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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.