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  "Title": "Deconvolution for LINCS L1000 Data",
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  "Date": "2017-07-05",
  "Author": "Zhao Li[aut], Peng Yu[aut, cre]",
  "Maintainer": "Zhao Li <lizhao.informatics@gmail.com>",
  "Description": "LINCS L1000 is a high-throughput technology that allows\nthe gene expression measurement in a large number of assays.\nHowever, to fit the measurements of ~1000 genes in the ~500\ncolor channels of LINCS L1000, every two landmark genes are\ndesigned to share a single channel. Thus, a deconvolution step\nis required to infer the expression values of each gene. Any\nerrors in this step can be propagated adversely to the\ndownstream analyses. We present a LINCS L1000 data peak calling\nR package l1kdeconv based on a new outlier detection method and\nan aggregate Gaussian mixture model. Upon the remove of\noutliers and the borrowing information among similar samples,\nl1kdeconv shows more stable and better performance than methods\ncommonly used in LINCS L1000 data deconvolution.",
  "License": "GPL (>= 2)",
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  "Date/Publication": "2017-07-08 03:41:45 UTC",
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