A Data Manipulation and Analysis Package for Calcium Indicator Data.

GCalcium

Calcium imaging methods produce massive datasets that require immense data manipulation, exploration, and summarizing. GCalcium provides highly-accessible functions to address these issues for both inexperienced and seasoned R users to save researchers time. This package is catered to calcium imaging data, but works with any type of waveform data. A few functions include:

• format_data converts a data frame or matrix to a GCalcium-friendly format
• avg_curve_slope gets the average slope of curves for a trial
• between_trial_change finds the difference in mean activity between trials
• centered_AUC finds the area under each curve
• moving_window summarizes data within windows of time

The "Examples" vignette explains all functions in greater detail.

Installation

### Install from CRAN repository
install.packages("GCalcium")

Getting started

Since there is currently no ubiquitous way to analyze or format calcium imaging data, most of GCalcium's commands require the data frame to be in "GCalcium format." This is essentially a time series data frame; where the times of recorded signals are in the first row or column, and the observed values of each trial are in the following rows or columns.

### Format data
df.new <- format_data(GCaMP)

### What is the average slope for each curve in trial 1?
cat( avg_curve_slope(Dataframe = df.new, Trial = 1) )
#> NA 2.686828 -5.232394 3.079274 -4.75037 5.301835 -3.920928 -0.1513382 -2.021098 0.1617518 -0.3044619 6.910018 -2.222297 3.505145 -4.423766 6.068415 -3.884568 5.948769 -0.6739143 3.88837 -4.38267 NA 1.690167 -3.259148 1.04693 -1.352179 3.079839 -1.26231 2.280547 -2.764681 2.086012 -2.176606 5.576817 0.9183673 7.01988 -2.683797 2.563535 -4.106713 -1.010101 -5.764288 4.887615 -0.8646999 7.591648 -9.51594 1.778811 -1.007121 4.799507 0.1424286 1.088477 -5.46543 5.243424 -4.167272 3.803906 -0.6307223 0.8850458 -5.676258 -0.2339776 -2.8535 7.08697 -1.74929 -0.508647 -7.278496 3.408955 -6.524187 3.929158 -4.112074 1.066656 -4.287113 4.406864 NA -3.244568 2.916868 -0.5786535 4.053837 -11.21297 5.769475 0.8163265 4.744986 -3.187327 4.494263 -5.132678 10.89168 -10.5337 11.71813 -6.204929 6.66565 -4.533736 4.002959 -3.571172 4.214516 -6.47418 8.999957 -8.60198 9.384561 -5.144969

### How does activity of each curve differ from the average in trial 1?
head( centered_AUC(Dataframe = df.new, Trial = 1, FUN = mean) )
#>   Curve.Num          AUC
#> 1         1           NA
#> 2         2 0.0102761563
#> 3         3 0.0158426888
#> 4         4 0.0142982157
#> 5         5 0.0168281125
#> 6         6 0.0001885507

### What is the average activity for trial 1 in 0.5 second intervals?
head ( moving_window(Dataframe = df.new, Trial = 1, Window.length = 0.5, FUN = mean) )
#>   Summary.vals Start.time Stop.time Window.num
#> 1     82.58849    -3.9902   -3.4902          1
#> 2     82.57888    -3.4902   -2.9902          2
#> 3     82.38037    -2.9902   -2.4902          3
#> 4     82.66410    -2.4902   -1.9902          4
#> 5     82.59971    -1.9902   -1.4902          5
#> 6     82.77696    -1.4902   -0.9902          6

### How much does activity differ in the first second of trials 1 & 2, and 3 & 4?
head( between_trial_change(Dataframe = df.new, TrialRange1 = 1:2, TrialRange2 = 3:4, 
                           Time.period = c(0, 1)) )
#> [1] 3.778916