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  "Title": "Analyze High-Dimensional High-Throughput Dataset and Quality\nControl Single-Cell RNA-Seq",
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  "Date": "2025-09-24",
  "Authors@R": "c(\nperson(given = \"Yajing\", family = \"Hao\", email = \"yahao@health.ucsd.edu\", role  = c(\"aut\"), comment = c(ORCID = \"0000-0003-1384-4176\")),\nperson(given = \"Shuyang\", family = \"Zhang\", email = \"s4zhang@ucsd.edu\", role  = \"ctb\", comment=c(ORCID=\"0000-0002-8428-1828\")),\nperson(given = \"Junhui\", family = \"Li\", email = \"ljh.biostat@gmail.com\", role = \"cre\", comment=c(ORCID=\"0000-0003-3973-1700\")),\nperson(given = \"Guofeng\", family = \"Zhao\", email = \"gzhao@health.ucsd.edu\", role = \"ctb\"),\nperson(given = \"Xiang-Dong\", family = \"Fu\", email = \"xdfu@health.ucsd.edu\", role = c(\"cph\",\"fnd\"), comment = c(ORCID = \"0000-0001-5499-8732\"))\n)",
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  "Description": "The advent of genomic technologies has enabled the\ngeneration of two-dimensional or even multi-dimensional\nhigh-throughput data, e.g., monitoring multiple changes in gene\nexpression in genome-wide siRNA screens across many different\ncell types (E Robert McDonald 3rd (2017) <doi:\n10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi:\n10.1016/j.cell.2017.06.010>) or single cell transcriptomics\nunder different experimental conditions. We found that simple\ncomputational methods based on a single statistical criterion\nis no longer adequate for analyzing such multi-dimensional\ndata. We herein introduce 'ZetaSuite', a statistical package\ninitially designed to score hits from two-dimensional RNAi\nscreens.We also illustrate a unique utility of 'ZetaSuite' in\nanalyzing single cell transcriptomics to differentiate rare\ncells from damaged ones (Vento-Tormo R (2018) <doi:\n10.1038/s41586-018-0698-6>). In 'ZetaSuite', we have the\nfollowing steps: QC of input datasets, normalization using\nZ-transformation, Zeta score calculation and hits selection\nbased on defined Screen Strength.",
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