QuantCompare

This script is designed for calculating quantitative ratios between groups of TMT reporter ion intensities.

Install

From Pip

pip install quantcompare

How to Run

The main script is located at quantcompare.main:run, but the 'compare' entry point is provided by the package.

compare results.sage.parquet output --pairs 1,2;1,3;1,4 --groups 1:0,1,2;2:3,4,5;3:6,7,8;4:9,10

Input File (results.sage.parquet)

Required Columns

Arguments

Basic Steps

1. Read Input File
2. Generate QuantGroup's (dataclass to store group_name, group_indecies, and the psm info)
3. Group by PSMs (peptide, charge, Optional[filename])
4. Group by Peptides (peptide, Optional[filename])
5. Filter QuantGroup's based on --filter option
6. Group by Proteins (proteins, Optional[filename])

Grouping Steps

1. Sort QuantGroups based on grouping criteria.
2. For each group of QuantGroups, create a dict mapping groupname to a list of QuantGroups.
3. Loop over the pairs of groups (specified by pairs argument)
4. Create a GroupRatio object for each pair (dataclass which stores the list of QuantGroups for each groupname of the pair)
5. Calculate the qvalue for all GroupRatios. The GroupRatio dataclass has properties for pvalue, ratio.... ect, but not for qvalue since this requires knowledge of all GroupRatios.

Ratio Calculation (Psudo Code)

This method was chosen since one off groups (groups which contain only one datapoint) cannot generate a pvalue since the degrees of freedom would be 0.

The following values are not accurate at all.

grp1 = [[100, 110, 105], [250, 245, 255]]
grp2 = [[200, 210, 205], [500, 490, 0]]

1) Calculate the mean intensity of the reference channel (first groupname in pairs)

grp1_mean = mean(grp1)
grp1_mean = [100, 250]

2) Divide the reference mean intensity by the other groups reporter ions. 

ratios = grp1_mean / grp2 
ratios = [[100/200, 100/210, 100/205], [250/500, 250/490, 250/0]]
ratios = [[~0.5, ~0.45, ~0.47], [~0.5, ~0.55, inf]]

3) Flatten the array, and Remove NANs (caused by 0/0)
ratios = flatten(ratios)
ratios = [~0.5, ~0.45, ~0.47, ~0.5, ~0.55, ~inf]

ratios = remove_nan(ratios)
ratios = [~0.5, ~0.45, ~0.47, ~0.5, ~0.55, ~inf]

4) Calcualte the log2 ratios, and replace -inf and +inf with the inf_replacement value, default is 100)

log2_ratios = log2(ratios)
log2_ratios = [~-2, ~-2, ~-2, ~-2, ~-2, ~-2, inf]

log2_ratios = replace_inf(log2_ratios)
log2_ratios = [~-2, ~-2, ~-2, ~-2, ~-2, ~-2, 100]

5) Calculate the mean and std

log2_ratio = mean(log2_ratios)
log2_ratio = ~14
log2_ratio_std = std(log2_ratios)
log2_ratio_std = ~5

6) Calculate the p-value for a one-sample t-test comparing the sample mean to 0.

log2_ratio_pvalue = pvalue(log2_ratio, log2_ratio_std, len(grp1))
log2_ratio_pvalue = ~0.50