Processing

Question 1

MS data analysis pipeline

Answer 1

1. Pre-processing
2. Pre-treatment
3. Statistical Modelling
4. Peak annotation

Question 2

Pre-processing

Answer 2

Extract all relevant information from the raw data and summarise in a table

Includes: filtering, data binning, peak detection, alignment

Question 3

Instrument background signal

Answer 3

The signal output from the instrument when a blank is measured, generally a voltage output that that is digitalized by an analog to a digital converter

Question 4

Noise

Answer 4

Sum of electronic and chemical noise, which is independent of the data signal

The fluctuation in the instrument background signal

Question 5

Analyte signal

Answer 5

The change in the instrument response to the presence of a substance

Question 6

Signal-to-noise ratio

Answer 6

Th ratio of the analyte signal to the noise measured on a blank

Question 7

Noise filtering

Answer 7

-Removes random noise, typically electronic or chemical in origin

Question 8

Chemical noise

Answer 8

Derived from chemical components in the matrix other than the target analyte

Question 9

Baseline correction

Answer 9

Specific form of noise occurring at the baseline of a spectrum

The baseline is an offset of the intensities of masses, and should be subtracted from the measured intensities

Question 10

Peak detection

Answer 10

The process of determining existence of a peak in a specific m/z value, and to quantify its intensity.

Question 11

Alignment vs calibration

Answer 11

A calibration is a comparison of an item to a standard in order to make a quantitative evaluation. An alignment is to make an adjustment in order to bring an item into range. If the item fails the calibration an alignment is performed to bring the item within its specified tolerances.

Question 12

baseline drift

Answer 12

The baseline drift of a mass spectrum is a constant shift of the peak intensities from their original values to the apparently determined values and consistently occurs for the entire spectrum. This drift is different from the noise background of the spectrum.

Question 13

baseline subtraction

Answer 13

Removes systematic artifact, usually attributed to clusters of ionised matrix molecules hitting the detector during early portions or the experiment

Question 14

Calibration

Answer 14

ensures mass accuracy

hat the spectra (mass assignment and relative abundance of spectral signals) resemble a previously determined standard.

Question 15

Normalisation

Answer 15

• reduce systematic variation but preserve biological variation
• Reduce variation from non-biological sources such as instrument batch effect

Question 16

Miscalibration

Answer 16

Miscalibration of the mass spectrometer leads to variations of the relationship between the observed M/Z vector and the true time-of-flight of the ions. Therefore, systematic shifts can appear in repeated experiments.

Question 17

Peak annotation

Answer 17

Process of noting each observed feature with a putative identity

Question 18

Matrix effect

Answer 18

The effect of an analytical method caused by all other components of the sample except the specific compound to be quantified

Question 19

Isotope

Answer 19

Same atomic number (number of protons), different mass number (protons+neutrons)

Question 20

Isomer

Answer 20

identical formula but different structure

Question 21

Systems biology

Answer 21

Systems biology studies interactions between interconnected networks that involve changes at the genomic, proteomic, and metabolomic levels under homeostatic conditions and in response to stimuli.

Question 22

Targeted metabolomics

Answer 22

While for targeted
metabolomics, it is a hypothesis-driven research and focuses on
absolute quantification of targeted metabolites involved in some
interested pathways. Appropriate sample preparation is required for
accurate quantification of target metabolites. The strength of this
strategy is high sensitivity and specificity; thus numerous metabolites of low abundance in biological samples might be quantified.
However, an obvious weakness of this strategy is its narrow coverage of metabolite detection

Question 23

Untargeted metabolomics

Answer 23

The untargeted metabolomics aims to detect metabolites as many as possible
in a biological sample. Thus in untargeted strategy metabolomics,
samples should be less preprocessed to keep more metabolites. This
strategy can identify patterns or fingerprints of metabolites
responding to genetic alterations and environmental stimuli,
which often used for hypothesis-generating and hypothesis refining study However, there are some difficulties of this
strategy in comparing investigation results of different laboratories,
combining research data in several time points, and characterizing
unknown metabolites in multi-experiments.

Question 24

Metabolite profiling

Answer 24

seeks to identify and quantify a selected number of pre-defined metabolites in a given sample

Question 25

Metabolite fingerprinting

Answer 25

aimed at measuring the global profile of the metabolites characterizing the sample, without specific identification and quantification (untargeted analyses)

Question 26

Untargeted followed by targeted analysis

Answer 26

According to characteristics of untargeted and targeted strategy, it is a powerful strategy to combine the
initially untargeted metabolomics for exploration with the subsequently targeted metabolomics for validation

Question 27

TIC normalisation

Answer 27

The Total Ion Current Normalization (TIC) is one of the more common types of global normalizations in MALDI-MS. This norm- alization makes the total amount of detected ions (the sum of all intensity values) equal in all spectra

Question 28

scaling

Answer 28

Scaling methods are data pretreatment approaches that divide each variable by a factor, the scaling factor, which is different for each variable. They aim to adjust for the differences in fold differences between the different metabolites by converting the data into differences in concentration relative to the scaling factor. This often results in the inflation of small values, which can have an undesirable side effect as the influence of the measurement error, that is usually relatively large for small values, is increased as well.

There are two subclasses within scaling. The first class uses a measure of the data dispersion (such as, the standard deviation) as a scaling factor, while the second class uses a size measure (for instance, the mean).