This module is part of DIMet: Differential analysis of Isotope-labeled targeted Metabolomics data (https://pypi.org/project/DIMet/).

Input data files

This tool performs a time course differential analysis on your time series data. For illustration see the section Metadata File Information which contains several time points.

This time course differential analysis is sequential: by each individual condition, a comparison between the timepoints t_x+1 vs t_x (e.g. [Control, 90min] vs [Control, 60min]), for all the timepoints present in the data. Our tool automatically detects the conditions and timepoints, and automatically organizes the comparisons (you do not need to set this part yourself, DIMet does it for you).

Note that if you need only to compare specific [condition, timepoint] pairs not comprised by our automatic time course analysis, you can use the differential analysis in the pairwise mode instead.

This tool requires (at max.) 5 tab-delimited .csv files as inputs. There are two types of files:

• The measures' (or quantifications') files, that can be of 4 types.
• The metadata, a unique file with the description of the samples in your measures' files. This is compulsory.

For running DIMet timecourse_analysis you need at least one file of measures:

• The total abundances (of the metabolites) file
• The mean enrichment or labelled fractional contributions
• The isotopologues absolute values files (optional)
• The isotopologue proportions file (optional)

and one metadata file, WHICH IS COMPULSORY, see section Metadata File Information.

The measure's files must be organized as matrices:

• The first column must contain Metabolite IDs that are unique (not repeated) within the file.
• The rest of the columns correspond to the samples
• The rows correspond to the metabolites
• The values must be tab separated, with the first row containing the sample/column labels.

See the following examples of measures' files:

Example - Metabolites abundances:

ID	MCF001089_TD01	MCF001089_TD02	MCF001089_TD03	MCF001089_TD04	MCF001089_TD05	MCF001089_TD06
2_3-PG	8698823.9926	10718737.7217	10724373.9	8536484.5	22060650	28898956
2-OHGLu	36924336	424336	92060650	45165	84951950	965165051
Glc6P	2310	2142	2683	1683	012532068	1252172
Gly3P	399298	991656565	525195	6365231	89451625	4952651963
IsoCit	0	0	0	84915613	856236	954651610

Example - mean enrichment or labeled fractional contributions:

ID	MCF001089_TD01	MCF001089_TD02	MCF001089_TD03	MCF001089_TD04	MCF001089_TD05	MCF001089_TD06
2_3-PG	0.9711	0.968	0.9909	0.991	0.40	0.9
2-OHGLu	0.01719	0.0246	0.554	0.555	0.73	0.68
Glc6P	0.06	0.66	2683	0.06	2068	2172
Gly3P	0.06	0.06	0.06	1	5	3
IsoCit	0.06	1	0.49	0.36	6	10

Example - Isotopologues

ID	MCF001089_TD01	MCF001089_TD02	MCF001089_TD03	MCF001089_TD04	MCF001089_TD05	MCF001089_TD06
2_3-PG_m+0	206171.4626	285834.0353	36413.27637	27367.17784	6171.4626	119999
2_3-PG_m+1	123	432	101	127	206171.4626	119999
2_3-PG_m+2	133780.182	161461.2364	182631.3947	132170.3807	358749.348	848754.36
2_3-PG_m+3	8358749.348	10271010.45	10505228.3	8376820.028	62163.30727	1088.8963
2-OHGLu_m+0	5550339.322	6072872.833	3855047.791	3216178.72	8358749.348	10271010.45
2-OHGLu_m+1	0.0	0.0	0.0	0.0	206171.4626	285834.0353

Example - Isotopologue proportions:

ID	MCF001089_TD01	MCF001089_TD02	MCF001089_TD03	MCF001089_TD04	MCF001089_TD05	MCF001089_TD06
2_3-PG_m+0	0.023701408	0.026667837	0.003395407	0.05955	0.034383527	0.12
2_3-PG_m+1	0.0	0.0	0.0	0.0	0.4	0.12
2_3-PG_m+2	0.015379329	0.01506	0.017029723	0.35483229	0.54131313	0.743
2_3-PG_m+3	0.960919263	0.958268099	0.97957487	0.581310816	0.017029723	0.017
2-OHGLu_m+0	0.972778716	0.960016157	0.238843937	0.234383527	0.9998888	0.015064063
2-OHGLu_m+1	0.0	0.0	0.0	0.0	0.0001112	0.960919263

Metadata File Information

Provide a tab-separated file that has the names of the samples in the first column and one header row. Column names must be exactly in this order:

name_to_plot condition timepoint timenum compartment original_name

Example Metadata File:

name_to_plot	condition	timepoint	timenum	compartment	original_name
Spleen1_cell_0-1	Spleen1	0min	0	cell	MCF001089_TD01
Spleen1_cell_0-2	Spleen1	0min	0	cell	MCF001089_TD02
Spleen1_cell_10-1	Spleen1	10min	10	cell	MCF001089_TD03
Spleen1_cell_10-2	Spleen1	10min	10	cell	MCF001089_TD04
Spleen1_cell_30-1	Spleen1	30min	30	cell	MCF001089_TD05
Spleen1_cell_30-2	Spleen1	30min	30	cell	MCF001089_TD06
Spleen1_cell_60-1	Spleen1	60min	60	cell	MCF001089_TD07
Spleen1_cell_60-2	Spleen1	60min	60	cell	MCF001089_TD08
Spleen1_cell_90-1	Spleen1	90min	90	cell	MCF001089_TD09
Spleen1_cell_90-2	Spleen1	90min	90	cell	MCF001089_TD011
Spleen1_med_30-3	Spleen1	30min	30	med	MCF001089_TD025
Spleen1_med_30-2	Spleen1	30min	30	med	MCF001089_TD023

The column original_name must have the names of the samples as given in your data.

The column name_to_plot must have the names as you want them to be (or set identical to original_name if you prefer). To set names that are meaningful is a better choice, as we will take them to display the results.

The column timenum must contain only the numeric part of the timepoint, for example 2,0, 10, 100 (this means, without letters ("T", "t", "s", "h" etc) nor any other symbol). Make sure these time numbers are in the same units (but do not write the units here!).

The column compartment is an abbreviation, coined by you, for the compartments. This will be used for the results' files names: the longer the compartments names are, the longer the output files' names! Please pick short and clear abbreviations to fill this column.

Running the analysis

You can precise how you want your analysis to be executed, with the parameters:

• datatypes : the measures type(s) that you want to run
• statistical_test : choose, by type of measure, the specific statistical test to be applied.

Kruskal-Wallis, Mann-Whitney, Wilcoxon’s signed rank test, Wilcoxon’s rank sum test t-test, and permutation test are currently offered (we use the trusted functions from scipy library https://docs.scipy.org/doc/scipy/reference/stats.html).

For the permutation test, we have established as test statistic, the absolute difference of geometric means of the two compared groups.

• qualityDistanceOverSpan: a normalized distance between the intervals of values of the compared groups, that is the cutoff for considering a minimal acceptable "separation". A 'distance/span' == 1 is a perfect separation, whereas if 'distance/span' < 0 there is no separation. To use with caution in case of important dispersion of your intra-group values. Default is -0.3 (not stringent)
• correction_method : one of the methods for multiple testing correction available in statsmodels library (bonferroni, fdr_bh, sidak, among others, see https://www.statsmodels.org/dev/generated/statsmodels.stats.multitest.multipletests.html).

There exist hints on use that will guide you, next to the parameters.

The output consists of tables with the computed metrics, one by each pair of timepoints compared. The number of output tables = number-of-conditions x (number-of-timepoints)-1 x number-of-compartments.

For more information about the implemented statistical tests, please visit: https://github.com/cbib/DIMet/wiki/2-Statistical-tests

The output files are explained in https://github.com/cbib/DIMet/wiki/3-Output

Available data for testing

You can test our tool with the data from our manuscript https://zenodo.org/record/10579862 (the pertinent files for you are located in the subfolders inside the data folder). You can also use the minimal data examples from https://zenodo.org/record/10579891