ceas 1.3.0

• normalize() can now normalize by each well (norm_column = "well") as an alternative to the existing normalization by experimental group (norm_column = "exp_group")

• normalize() has two normalization methods.

  • norm_method = "self": based on the corresponding well or experimental group row of the measure column in the input normalization CSV.

    exp_group	measure
    Group_1	30000
    Group_2	30000
    Group_3	5000
    Group_4	5000

    Given the input normalization data above, normalizing by experimental group will divide each of those experimental group rows of the seahorse table by the corresponding measure value of the experimental group in the input CSV. Similarly, if normalizing by well, each set of well rows is normalized by the corresponding measure value of the well - the input normalization CSV must have a column for well instead of exp_group for every well in the Seahorse data.

  • based on the minimum of the measure column of the input normalization data (norm_method = "minimum") (same as before). A normalization constant is calculated dividing each well or experimental group measure by the minimum measure.

    exp_group	measure	norm_const
    Group_1	30000	6
    Group_2	30000	6
    Group_3	5000	1
    Group_4	5000	1

    If normalizing by experimental group, each row of the seahorse table is divide by the group’s normalization constant. Similarly, if normalizing by well, each well row is divided by the well’s normalization constant.

  Note: the current default is to normalize by experimental group and using the minimum (norm_column = "exp_group", norm_method = "minimum") to maintain backwards compatibility, but future releases will normalize by well and using each corresponding row (norm_column = "well", norm_method = "self").

ceas 1.2.1

• read_data() throws an error if the “Group” column of the input data is only one word that cannot be separated with the delimiter provided by the user.

ceas 1.2.0

• Linear mixed-effects models can now be used to get mean and confidence intervals for datasets with replicates. ATP production, OCR and ECAR are modelled as the response with experimental group as a fixed effect and replicate as a random effect. Mixed-effects modelling is supported by get_energetics_summary(), get_rate_summary(), bioscope_plot(), atp_plot(), and rate_plot().

ceas 1.1.2

• get_energetics() now warns about possible mismatches between the replicates in the MITO and GLYCO groups instead of stopping as datasets with different replicate counts can cause a mismatch that may not be erroneous.

ceas 1.1.1

• rate_plot() now has a linewidth parameter to set the width of its geom_lines

ceas 1.1.0

• Separating replicates is now supported for getting get_energetics_summary(), bioscope_plot(), atp_plot() and rate_plot() with sep_reps = TRUE. This will calculate summary statistics for each replicate within a group instead of combining them. atp_plot() now uses a linerange plot instead of a crossbar plot and color to distinguish between replicates instead of experimental groups. There is no color if there are no replicates or if they are combined.

  Note: the current default is to combine replicates (sep_reps = FALSE) to maintain backwards compatibility, but future releases will separate them by default. If sep_reps is not explicitly set to FALSE, the functions will warn the user about this future change in defaults.

ceas 1.0.3

• Preserve the replicate column when returning energetics from get_energetics()

ceas 1.0.2

• read_data() returns the replicate column as a factor instead of numeric

ceas 1.0.1

• Replace geom_line’s deprecated size option with linewidth in rate_plot

ceas 1.0.0

• First CRAN release.

ceas 0.3.3

• Don’t divide by 0 when normalizing if already 0; PER is now normalized with OCR and ECAR.

ceas 0.3.2

• Warn if data are not normalized by checking that the “Background” group’s OCR, ECAR, and PER values are 0.

ceas 0.3.1

• Add assay type as argument to plot ECAR/OCR meaningfully as all assay types were previously being summarized instead of just one.

ceas 0.3.0

• Add normalize(), a cell count/protein mass normalization function. read_data now can take a csv file with cell counts or protein mass (\(\mu\)g) for each of the experimental groups to normalize the data. An example csv is provided below for a dataset with 4 experimental groups:

  exp_group	measure
  Group_1	30000
  Group_2	30000
  Group_3	5000
  Group_4	5000

ceas 0.2.0

• Add delimiter between group and assay labels as argument in read_data() to support delimiters other than .

ceas 0.1.1

• Fix the calculation of coupled mitochondrial respiration (#4). This correction was published in https://doi.org/10.1074/jbc.AAC118.004855.

ceas 0.1.0

First release before initial submission for publication.