# Summarize Growth Curves

### Description

This function finds the parameters that describe the input data's growth. It does so by fitting the logistic curve to your growth curve measurements.

### Usage

1 2 | ```
SummarizeGrowth(data_t, data_n, t_trim = 0, bg_correct = "min",
blank = NA)
``` |

### Arguments

`data_t` |
A vector of timepoints (data_n must also be provided and be the same length). |

`data_n` |
A vector of cell counts or absorbance readings. |

`t_trim` |
Measurements taken after this time should not be included in fitting the curve. If stationary phase is variable, this may give you a better fit. A value of 0 means no trimming. Defaults to no trimming (0). |

`bg_correct` |
The background correction method to use. No background correction is performed for the default "none". Specifying "min" subtracts the smallest value in a column from all the rows in that column, and specifying "blank" subtracts the values from the blank vector from the data_n vector. |

`blank` |
A vector of absorbance readings from a blank well (typically contains only media) used for background correction. The corresponding blank value is subtracted from the data_n vector for each timepoint. Defaults to NA. |

### Details

The logistic curve equation is

*N_t = N_0 K / (N_0 + (K - N_0) exp(-rt))*

where *N_t* is the number
of cells (or the absorbance reading) at time t, *N_0* is the initial
cell count (or absorbance reading), K is the carrying capacity, and r is the
growth rate.

The fitness proxies returned are the parameters of the logistic equation
and the area under the curve (a measure that integrates the effects
of *N_0*, K, and r). See `gcfit`

for more documentation on these.

### Value

An object of type gcfit containing the "fitness" proxies, as well as the input data and the fitted model.

### See Also

See the accompanying Vignette for an example of how to use and interpret SummarizeGrowth. bit.ly/1p7w6dJ.

See also `gcfit`

.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
# We can check that the parameters that are found are the same
# as we use to generate fake experimental data. To do so, let's first
# generate the "experimental" data using the logistic equation,
# e.g., absorbance readings from a single well in a plate reader over time.
k_in <- 0.5 # the initial carrying capacity
n0_in <- 1e-5 # the initial absorbance reading
r_in <- 1.2 # the initial growth rate
N <- 50 # the number of "measurements" collected during the growth
# curve experiment
data_t <- 0:N * 24 / N # the times the measurements were made (in hours)
data_n <- NAtT(k = k_in, n0 = n0_in, r = r_in, t = data_t) # the measurements
# Now summarize the "experimental" growth data that we just generated
gc <- SummarizeGrowth(data_t, data_n)
# Get the possible metrics for fitness proxies
gc$vals$r # growth rate is a common choice for fitness
gc$vals$t_gen # doubling time, or generation time, is also common
gc$vals$k
gc$vals$n0
gc$vals$auc_l
gc$vals$auc_e
gc$vals$t_mid
# Compare the data with the fit visually by plotting it
plot(gc)
``` |