stratifyPatients: Assign samples to sepsis response signature groups

In jknightlab/SepstratifieR: Stratification of infectious disease patients by gene expression profile

Assign samples to sepsis response signature groups

Description

Given a set of samples, classify them into one of three sepsis response signature (SRS) groups and predict a quantitative sepsis response score (SRSq) for each of them.

Usage

stratifyPatients(dat, gene_set = "davenport", k = 20, verbose = T)

Arguments

dat	A data.frame containing 'n' samples (rows) x 'm' genes (columns). It should contain at least 7 columns, corresponding to the genes listed in the main package documentation.
gene_set	A character value specifying which gene signature to use for stratification. This must be one of two values: 'davenport' (uses the 7-gene signature described by Davenport et al.) or 'extended' (uses an extended 19 gene-signature).
k	A numeric value specifying the number of nearest neighbours used to align the input to the reference data. Higher values of k will result in a more aggressive integration but can result in missed outliers. In contrast, lower values of k will retain more substructure in the input data, but can result in some samples being incorrectly flagged as outliers. We recommend setting 'k' to a value between 20% and 30% of the total number of input samples. For more information on this paramter, see the general package documentation.
verbose	A logical value indicating whether or not to print a step by step summary of the function's progress.

Details

This function is designed to stratify patients with suspected infectious disease into different molecular groups based on a sample of their gene expression from whole blood. These molecular groups are defined based on either a 7-gene signature or a 19-gene signature, and are referred to as sepsis response signature (SRS) groups.

To perform stratification, the function first aligns the input samples to a reference data set containing gene expression profiles from healthy individuals and sepsis patients. This alignment is performed using the mutual nearest neighbours (mNN) algorithm for batch correction. This has the purpose of bringing the predictor variables to the desired scale. Next, the samples are classified into SRS groups using a previously trained random forest model. In addition, each sample is also assigned a quantitative sepsis response signature score (SRSq) based on a second random forest prediction model.

The input expected by this function is a data.frame object with rows corresponding to individuals/samples and columns corresponding to genes. This data.frame must contain at least the following seven columns: ENSG00000152219, ENSG00000100814, ENSG00000127334, ENSG00000131355, ENSG00000137337, ENSG00000156414, and ENSG00000115085.

We recommend removing any technical batch effects from the input data set before calling this function. In addition, the predictor variables should have a roughly symmetric distribution. Thus, a transformation step is often useful. While this is often not a problem for microarray data sets, we recommend log-transforming any RNA-sequencing and qPCR data before prediction.

For more information on how SRS groups were originally defined, please refer to the following publications:

https://doi.org/10.1016/S2213-2600(16)00046-1 https://doi.org/10.1164/rccm.201608-1685OC

Value

A SepsisPrediction object containing the SRS and SRSq predictions obtained for each sample.

This object contains the following slots:

SRS

factor variable indicating the SRS group predicted for each sample, as obtained from random forest prediction.

SRS_probs

numeric variable containing the probability of each sample belonging to each SRS group, as obtained from random forest prediction.

SRSq

numeric variable indicating the quantitative sepsis response score (SRSq) predicted for each sample, as obtained from random forest prediction. An SRSq close to 0 indicates the individual is likely healthy, an SRSq close to 1 indicates the individual is at high risk of severe sepsis.

predictors_raw

data.frame containing the predictor variables as directly extracted from the user's input.

predictors_transformed

data.frame containing the predictor variables after mNN-based alignment with the reference set.

algined_set

data.frame containing both the user's input data and the reference set, aligned together using mNN.

gene_set

character variable indicating which gene signature was used for prediction (either the Davenport or the Extended signature)

mNN_outlier

logical variable indicating whether each sample is believed to be a potential outlier. Outliers are defined as any samples for which no mutual nearest neighbours were found in the reference set when applying the mNN algorithm.

Examples

# Load test data set
data(test_data)

# Stratify patients based on the signature originally described by Davenport et al.
predictions <- stratifyPatients(test_data)
predictions

# Stratify patients based on an extended 19-gene signature
predictions <- stratifyPatients(test_data, gene_set="extended")
predictions

jknightlab/SepstratifieR documentation built on March 19, 2022, 9:43 p.m.