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  "Package": "scTenifoldKnk",
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  "Title": "In-Silico Knockout Experiments from Single-Cell Gene Regulatory\nNetworks",
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  "Authors@R": "c(person(given = \"Daniel\", family = \"Osorio\", email = \"dcosorioh@gmail.com\", role = c(\"aut\",\"cre\"), comment = c(ORCID = '0000-0003-4424-8422')),\nperson(given = \"Yan\", family = \"Zhong\", role = c(\"aut\",\"ctb\")),\nperson(given = \"Guanxun\", family = \"Li\", role = c(\"aut\",\"ctb\")),\nperson(given = \"Qian\", family = \"Xu\", role = c(\"aut\",\"ctb\")),\nperson(given = \"Yongjian\", family = \"Yang\", role = c(\"aut\",\"ctb\")),\nperson(given = \"Yanan\", family = \"Tian\", role = c(\"aut\",\"ctb\")),\nperson(given = \"Robert\", family = \"Chapkin\", role = c(\"aut\",\"ctb\")),\nperson(given = \"Jianhua\", family = \"Huang\", role = c(\"aut\",\"ctb\")),\nperson(given = \"James J.\", family = \"Cai\", role = c(\"aut\", \"ctb\", \"ths\"), comment = c(ORCID = '0000-0002-8081-6725'))\n)",
  "Description": "A workflow based on 'scTenifoldNet' to perform in-silico\nknockout experiments using single-cell RNA sequencing\n(scRNA-seq) data from wild-type (WT) control samples as input.\nFirst, the package constructs a single-cell gene regulatory\nnetwork (scGRN) and knocks out a target gene from the adjacency\nmatrix of the WT scGRN by setting the gene’s outdegree edges to\nzero. Then, it compares the knocked out scGRN with the WT scGRN\nto identify differentially regulated genes, called\nvirtual-knockout perturbed genes, which are used to assess the\nimpact of the gene knockout and reveal the gene’s function in\nthe analyzed cells. It also predicts the direction (up or down)\nof the response of each gene from the WT expression, and reads\nall knockouts of a network from a single heat kernel, which\nmakes transcriptome-wide knockout screens practical.",
  "URL": "https://github.com/cailab-tamu/scTenifoldKnk",
  "BugReports": "https://github.com/cailab-tamu/scTenifoldKnk/issues",
  "License": "GPL (>=2)",
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  "Repository": "https://cailab-tamu.r-universe.dev",
  "Date/Publication": "2026-10-08 13:09:18 UTC",
  "RemoteUrl": "https://github.com/cailab-tamu/sctenifoldknk",
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  "Author": "Daniel Osorio [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4424-8422>),\nYan Zhong [aut, ctb],\nGuanxun Li [aut, ctb],\nQian Xu [aut, ctb],\nYongjian Yang [aut, ctb],\nYanan Tian [aut, ctb],\nRobert Chapkin [aut, ctb],\nJianhua Huang [aut, ctb],\nJames J. Cai [aut, ctb, ths] (ORCID:\n<https://orcid.org/0000-0002-8081-6725>)",
  "Maintainer": "Daniel Osorio <dcosorioh@gmail.com>",
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  "_published": "2026-10-08T17:34:50.670Z",
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  "_commit": {
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    "message": "scTenifoldKnk 2.0.0: direction of the response, heat manifold alignment and perturbationMap() (#51)\n\n* Add heat manifold alignment, knockout direction and QC best practices\n\n- heatKernel(): spectral heat kernel of a gene-gene matrix.\n- heatManifoldAlignment(): kernel counterpart of the manifold alignment;\n  the heat kernel of the WT network is computed once and every knockout\n  is read from it.\n- knockoutDirection(): predicted up/down response of each gene from the\n  heat kernel of the WT gene-gene correlation matrix.\n- scTenifoldKnk(): ma_method (\"manifold\"/\"heat\"; heat by default when\n  transcriptomeWide = TRUE), ma_heatT, dr_direction, dr_directionT;\n  transcriptome-wide output gains perturbationDirections.\n- dRegulation(): optional direction argument adds the direction and\n  directionScore columns; existing columns are unchanged.\n- README: QC best practices (ribosomal/mitochondrial gene removal,\n  ~3,000 HVGs, mitochondrial filter, seeds), the two comparison methods,\n  and the benchmarked direction with its limitations.\n- Tests for the new functions and outputs; version 1.2.0.\n\n* Rename heatManifoldAlignment() to hkManifoldAlignment()\n\n* README: guidance for applying virtual knockouts to tissues and disease data\n\n* README: drop the HVG and mitochondrial-filter recommendations\n\n* README and docs: describe the evaluation qualitatively until it is published\n\nRemove benchmark-specific numbers that cannot be referenced yet; a citation\nwill be added when the evaluation is available.\n\n* README: replace em dashes in the lists this branch edits with colons\n\n* Optional library-size regression for the predicted direction\n\nknockoutDirection(regressLibSize) and scTenifoldKnk(dr_directionRegressLibSize)\nregress log library size out of each gene before the correlations, removing the\nsequencing-depth axis that can make nearly every gene predicted down. Off by default.\n\n* Add perturbationMap()\n\nRanks the knockouts of a transcriptome-wide run by the similarity of their signed\npro",
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      "title": "Evaluates gene differential regulation based on manifold alignment distances.",
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