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

Source

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

Value

cells

a tibble

Details

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

The raw data used in the paper can be found at the Biomedcentral website. The version contained in cells is modified. First, several discrete versions of some of the predictors (with the suffix "Status") were removed. Second, there are several skewed predictors with minimum values of zero (that would benefit from some transformation, such as the log). A constant value of 1 was added to these fields: avg_inten_ch_2, fiber_align_2_ch_3, fiber_align_2_ch_4, spot_fiber_count_ch_4 and total_inten_ch_2.