## 3 Results and Discussion
### Data Processing Results Obtained with SSIR  
The set of 56 samples was dichotomized into two classes: treated and non-treated. The bulk chromatographic raw data file was obtained using the file converter tool of the Xcalibur software. For each sample, all the time and mass registers were reorganized in a rectangular grid of time and mass combination (see the intensities represented in the schematic bubble chart of Figure 2a). A first calculation using the whole data in the time domain between 3 and 19 min and mass fragments 40-350 was used to confirm that no variable was selected before 6 min, and that the ions of mass 210 and 212 attached to TCA (see Table 1) did not appear at a time greater than 18 min. Hence, in order to reduce the search space and exclude d5-TCA, which was used as an internal standard, the time range was set from 6 to 17 min. The corresponding time partitions were 20 s (1/3 min) wide, resulting in 33 intervals. In turn, the range for the permitted m/z variable values was set from 49 to 170.5 amu. In the case of the mass interval, this was partitioned into 243 intervals (i.e., in steps of 0.5 amu), centered at half integer and integer mass values. Therefore, the original rectangular grid was made up of \(33\times 243=8019\) nodes for each sample, with each node being the combination of two intervals (time and ion mass). A symbolic representation (in a simple schematic \(10\times 10\) grid) of the time and mass variable combinations is shown in Figure 2b. All the intensity registers were normalized by re-scaling and setting the mean of intensities to an arbitrary value of 1000 units per sample. All these intensity values were then added into the corresponding nodes of Figure 2b, according to the defined time and mass intervals. Therefore, the available data descriptors per sample are a two-dimensional grid collecting a sum of intensities in each node. The SSIR method deals only with categorical variables. For the sake of simplicity, only binary variables have been considered here, as it was decided to dichotomize the values of the collected intensities in each node. The top 1% percentile values constituted the high level (black squares in Figure 2b,c), and the remaining values constituted the low level (grey squares). In effect, each sample has been codified by a string of levels (Figure 2c). The goal is to select those grid nodes (i.e., combinations of time and mass intervals) that correlate with the previously defined dichotomization label of the samples. Figure 2: Schematic representation for a sample of the variables and levels grid definition needed to apply the superposing significant interaction rules (SSIR) method: (**a**) a bubble graph representation of the original peak signals; (**b**) after gridding, each node is ultimately assigned to a binary label: low level (grey) or high level (black); (**c**) representation of the discretized grid variables and the respective low and high level series for three samples. Each sample bears its own distribution of labels (codification) along the grid. See text for more details. Figure 2c shows a simplified representation of the discretized grid variables and the codification series of low and high binary levels for some samples (horizontal readings). Particular possibilities were considered. Firstly, some nodes can exhibit a constant state over all the samples, i.e., they are always present the same level feature (see, for instance, the vertical readings along the first and last drawn nodes in Figure 2c). These types of nodes are not useful at all and were discarded. Of the 8019 dichotomized nodes, only 299 change their low or high condition over the samples. Secondly, some of the remaining nodes could present a degeneration, i.e., the same pattern variation along the samples, as is shown schematically for nodes 3 and 4 in Figure 2c. These degenerated nodes are equivalent and redundant.