detect_outliers_mad

Detects outliers in numeric data using the Median Absolute Deviation (MAD) method.
This robust method is less sensitive to extreme values than standard deviation
and works well for non-normally distributed data.

internal

An implementation of the TopoLow algorithm, a novel, physics-inspired method for
antigenic cartography. TopoLow addresses significant challenges in mapping antigenic relationships,
especially from sparse and noisy experimental data. The package transforms cross-reactivity and
binding affinity measurements into accurate spatial representations in a phenotype space.
Key features include:
* Robust Mapping from Sparse Data: Effectively creates complete and consistent maps even with high
proportions of missing data (e.g., >95%).
* Physics-Inspired Optimization: Models antigens as particles connected by springs (for measured
interactions) and subject to repulsive forces (for missing interactions), reducing the need for
complex gradient computations.
* Automatic Dimensionality Detection: Employs a likelihood-based approach to determine the optimal
number of dimensions for the antigenic map, avoiding distortions common in methods with fixed low dimensions.
* Noise and Bias Reduction: Naturally mitigates experimental noise and bias through its network-based,
error-dampening mechanism.
* Antigenic Velocity Calculation: Introduces and quantifies "antigenic velocity," a vector that describes
the rate and direction of antigenic drift for each pathogen isolate. This can help identify cluster
transitions and potential lineage replacements.
* Broad Applicability: Analyzes data from various pathogens, including influenza, HIV, and Dengue viruses.
It can be applied to any continuous and relational phenotype under directional selection pressure.
Methods are described in Arhami and Rohani (2025) <doi:10.1093/bioinformatics/btaf372>.

Omid Arhami

topolow

Antigenic Mapping and Antigenic Velocity Algorithm

detect_outliers_mad function

<dl><dt>data</dt>
<dd>Numeric vector of values to analyze</dd>
<dt>k</dt>
<dd>Numeric threshold for outlier detection (default: 3). Points more than
k MADs from median are considered outliers.</dd>
<dt>take_log</dt>
<dd>Logical. Whether to log transform data before (and only for) outlier detection (default: FALSE)</dd></dl>

Arguments

Detect Outliers Using Median Absolute Deviation — detect_outliers_mad

<dl>

<dt>data</dt>
<dd>Numeric vector of values to analyze</dd>


<dt>k</dt>
<dd>Numeric threshold for outlier detection (default: 3). Points more than
k MADs from median are considered outliers.</dd>


<dt>take_log</dt>
<dd>Logical. Whether to log transform data before (and only for) outlier detection (default: FALSE)</dd>

</dl>

detect_outliers_mad: Detect Outliers Using Median Absolute Deviation

Description

Usage

Value

Arguments

Details