.
Documentation
- README rewritten: quality-control recommendations (removal of
ribosomal and mitochondrial genes), use in tissues and disease data, and
the limitations of the predicted direction.
scTenifoldKnk 1.1.5
- The knocked-out genes are left out of the expectation of the
differential regulation test (#45).
scTenifoldKnk 1.1.4
- Distances at the level of floating-point noise are ignored by
dRegulation().
scTenifoldKnk 1.1.3
- Warning when the networks of the requested genes do not fit in
memory (#10).
scTenifoldKnk 1.1.2
- Multi-gene knockout: several genes in
gKO are knocked
out together (#47).
plotKO() enrichment works without attaching enrichR
(#43); data.frame input is rejected (#44).
scTenifoldKnk 1.1.1
seed argument for reproducible results (#48); warning
when the knocked-out genes have no outgoing edges.
scTenifoldKnk 1.1.0
- Transcriptome-wide perturbation mode and Efron’s empirical null for
the differential regulation test.