| Title: | A Data Manipulation and Analysis Package for Calcium Indicator Data |
|---|---|
| Description: | Provides shortcuts in extracting useful data points and summarizing waveform data. It is optimized for speed to work efficiently with large data sets so you can get to the analysis phase more quickly. It also utilizes a user-friendly format for use by both beginners and seasoned R users. |
| Authors: | Andrew Tamalunas |
| Maintainer: | Andrew Tamalunas <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.0 |
| Built: | 2024-11-20 06:33:14 UTC |
| Source: | CRAN |
'averaged_trials' averages values over each time point, across the specified trials
averaged_trials(Dataframe, Trials)averaged_trials(Dataframe, Trials)
Dataframe |
a GCalcium-format data frame or matrix |
Trials |
numbers of trials to be averaged across |
a data frame with observation times and averaged values
### Format data frame df.new <- format_data(GCaMP) ### Plot the average fluorescence signal across trials 1-5 df.1thru5 <- averaged_trials(df.new, 1:5) plot(x = df.1thru5$Time, df.1thru5$Values)### Format data frame df.new <- format_data(GCaMP) ### Plot the average fluorescence signal across trials 1-5 df.1thru5 <- averaged_trials(df.new, 1:5) plot(x = df.1thru5$Time, df.1thru5$Values)
'avg_curve_slope' finds the average curve slopes for a trial using inflect_points_df and lm
avg_curve_slope(Dataframe, Trial)avg_curve_slope(Dataframe, Trial)
Dataframe |
a GCalcium-format data frame or matrix |
Trial |
a single trial number |
vector of average rate of change for each curve
curves of a single value will result in the average slope being labeled as NA
df.new <- format_data(data = GCaMP) avg_curve_slope(Dataframe = df.new, Trial = 1)df.new <- format_data(data = GCaMP) avg_curve_slope(Dataframe = df.new, Trial = 1)
'between_trial_change' finds the difference in means during same time range between sets of trials.
between_trial_change(Dataframe, TrialRange1, TrialRange2, Time.period = c(min(Dataframe[1]), max(Dataframe[1])))between_trial_change(Dataframe, TrialRange1, TrialRange2, Time.period = c(min(Dataframe[1]), max(Dataframe[1])))
Dataframe |
a GCaMP-format data frame or matrix |
TrialRange1 |
range of trial numbers to be compared to second set of trials |
TrialRange2 |
range of trial numbers to be compared to first set of trials |
Time.period |
range of time to be compared between sets of trials |
Number representing mean difference of trial set 2 and trial set 1
# How much did the mean value change 2s after epoc between trials 1-5 and trials 6-10? df.new <- format_data(data = GCaMP) between_trial_change(Dataframe = df.new, TrialRange1 = c(1, 5), TrialRange2 = c(6, 10), Time.period = c(0, 2))# How much did the mean value change 2s after epoc between trials 1-5 and trials 6-10? df.new <- format_data(data = GCaMP) between_trial_change(Dataframe = df.new, TrialRange1 = c(1, 5), TrialRange2 = c(6, 10), Time.period = c(0, 2))
'centered_AUC' computes the area under the curve (AUC) after centering values using a specified function, such as mean or min. Computed using trapezoidal integration.
centered_AUC(Dataframe, Trial, FUN = mean)centered_AUC(Dataframe, Trial, FUN = mean)
Dataframe |
a GCalcium-format data frame or matrix |
Trial |
a single trial number |
FUN |
a function to apply to each window |
Data frame of AUCs and curve number for each curve
# Get AUCs for trial 2, centered at the mean df.new <- format_data(data = GCaMP) centered_AUC(Dataframe = df.new, Trial = 2, FUN = mean)# Get AUCs for trial 2, centered at the mean df.new <- format_data(data = GCaMP) centered_AUC(Dataframe = df.new, Trial = 2, FUN = mean)
'consecutive_trial_change' finds the change in means between consecutive trials.
consecutive_trial_change(Dataframe, Trials, Time.period)consecutive_trial_change(Dataframe, Trials, Time.period)
Dataframe |
a GCaMP-format data frame or matrix |
Trials |
range of trial numbers to be compared |
Time.period |
range of time to be compared |
Data frame with the "Mean.Change" column representing differences in means between trial n and trial n + 1 for the user-inputted range of trials.
### How much did the mean value change after epoc between consecutive trials in trials 1-10? df.new <- format_data(data = GCaMP) consecutive_trial_change(Dataframe = df.new, Trials = c(1, 10), Time.period = c(0, 4))### How much did the mean value change after epoc between consecutive trials in trials 1-10? df.new <- format_data(data = GCaMP) consecutive_trial_change(Dataframe = df.new, Trials = c(1, 10), Time.period = c(0, 4))
‘find_peaks' finds peaks or valleys in waveform by using inflection points, with filter of ’n' increasing/decreasing points on both sides of each inflection point.
find_peaks(x, n.points = 3)find_peaks(x, n.points = 3)
x |
vector of numbers |
n.points |
the number of decreasing (peaks) or increasing (valleys) data points on left and right of inflection point required to be considered a "peak". A positive number as an input finds peaks, and a negative number finds valleys. |
a numeric vector of indices
### Format data frame df.new <- format_data(GCaMP) ### How many peaks are there in trial 1 with 10 decreasing data points on each side? peak.indices <- find_peaks(df.new$Trial1, n.points = 10) ### When do they occur? data.frame(times = df.new$Time[peak.indices], vals = df.new$Trial1[peak.indices])### Format data frame df.new <- format_data(GCaMP) ### How many peaks are there in trial 1 with 10 decreasing data points on each side? peak.indices <- find_peaks(df.new$Trial1, n.points = 10) ### When do they occur? data.frame(times = df.new$Time[peak.indices], vals = df.new$Trial1[peak.indices])
'format_data' changes the format of data from wide time series to long format with labeled columns. If data is already in long format, the function simply re-labels the columns
format_data(data)format_data(data)
data |
A data set with observation times in the first row or column, and observed values from trials in each following row or column. |
Data frame with labeled time and trial columns
df.new <- format_data(GCaMP)df.new <- format_data(GCaMP)
A dataset containing calcium activity and observed times during a behavioral paradigm pilot study; organized into 10 individual trials
data(GCaMP)data(GCaMP)
A data frame with 11 rows and 814 columns
observed times of Trial.n values, in seconds
values of calcium activity signals for trial n
...
Dr. David Root, Maier-Watkins laboratory
'inflect_points' finds inflection points of waveform data
inflect_points(x)inflect_points(x)
x |
A vector of numbers |
Inflection points of the input vector
df.new <- format_data(GCaMP) inflect_points(df.new$Trial1)df.new <- format_data(GCaMP) inflect_points(df.new$Trial1)
'inflect_points_df' finds inflection points of activity waves, returning a summarized data frame
inflect_points_df(Dataframe, Trial)inflect_points_df(Dataframe, Trial)
Dataframe |
a GCalcium-format data frame or matrix |
Trial |
a single trial number |
data frame with variables indicating the time, raw values, curve numbers, and inflection points corresponding to each data point of the input
df.new <- format_data(GCaMP) inflect_points_df(Dataframe = df.new, Trial = 1)df.new <- format_data(GCaMP) inflect_points_df(Dataframe = df.new, Trial = 1)
'moving_window' summarizes data within windows of a certain length.
moving_window(Dataframe, Trial, Window.length, FUN = mean)moving_window(Dataframe, Trial, Window.length, FUN = mean)
Dataframe |
a GCalcium-format data frame or matrix |
Trial |
a single trial number |
Window.length |
length of time each window encompasses |
FUN |
a function to apply to each window |
a data frame with start and stop times of each window, the chronological number of each window, and summarized values
### Format data frame df.new <- format_data(GCaMP) ### In trial 5, how does the average fluorescence change in 1 second time frames? moving_window(Dataframe = df.new, Trial = 5, Window.length = 1, FUN = mean)### Format data frame df.new <- format_data(GCaMP) ### In trial 5, how does the average fluorescence change in 1 second time frames? moving_window(Dataframe = df.new, Trial = 5, Window.length = 1, FUN = mean)
'perc_baseline' calculates the percent change from a user-specified baseline period
perc_baseline(Dataframe, Baseline.times, Baseline.frame = FALSE)perc_baseline(Dataframe, Baseline.times, Baseline.frame = FALSE)
Dataframe |
a GCalcium-format data frame or matrix |
Baseline.times |
range of time from Baseline.frame to compute the baseline value from |
Baseline.frame |
a GCalcium-format data frame or matrix containing the baseline period. If frame is not specified, Dataframe is automatically used |
a GCalcium-format data frame with values transformed to percent baseline
### Format data frame df.new <- format_data(GCaMP) ### Transform into percent baseline: relative to -3s to -1s before epoc perc_baseline(Dataframe = df.new, Baseline.times = c(-3, -1))### Format data frame df.new <- format_data(GCaMP) ### Transform into percent baseline: relative to -3s to -1s before epoc perc_baseline(Dataframe = df.new, Baseline.times = c(-3, -1))
'plot_trials' plots the user-inputted trials. This function automatically adjusts the x- and y- axis limits to fit all data
plot_trials(Dataframe, Trials, ...)plot_trials(Dataframe, Trials, ...)
Dataframe |
a GCalcium-format data frame or matrix |
Trials |
set of trials to be plotted |
... |
extra commands to be called to the blank base plot |
a single plot of user-specified trials
### Format data frame df.new <- format_data(GCaMP) ### Specify and plot trials my.trials <- c(1, 2, 7, 8) plot_trials(Dataframe = df.new, Trials = my.trials)### Format data frame df.new <- format_data(GCaMP) ### Specify and plot trials my.trials <- c(1, 2, 7, 8) plot_trials(Dataframe = df.new, Trials = my.trials)
'within_trial_change' finds the change in mean values between beginning and end of entered time range.
within_trial_change(Dataframe, Trial, Beg.period = FALSE, End.period = FALSE)within_trial_change(Dataframe, Trial, Beg.period = FALSE, End.period = FALSE)
Dataframe |
a GCalcium-format data frame or matrix |
Trial |
a single trial number |
Beg.period |
time frame of period 1 to be compared to period 2 |
End.period |
time frame of period 2 to be compared to period 1 |
vector with AUC for each curve in a trial
# In trial 1, how much did the mean value change between -2s and 0s, and 0 and 2s? df.new <- format_data(data = GCaMP) within_trial_change(Dataframe = df.new, Trial = 1, Beg.period = c(-2, 0), End.period = c(0, 2))# In trial 1, how much did the mean value change between -2s and 0s, and 0 and 2s? df.new <- format_data(data = GCaMP) within_trial_change(Dataframe = df.new, Trial = 1, Beg.period = c(-2, 0), End.period = c(0, 2))
'z_score' transforms input values to z scores. Allows user input of mu and sigma values for comparing distributions.
z_score(xvals, mu = FALSE, sigma = FALSE)z_score(xvals, mu = FALSE, sigma = FALSE)
xvals |
vector of numbers |
mu |
the population mean |
sigma |
the population standard deviation |
a numeric vector of z scores
# Format data df.new <- format_data(GCaMP) # Split data basevals <- df.new$Trial1[df.new$Time <= 0] eventvals <- df.new$Trial1[df.new$Time > 0] # Find baseline (pre-epoc) values base.mu <- mean(basevals) base.sigma <- sd(basevals) # Compute values z_score(x = eventvals, mu = base.mu, sigma = base.sigma)# Format data df.new <- format_data(GCaMP) # Split data basevals <- df.new$Trial1[df.new$Time <= 0] eventvals <- df.new$Trial1[df.new$Time > 0] # Find baseline (pre-epoc) values base.mu <- mean(basevals) base.sigma <- sd(basevals) # Compute values z_score(x = eventvals, mu = base.mu, sigma = base.sigma)