Auto-qPCR

NeuroEDDU

All inputs are case insensitive. Separate with commas when multiple inputs are required and do not leave spaces.

* fields are required


Select model

Choose your model based on your qPCR design.


Upload your data (results.csv or results.txt):

Upload your data: Navigate to your folder of raw data files * This must be a either a csv (comma separated file) or a txt (tab separated file). These should be the output files from you PCR machine. Multiple spread sheets can be included. The data will be combined into a single analysis. Be sure to include the control genes you will use to normalize.


File information

This is only needed if you have an irregular file or you wish to filter out a quencher. If your file contains no target you can indicate the target (gene) names in the file names and input these here. You can also choose to filter out your quencher or probe.

Would you like to fill in file information?

If your dataframe doesn't contain the gene target names you must include the gene name in the file name. Enter the gene names in as they are found in the file name.

Enter the name of the quencher that will be filtered out.

This is the "Task" or "Content" file and is by default listed as UNKNOWN. Could be "Sample" or "Unk. If you enter a partial work the column will be searched for anything containing the letters."


Normalization options

Gene or region to to normalize with (Actin, GAPDH, CHR4, B2M). Must be entered identically to the input spreadsheet.
The sample used as calibrator (compare with this sample) for the relative (ΔΔCT) and for the instability models.


Options for removing technical replicates

Technical replicates for a given sample will be removed if the CT-SD of a given sample is above the threshold (default 0.3).
Max proportion of replicates removed to reach the CT-SD cut off. Default of 0.5 means that in 3 replicates only one can be removed.
Preserve outliers if the sample has high variability


Visualization options

Enter the order you would like genes/chromosomes to appear on the plots. Use the exact/complete names.
Enter the order you would like cell lines/conditions/timepoints to appear on the plots. Use the exact/complete names.


Statistics

Based on your selections the appropriate statistic will be performed. For 2 groups: paired or unpaired t-tests for normally distributed data, non-paramentric equivalent MannWhitneyU tests are used. For more than 2 groups one way ANOVA (normal distributions) or Kruskal-Wallis independent measures) or Friedman test (repeated measures) will be used. Post hoc comparisons are performed on 3 or more groups.

Would you like to do statistical analysis?

Column names that indicate grouping of the data (e.g. treatments, time points). Do not enter column names for sample names and target names. If your sample name contains the group variable names, leave this field empty.

Groups can be different genes, time points, animals, treatments or any conditions you wish to compare. Must be entered as they appear in the spreadsheet.


Identify your groups (variables)

Where are your groups?

Select which column will have your group names (the levels of the variable to compare). If you added the groups in your sample names select sample names. Otherwise you must have a column with the group names. If you have not indicated the groups/variables in your raw data you will not be able to do statistics with this module.

How many variables do you want to compare?

Select one variable for a one-way anova. For example you have three doses of a drug, this is one variable "Dose of Drug". If you have two genotypes and three doses of a drug, this is two variables "Genotype" and "Dose of Drug".


Select Parameters

Type of tests

Parametric test should be used on samples with a normal distribution. Tests for normality require more than the usual number of biological replicates used in a qPCR experiment. The users knowledge of the data type will need to suffice.

Type of measurements

Dependent measurements are measurements from the same sample or cell line. Using this option reduces the effect of biological variation that is masking the effect that is being measured. However, this cannot be used if the measurements are independed.


Plots will be automatically generated.