Description Usage Arguments Details Value Author(s) References See Also Examples

Calculates the Cq values of a qPCR experiment within a defined range of cycles. The function can be used to extract Cq values of a chamber based qPCR for conversion into a dPCR experiment. All Cq values are obtained by Second Derivative Maximum or by Cy0 method (Guescini et al. (2008)).

1 2 |

`data` |
a dataframe containing the qPCR data. |

`cyc` |
the column containing the cycle data. Defaults to first column. |

`fluo` |
the column(s) (runs) to be analyzed. If |

`Cq_range` |
is a user defined range of cycles to be used for the determination of the Cq values. |

`model` |
is the model to be used for the analysis for all runs. Defaults
to 'l5' (see |

`SDM` |
if |

`pb` |
if |

The `Cq_range`

for this function an be defined be the user. The default
is to take all amplification curves into consideration. However, under
certain circumstances it is recommended to define a range. For example if
amplifications are positive in early cycle numbers (less than 10).

Approximated second derivative is influenced both by how often interpolation
takes place in each data interval and by the smoothing method used. The user
is encouraged to seek optimal parameters for his data himself. See
`inder`

for details.

The calculation of the Cy0 value (equivalent of Cq) is based on a
five-parameter function. From experience this functions leads to good
fitting and avoids overfitting of critical data sets. Regardless, the user
is recommended to test for the optimal fitting function himself (see
`mselect`

for details).

A data frame with two columns and number of rows equal to the number
of runs analyzed. The column `Cy0`

contains calculated Cy0 values. The
column `in.range`

contains adequate logical constant if given Cy0 value
is in user-defined `Cq_range`

.

Michal Burdukiewicz, Stefan Roediger.

Guescini M, Sisti D, Rocchi MB, Stocchi L & Stocchi V (2008)
*A new real-time PCR method to overcome significant quantitative
inaccuracy due to slight amplification inhibition*. BMC Bioinformatics, 9:
326.

Ruijter JM, Pfaffl MW, Zhao S, et al. (2013) *Evaluation of qPCR curve
analysis methods for reliable biomarker discovery: bias, resolution,
precision, and implications*. Methods, San Diego Calif 59:32–46.

SDM method: `inder`

,
`summary.der`

.

Cy0 method: `mselect`

, `efficiency`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
library(qpcR)
test <- cbind(reps[1L:45, ], reps2[1L:45, 2L:ncol(reps2)], reps3[1L:45,
2L:ncol(reps3)])
# results.dPCR contains a column with the Cy0 values and a column with
# converted values.
Cq.range <- c(20, 30)
ranged <- limit_cq(data = test, cyc = 1, fluo = NULL,
Cq_range = Cq.range, model = l5)
# Same as above, but without Cq.range
no_range <- limit_cq(data = test, cyc = 1, fluo = NULL, model = l5)
# Same as above, but only three columns
no_range234 <- limit_cq(data = test, cyc = 1, fluo = c(2:4), model = l5)
``` |

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