{"name":"cells","title":"Cell body segmentation","pagetitle":"Cell body segmentation — cells","source":"

Hill, LaPan, Li and Haney (2007). Impact of image segmentation on\nhigh-content screening data quality for SK-BR-3 cells, BMC\nBioinformatics, Vol. 8, pg. 340,\nhttps://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-340.

","filename":"cells.Rd","author":[],"aliases":"cells","keywords":"datasets","description":"

Hill, LaPan, Li and Haney (2007) develop models to predict which cells in a\nhigh content screen were well segmented. The data consists of 119 imaging\nmeasurements on 2019. The original analysis used 1009 for training and 1010\nas a test set (see the column called case).

","opengraph":{"description":"Hill, LaPan, Li and Haney (2007) develop models to predict which cells in a\nhigh content screen were well segmented. The data consists of 119 imaging\nmeasurements on 2019. The original analysis used 1009 for training and 1010\nas a test set (see the column called case)."},"examples":"data(cells)\nstr(cells)\n","sections":[],"value":"

cells: a tibble

","details":"

The outcome class is contained in a factor variable called class with\nlevels \"PS\" for poorly segmented and \"WS\" for well segmented.

The raw data used in the paper can be found at the Biomedcentral website.\nThe version\ncontained in cells is modified. First, several discrete\nversions of some of the predictors (with the suffix \"Status\") were removed.\nSecond, there are several skewed predictors with minimum values of zero\n(that would benefit from some transformation, such as the log). A constant\nvalue of 1 was added to these fields: avg_inten_ch_2,\nfiber_align_2_ch_3, fiber_align_2_ch_4, spot_fiber_count_ch_4 and\ntotal_inten_ch_2.

","package":{"package":"modeldata","version":"1.3.0"}}

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