The goal of SepstratifieR is to stratify patients with suspected infection into groups with different molecular characteristics. This is done based on the expression level of a small set of genes measured from whole blood.
License: MIT + file LICENSE
You can install the development version of SepstratifieR from GitHub with:
# Install dependencies
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("batchelor")
BiocManager::install("MatrixGenerics")
# Install SepstratifieR
# install.packages("devtools")
devtools::install_github("jknightlab/SepstratifieR")This package 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 a small gene signature, and are referred to as sepsis response signature (SRS) groups.
There are three SRS groups, which are as follows:
SRS1 = Composed of sepsis patients with an immunosupressed profile. These individuals are often at high risk of mortality.
SRS2 = Composed of sepsis patients with an immunocompetent profile. These individuals are at lower risk of mortality.
SRS3 = Composed mostly of healthy individuals.
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
To perform stratification on a group of patient samples (i.e. the user’s input), SepstratifieR 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 based on a previously trained random forest model. In addition, each sample is also assigned a quantitative sepsis response score (SRSq) based on a second random forest prediction model. This score (SRSq) goes from 0 to 1. Patients with SRSq close to zero are likely to be healthy, while patients with SRSq close to one are at high risk.
The diagram below describes how the models used by SepstratifieR were built (top panel), as well as how the package’s functions perform alignment and classification (bottom panel):
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 columns:
- When using the 7-gene signature defined by Davenport et al:
| Column_name | Gene_name |
|---|---|
| ENSG00000152219 | ARL14EP |
| ENSG00000100814 | CCNB1IP1 |
| ENSG00000127334 | DYRK2 |
| ENSG00000131355 | ADGRE3 |
| ENSG00000137337 | MDC1 |
| ENSG00000156414 | TDRD9 |
| ENSG00000115085 | ZAP70 |
- When using the extended 19-gene signature:
| Column_name | Gene_name |
|---|---|
| ENSG00000144659 | SLC25A38 |
| ENSG00000103423 | DNAJA3 |
| ENSG00000135372 | NAT10 |
| ENSG00000079134 | THOC1 |
| ENSG00000135972 | MRPS9 |
| ENSG00000087157 | PGS1 |
| ENSG00000165006 | UBAP1 |
| ENSG00000111667 | USP5 |
| ENSG00000182670 | TTC3 |
| ENSG00000097033 | SH3GLB1 |
| ENSG00000165733 | BMS1 |
| ENSG00000103264 | FBXO31 |
| ENSG00000152219 | ARL14EP |
| ENSG00000100814 | CCNB1IP1 |
| ENSG00000127334 | DYRK2 |
| ENSG00000131355 | ADGRE3 |
| ENSG00000137337 | MDC1 |
| ENSG00000156414 | TDRD9 |
| ENSG00000115085 | ZAP70 |
If more columns are present, they will simply be ignored.
We recommend that predictor variables have the following units:
Microarray: Background-corrected, VSN-normalized, log-transformed intensity values
RNA-seq: Log-transformed counts per million (i.e. log-cpm)
qRT-PCR: Negative Cq values
In addition, any technical batch effects should be removedfrom the input data set before using SepstratifieR.
Below is a basic example which shows you how to use this package to stratify a small set of patients into sepsis response groups:
# Load package
library(SepstratifieR)
# Load test data set
data(test_data)
head(test_data)
#> ENSG00000144659 ENSG00000103423 ENSG00000135372 ENSG00000079134
#> s1 4.692800 4.736014 5.443159 3.828598
#> s2 5.013893 4.413963 5.140826 3.804591
#> s3 4.212220 4.917711 5.154672 3.671660
#> s4 2.938143 3.693315 4.255717 2.470483
#> s5 4.431750 4.718650 5.326826 3.910956
#> s6 3.919289 4.114919 4.461082 3.065047
#> ENSG00000135972 ENSG00000087157 ENSG00000165006 ENSG00000111667
#> s1 2.977473 7.077379 6.377639 5.561227
#> s2 3.052402 7.193405 6.995388 5.125931
#> s3 3.455646 7.939844 6.848399 5.661574
#> s4 1.642446 9.115725 7.271863 4.215087
#> s5 3.344655 6.972575 6.678864 5.675333
#> s6 2.074246 8.795017 6.937671 5.144748
#> ENSG00000182670 ENSG00000097033 ENSG00000165733 ENSG00000103264
#> s1 6.648605 7.541566 5.047954 4.928851
#> s2 6.404868 7.199108 4.764898 3.894049
#> s3 6.497354 7.905404 5.142841 4.164078
#> s4 5.576401 8.612519 3.824025 3.425956
#> s5 6.484286 7.718506 4.971902 4.171239
#> s6 5.933122 7.504945 4.194316 3.550335
#> ENSG00000152219 ENSG00000100814 ENSG00000127334 ENSG00000131355
#> s1 3.765825 2.723711 6.711303 6.461896
#> s2 3.581115 2.723518 6.226455 7.230779
#> s3 3.689515 2.609898 5.410223 5.412017
#> s4 2.648860 1.657687 4.579567 7.341940
#> s5 4.123084 2.629408 6.232329 5.868802
#> s6 2.131015 2.108574 4.714273 6.712530
#> ENSG00000137337 ENSG00000156414 ENSG00000115085
#> s1 5.136627 3.114777 8.016714
#> s2 4.620764 3.802095 7.002473
#> s3 4.951400 4.585588 6.192307
#> s4 3.986742 6.316707 4.445492
#> s5 4.737964 3.810283 6.879157
#> s6 4.584105 5.820275 6.431018
# Stratify patients
predictions <- stratifyPatients(test_data)
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=20
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!The results from this prediction look as follows:
predictions
#> SepsisPrediction
#>
#> Gene set used: davenport
#> 143 samples
#> 7 predictor variables
#>
#> Predictor variables: ENSG00000152219, ENSG00000100814, ENSG00000127334, ENSG00000131355, ...
#> Sample names: s1, s2, s3, s4, ...
#> SRS: SRS3, SRS3, SRS2, SRS1, ...
#> SRSq: 0.07342065, 0.2410302, 0.3857127, 0.8267178, ...Futhermore, you can use SepstratifieR’s built-in plotting function to check whether the input samples were successfully mapped to the reference set and if there are any clear outliers.
plotAlignedSamples(predictions)
SepstratifieR enables predictions based on two different gene signatures:
- A minimal set of 7 genes defined by Davenport et al. in 2016
- An extended set of 19 genes defined by Cano-Gamez et al. in 2021. This extended set includes the 7 genes proposed by Davenport plus an additional 12 genes derived from integrative analysis of RNA-seq and microarray data for sepsis patients in the GAinS study.
For further details on the definition of these signatures, please refer to the relevant publications.
The user can specify which gene signature to use for prediction by simply assigning a value to the ‘gene_set’ parameter. The default behaviour of SepstratifieR is to use the minimal 7-gene signature. This parameter can be modified as illustrated bellow:
# Stratify patients based on the 7-gene signature (this is the default option)
predictions <- stratifyPatients(test_data, gene_set = "davenport")
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=20
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
# Stratify patients based on the 19-gene signature
predictions_extended <- stratifyPatients(test_data, gene_set = "extended")
#>
#> Using the 'extended' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=20
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!IMPORTANT: Note that the extended gene signature was trained using RNA-seq and microarray measurements only. Thus, in its current state this signature should not be used to predict labels from qPCR data.
Perhaps the most important parameter to take into account when performing patient stratification is the number of mutual nearest neighbours in the data alignment step (k). The impact of this parameter on data integration has been previously summarized in the documentation of mNN (https://rdrr.io/github/LTLA/batchelor/man/mnnCorrect.html).
In brief, lower values of ‘k’ will retain more substructure in the input data, with samples that do not closely resemble the reference set being flagged as outliers (i.e. samples for which no mutual nearest neighbor was found). Conversely, higher values of ‘k’ will result in a more lenient and aggressive merging, where samples are forced to align with the reference data even if they differ in certain aspects. Higher values of ‘k’ often result in better performance of data integration, but can also cause outliers to pass undetected, especially when a small group of samples is not well represented in the reference set.
The authors of mNN suggest that ‘k’ be set to the expected size for the smallest subpopulation. Based on the proportion of individuals from different sepsis response (SRS) groups previously reported in the context of sepsis patients in intensive care, we recommend that this parameter be set to 20-30% the number of input samples. However, please not that this value might not be ideal if you are using this algorithm in a different patient population.
In the section below we explain how to assess if the predictions obtained with SepstratifieR are robust to the choice of ‘k’.
It is often unclear which value of ‘k’ is appropriate for a specific analysis. Moreover, if ‘k’ is low, many samples might be flagged as potential outliers by SepstratifieR, and it can be difficult to distinguish whether they are true outliers or simply samples which remained unmapped in the mNN step.
The best way to tackle both of these problems is by performing a sensitivity analysis. In this analysis, patient stratification and SRS prediction are repeatedly performed for a range of ‘k’ values, and the results from each iteration are compared to each other so as to assess their stability.
SepstratifieR has a built in function for sensitivity analysis. You can run this function on the same input used for patient stratification, as shown below:
sensitivity_results <- runSensitivityAnalysis(test_data)
#>
#> Using the ' davenport ' gene signature for prediction...
#> The k parameter will iterate through: 15 29 44 58 72 86 100 115 129 143
#> Predicting SRSq scores at all k values...
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=15
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=29
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=44
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=58
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=72
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=86
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=100
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=115
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=129
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=143
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#> Identifying mNN outliers at k=29...
#> Estimating variance in SRSq prediction for different values of k...Plotting SRSq predictions as a function of k...
#> Plotting variance in SRSq estimation for each sample...
#> ...done!
The heatmap above clearly shows that the quantitative predictions from SepstratifieR (SRSq) are robust to the choice of ‘k’, as they do not seem to change. In addition, when computing the variability in predicted SRSq scores across all of the evaluated values of ‘k’, we obtain a very low variance, as shown in the scatter plot. Finally, because samples flagged as outliers are randomly distributed in the plot and do not show a higher variance compared to the rest of the data, we can be fairly confident that they are not true outliers.
Now let’s see how this would look like if a group of outlier samples were present in the input data.
We begin by artificially increasing the expression levels of ARL14EP for the last 30 samples in the data set.
set.seed(1)
test_data$ENSG00000152219[114:143] <- test_data$ENSG00000152219[114:143] + rnorm(30, mean=8, sd=1)
tail(test_data)
#> ENSG00000144659 ENSG00000103423 ENSG00000135372 ENSG00000079134
#> s138 3.915896 4.142888 4.550090 3.432809
#> s139 3.841592 3.838046 4.299008 3.056424
#> s140 4.224488 4.128654 4.825778 3.525132
#> s141 4.432159 3.845172 4.641201 3.590158
#> s142 4.567482 4.815802 5.530656 3.729049
#> s143 4.363877 4.465925 4.982700 3.860408
#> ENSG00000135972 ENSG00000087157 ENSG00000165006 ENSG00000111667
#> s138 1.975244 8.415272 7.133359 4.565842
#> s139 2.600997 8.952625 7.380804 4.457593
#> s140 2.780442 8.158555 7.178084 5.036091
#> s141 2.458304 7.974497 6.974679 5.012814
#> s142 3.318740 6.210728 6.288433 5.671912
#> s143 2.690685 7.566938 6.193921 5.038547
#> ENSG00000182670 ENSG00000097033 ENSG00000165733 ENSG00000103264
#> s138 6.448115 7.868539 4.155435 3.551486
#> s139 5.672482 8.718168 4.180824 3.421137
#> s140 6.035116 8.190729 4.487655 4.006522
#> s141 6.251814 7.602255 4.470171 3.565085
#> s142 6.821453 7.099148 5.150154 4.738323
#> s143 6.526056 7.010125 4.604899 4.163501
#> ENSG00000152219 ENSG00000100814 ENSG00000127334 ENSG00000131355
#> s138 11.904400 2.369174 5.653310 7.222766
#> s139 11.018487 1.777352 5.199707 6.928787
#> s140 11.695622 2.318047 5.947188 7.787000
#> s141 9.952713 2.409055 5.473638 7.166783
#> s142 11.298369 3.011082 6.455832 6.675081
#> s143 12.389283 2.372734 6.347841 7.299634
#> ENSG00000137337 ENSG00000156414 ENSG00000115085
#> s138 4.246216 6.541095 6.245411
#> s139 4.141095 5.764071 6.260288
#> s140 4.203260 5.357629 6.673227
#> s141 4.785670 3.899674 5.743222
#> s142 4.840135 2.929434 6.970752
#> s143 4.444932 4.016022 7.484469These samples should now act as a subgruop of outliers and, indeed, they very clearly separate along PC3.
preds <- stratifyPatients(test_data, verbose=F)
plotAlignedSamples(preds, pcs=c(1,3), color_by = "mNN_outlier")
To confirm this, let’s now re-run the sensitivity analysis on this data set. Notice how now the scatter plot clearly shows that the group of samples set as outliers has higher variance in their SRSq estimations. Indeed, most of them are correctly flagged as outliers during mNN alignment. You can also see this in the heatmap, where the SRSq value estimated for the outlier samples abruptly decreases as ‘k’ increases.
sensitivity_results <- runSensitivityAnalysis(test_data)
#>
#> Using the ' davenport ' gene signature for prediction...
#> The k parameter will iterate through: 15 29 44 58 72 86 100 115 129 143
#> Predicting SRSq scores at all k values...
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=15
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=29
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=44
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=58
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=72
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=86
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=100
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=115
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=129
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#>
#> Using the 'davenport' gene signature for prediction...
#> Fetching predictor variables...
#>
#> Aligning data to the reference set...
#> Number of nearest neighours set to k=143
#> Identifying potential outlier samples...
#> Stratifying samples into sepsis response signature (SRS) groups...
#> Assigning samples a quantitative sepsis response signature score (SRSq)...
#> Adding sample names to object...
#> ... done!
#>
#> Identifying mNN outliers at k=29...
#> Estimating variance in SRSq prediction for different values of k...Plotting SRSq predictions as a function of k...
#> Plotting variance in SRSq estimation for each sample...
#> ...done!
Under this last scenario, the sensitivity analysis suggests we should exclude at least a subset of the samples flagged as outliers.
SepstratifieR relies on aligning samples to a reference gene expression set. This step requires the availability of information from multiple samples, which is used to identify shared patterns of variation between batches and achieve a high quality alignment. Due to this requirement, we do not recommend using the main functions in SepstratifieR when dealing with a single sample or a limited sample size. This is because instability in batch alignment makes these predictions unreliable.
Based on simulations and data subsampling, we estimate that the stratifyPatients() function should only be applied to data sets over 25 samples.
For situations where sample size is limited, we instead provide a purpose-built function which uses a ‘lazy learning’ approach to estimate SRS and SRSq. This approach is based on identifying the samples in our reference set which are most similar to the sample of interest (i.e. their nearest neighbours), and then “projecting” the SRS and SRSq labels of these nearest neighbours into the sample of interest. Similarities between sample are estimated using cosine similarities, which are independent of scale differences and thus robust to technical variation. Projection is then done using a “majority vote” system, where each nearest neighbour contributes information proportionally to its similarity to the sample of interest.
The following diagram illustrates our lazy learning approach for patient stratification:
Our lazy learning algorithm can be performed using either of the two gene signatures, as specified by the user. Moreover, the number of nearest neighbours (k) used to estimate SRS/SRSq by majority voting can also be specified.
For this function, we recommend that predictor variables have the following units:
Microarray: Background-corrected, VSN-normalized, log-transformed intensity values
RNA-seq: Log-transformed counts per million (i.e. log-cpm)
qRT-PCR: 2^(Negative Cq values)
IMPORTANT NOTES:
-
The expected units for qRT-PCR data are not the same in stratifyPatients() than in projectPatient(). The latter function expects positive values (i.e. 2^-Cq).
-
The meaning of ‘k’ in this function is not the same as in stratifyPatients(). The latter uses k for alignment but not for prediction. For lazy learning, ‘k’ has a direct impact on prediction.
Below is an example of how to predict SRS/SRSq for a single isolated sample.
Let’s first choose one random sample from our test set:
set.seed(2)
test_sample <- test_data[sample(rownames(test_data),1),]We can stratify this sample by SRS/SRSq by calling the projectPatient() function within SepstratifieR, as follows:
# Predict SRS and SRSq using kNN-based label projection
prediction <- projectPatient(test_sample)
#>
#> Using the 'davenport' gene signature for projection...
#> Fetching predictor variables...
#>
#> Calculating similarity to reference samples...
#> Projecting SRS/SRS based on nearest neighbours...
#> Number of nearest neighours set to k=20
#> Adding sample names to object...
#> ... done!The resulting object, of class SepsisProjection, contains the SRS and SRSq values estimated using this approach, as well as metadata on how the algorithm was run:
prediction
#> SepsisProjection
#>
#> Gene set used: davenport
#> 7 predictor variables
#>
#> Predictor variables: ENSG00000152219, ENSG00000100814, ENSG00000127334, ENSG00000131355, ...
#> SRS: SRS3
#> SRSq: 0.1028843Note that this function is not as accurate as stratifyPatients(), since the latter is based on cross-validated random forest models, for which accuracy is known and stable. In contrast, projectPatient() is not model based and is substantially more computationally intensive. Thus, we only recommend using this approach when less than 25 samples are available.
Eddie Cano-Gamez: ecg@well.ox.ac.uk