# proliferationFitting: Estimate proliferation in cell-tracking dye studies In flowFit: Estimate proliferation in cell-tracking dye studies

## Description

The algorithm fit a set of N peaks on the flowFrame data using the nls.lm function. The number of peaks to be fitted is automatically estimated using generationsDistance.

The algorithm take the position (μ) and size (σ) of the Parent Population as estimates and fit a set of peaks on a flowFrame data.

The first peak correspond to the parent population:

a^2\exp\frac{(x - μ)^2}{2σ^2}

The next peak (corresponding to the next generation of cells) will be:

b^2\exp\frac{(x - (μ-D))^2}{2σ^2}

Where D is the estimated distance between 2 generations of cells.

The complete formula for the fitting of the 15 peaks is the following:

a^2\exp\frac{(x - M)^2}{2s^2} + b^2\exp\frac{(x - (M-D))^2}{2s^2} + ... + p^2\exp\frac{(x - (M-14 \cdot D))^2}{2s^2}

Where the parameters [a-q] represent an estimate of the number of cells for a given generation.

In the Levenberg-Marquadt algorithm implementation we use this formula to estimate the error between the model and the real data:

residFun = (Observed - Model)^2

The ration between the intergral of a single peak and the integral of all model formula is an estimate of the percentage of cells in a given generation.

## Usage

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 proliferationFitting( flowframe, channel, estimatedParentPosition, estimatedParentSize, dataRange = NA, logDecades = NA, estimatedDistance = NA, binning = TRUE, breaks = 1024, dataSmooth = TRUE, smoothWindow = 2, fixedModel = FALSE, fixedPars = NA, verbose = FALSE ) 

## Arguments

 flowframe An object of class flowFrame from flowCore channel FACS column/channel (flowFrame column) estimatedParentPosition Estimated parent peak position. estimatedParentSize Estimated parent peak size. dataRange Number of digital data points on the machine. If not provided will be extracted from flowFrame using keyword logDecades FACS dynamic range (log decades). If not provided will be extracted from flowFrame using keyword estimatedDistance Estimated distance between generations. If not provided will be estimated with generationsDistance binning Should I bin data? Some FACS have a large data range (Es: FACSCanto have 65536 data points, may be is convenient in this case to group data in bins to avoid acquiring too many cells). If you have you data log tranformed in range 0-5 it is mandatory to bin data breaks How many breaks if I bin data? dataSmooth Should I smooth data with a Kolmogorov-Zurbenko low-pass linear filter (kz)? smoothWindow Window used to smooth data with the Kolmogorov-Zurbenko low-pass linear filter (kz). fixedModel Should I use a model with fixed parameters? (Peak Position or Size). fixedPars A list of fixed parameters. If you give me a value, I use that value, otherwise I use estimates (check examples) verbose Verbose mode.

## Details

See the vignette for more details on this function.

## Value

return a proliferationFittingData object

Davide Rambaldi

## References

1. Timur V. Elzhov, Katharine M. Mullen and Ben Bolker (2012). minpack.lm: R interface to the Levenberg-Marquardt nonlinear least-squares algorithm found in MINPACK.

## 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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82  if(require(flowFitExampleData)){ # PKH26 data(PKH26data) parent.fitting <- parentFitting(PKH26data[], "FL2-Height LOG") my.fit <- proliferationFitting(PKH26data[], "FL2-Height LOG", parent.fitting@parentPeakPosition, parent.fitting@parentPeakSize) my.fit summary(my.fit) confint(my.fit) coef(my.fit) Data(my.fit) # plot results plot(my.fit) # modeling with locked Peak Size my.fit <- proliferationFitting(PKH26data[], "FL2-Height LOG", parent.fitting@parentPeakPosition, parent.fitting@parentPeakSize, fixedModel=TRUE, fixedPars=list(S=16)) # modeling with locked Peak Size and Position my.fit <- proliferationFitting(PKH26data[], "FL2-Height LOG", parent.fitting@parentPeakPosition, parent.fitting@parentPeakSize, fixedModel=TRUE, fixedPars=list(S=16, M=810)) # modeling with locked Peak Size, Position and Distance my.fit <- proliferationFitting(PKH26data[], "FL2-Height LOG", parent.fitting@parentPeakPosition, parent.fitting@parentPeakSize, fixedModel=TRUE, fixedPars=list(S=16, M=810, D=76)) # generations as vector my.fit@generations # generations as list getGenerations(my.fit) # CFSE, CPD and CTV data data(QuahAndParish) parent.fitting.cfse <- parentFitting(QuahAndParish[], "") fitting.cfse <- proliferationFitting(QuahAndParish[], "", parent.fitting.cfse@parentPeakPosition, parent.fitting.cfse@parentPeakSize) summary(fitting.cfse) confint(fitting.cfse) coef(fitting.cfse) Data(fitting.cfse) plot(parent.fitting.cfse) plot(fitting.cfse) # for CPD samples we use a Fixed Model: we keep fixed in the model the Parent Peak Position parent.fitting.cpd <- parentFitting(QuahAndParish[], "") fitting.cpd <- proliferationFitting(QuahAndParish[], "", parent.fitting.cpd@parentPeakPosition, parent.fitting.cpd@parentPeakSize, fixedModel=TRUE, fixedPars=list(M=parent.fitting.cpd@parentPeakPosition)) parent.fitting.ctv <- parentFitting(QuahAndParish[], "") fitting.ctv <- proliferationFitting(QuahAndParish[], "", parent.fitting.ctv@parentPeakPosition, parent.fitting.ctv@parentPeakSize) # let's compare the generations across the 3 samples: plot(parent.fitting.cfse, main="CFSE Non Stimulated") plot(fitting.cfse, which=3, main="CFSE") plot(fitting.cfse, which=4, main="CFSE") plot(fitting.cfse, which=5, main="CFSE") plot(parent.fitting.cpd, main="CPD Non Stimulated") plot(fitting.cpd, which=3, main="CPD") plot(fitting.cpd, which=4, main="CPD") plot(fitting.cpd, which=5, main="CPD") plot(parent.fitting.ctv, main="CTV Non Stimulated") plot(fitting.ctv, which=3, main="CTV") plot(fitting.ctv, which=4, main="CTV") plot(fitting.ctv, which=5, main="CTV") } 

flowFit documentation built on April 28, 2020, 9:04 p.m.