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").
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.get_energetics_summary(),
get_rate_summary(), bioscope_plot(),
atp_plot(), and rate_plot().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.rate_plot() now has a linewidth parameter
to set the width of its geom_linesSeparating 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.
get_energetics()read_data() returns the replicate column
as a factor instead of numericgeom_line’s deprecated size option
with linewidth in rate_plotAdd 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 |
read_data() to support delimiters other than First release before initial submission for publication.