Definitions

Here is a list of the concepts used in TidyMS.

batch correction

A correction step applied to reduce the time dependent variation in the metabolite signals due to instrumental response changes, carryover, or metabolite degradation, among others.

blank correction

A correction applied on study samples to remove the contribution to the signal coming from sample preparation. This process consist in measuring a set of blank samples and using them to estimate the sample preparation contribution to the signal.

carryover

A measurement artifact in LC-MS. Occurs when signals from one sample are detected in the next sample (signals are “carried over”).

correction

A data curation step where the data matrix is transformed to correct the data.

data curation

The process of reducing the bias introduced in the measurements during sample preparation and data acquisition. Also, the filtration of samples that cannot be measured in an analytically robust way.

data matrix

A matrix of feature values where each row is a sample or observation and each column is a feature.

feature

A measurable property of a phenomenon being observed. In LC-MS a feature is usually represented as a chromatographic peak.

feature correspondence

The process of match features extracted in different samples.

feature descriptor

A series of characteristics of a feature. In the case of a chromatographic peak, feature descriptors can be peak area, retention time, mean m/z among, others.

feature detection

The process of finding a feature in a data set. Once a feature is detected it can be extracted into a feature descriptor. In LC-MS the feature detection procedure involves the detection of chromatographic peaks and extraction into rt, m/z and area information.

feature table

The table obtained after feature extraction, where each row is a feature detected in a sample and each column is a descriptor.

filtration

A data curation step where samples or features are removed according to an specific criteria.

mapping

A dictionary that maps the sample type to sample classes The available sample types are: study sample, quality control, blank, system suitability.

normalization

An operation on the data matrix to adjust the sample values. Common normalization methods use different norms, such as the euclidean norm, Manhattan norm or maximum norm.

prevalence filter

A filter applied on a data matrix to remove features that are detected in a low number of samples.

quality control sample

Samples applied to demonstrate analytical accuracy, precision, and repeatability after data processing and can be converted to metrics describing data quality.

run order

Temporal order in which the different samples were analyzed.

sample class

The category of the sample. Can be related to the study (e.g: healthy, disease) or to the experiment design (quality control, blank, etc…).

sample descriptor

A characteristic of a sample. Can be the sample type, class, run order, analytical batch.

sample type

The type of sample used in the experiment. Sample types can be: study sample, quality control, blank, system suitability.

scaling

An operation on the data matrix to change the distribution of features.

system suitability check

The analysis of a series of samples to assess the performance of an analytical platform.