Abstract
Background
Differential expression (DE) analysis of transcriptomic data enables
genome-wide analysis of gene expression changes associated with
biological conditions of interest. Such analysis often provides a wide
list of genes that are differentially expressed between two or more
groups. In general, identified differentially expressed genes (DEGs)
can be subject to further downstream analysis for obtaining more
biological insights such as determining enriched functional pathways or
gene ontologies. Furthermore, DEGs are treated as candidate biomarkers
and a small set of DEGs might be identified as biomarkers using either
biological knowledge or data-driven approaches.
Methods
In this work, we present a novel approach for identifying biomarkers
from a list of DEGs by re-ranking them according to the Minimum
Redundancy Maximum Relevance (MRMR) criteria using repeated
cross-validation feature selection procedure.
Results
Using gene expression profiles for 199 children with sepsis and septic
shock, we identify 108 DEGs and propose a 10-gene signature for
reliably predicting pediatric sepsis mortality with an estimated Area
Under ROC Curve (AUC) score of 0.89.
Conclusions
Machine learning based refinement of DE analysis is a promising tool
for prioritizing DEGs and discovering biomarkers from gene expression
profiles. Moreover, our reported 10-gene signature for pediatric sepsis
mortality may facilitate the development of reliable diagnosis and
prognosis biomarkers for sepsis.
Keywords: Biomarkers discovery, Differential expression analysis,
Refined differential gene expression analysis, Feature selection
Background
Pediatric sepsis is a life-threatening condition that is considered a
leading cause of morbidity and mortality in infants and children
[[27]1, [28]2]. Sepsis is a systematic response to infection that is
characterized by a generalized pro-inflammatory cascade, which may lead
to extensive tissue damage [[29]3]. Early recognition of sepsis and
septic shock will help pediatric care physicians to intervene before
the onset of advanced organ dysfunction and thus reduce the mortality
and length of stay as well as post critical care complications [[30]4].
However, reliable risk stratification of sepsis, especially in
children, is a challenge due to significant patient heterogeneity
[[31]5] and existing poor definitions of sepsis in pediatric
populations [[32]6].
Existing physiological scoring tools commonly used in intensive care
units (ICUs), such as Acute Physiologic and Chronic Health Evaluation
(APACHE) [[33]7] and Sepsis-related Organ Failure Assessment (SOFA)
[[34]8], use clinical and laboratory measurements to quantify critical
illness severity but provide little information about the risk for poor
outcome (e.g., mortality) at the onset of the disease [[35]2]. Several
recent studies have proposed sepsis prognostic biomarkers (e.g.,
[[36]5, [37]9, [38]10]) as well as sepsis diagnostic biomarkers (e.g.,
[[39]11–[40]13]) by differentiating between infectious and
non-infectious systemic inflammatory response syndrome. To date,
transcriptomic, proteomic, and metabolomic data have been used to
identify sets of genes, proteins, or metabolites that are
differentially expressed among patients [[41]14]. However, a major
challenge for developing clinically feasible sepsis biomarkers is to
have a fast turnaround time [[42]14, [43]15].
Recent advances in high-throughput transcriptomic technology have
created opportunities for precision critical care medicine by enabling
fast and clinically feasible profiling of gene expressions within few
hours. For example, Wong et al. [[44]16] used a multiplex messenger RNA
quantification platform (NanoString nCounter) to profile the
expressions of previously identified 100 three subclass-defining genes
[[45]17] in 8–12 h. Differential gene expression analysis is a commonly
used computational approach for identifying genes whose expressions are
significantly different between two phenotypes. Given gene expression
profiles for septic patients annotated with targeted outcome (e.g.,
survivals vs. non-survivals), this analysis typically associates a
p-value (that could be corrected for multiple hypothesis testing) with
each gene from the two groups (e.g. survivals and non-survivals). Then,
DEGs are those genes with p-values lower than a specific threshold
(typically, 0.05) and user-specified thresholds for fold change (FC)
for up- and down-regulated genes [[46]18]. A typical DE analysis of
gene expression profiles often return hundred or more DEGs, where
considerable number of them might be highly correlated with one or more
other DEGs.
Against this background, we present a novel method for refining the
results of the statistical DE analysis methods via re-ranking and
prioritizing the genes from the outcome of DE analysis. Specifically,
we propose a hybrid approach that leverages: i) statistical DE analysis
for identifying a wide list of DEGs; ii) supervised feature selection
methods for selecting an optimal subset of DEGs with maximum relevance
for predicting the target variable and minimum redundancy among
selected genes; iii) supervised machine learning methods for assessing
the discriminatory power of the selected genes. Using gene expression
profiles from the blood samples extracted from 199 children admitted to
ICU and diagnosed with sepsis or septic shock, we first report a list
of 108 DEGs and associated enriched functional pathways. Then, we
demonstrate the viability of our proposed gene re-ranking method in
identifying a 10-gene signature for mortality in pediatric sepsis.
Finally, we make our Python code (including notebooks examples for
refining DEGs and analyzing biomarkers using two example datasets)
publicly available at [47]https://bitbucket.org/i2rlab/rdea/.
Methods
Data
Normalized and pre-processed transcriptomic gene expression profiles
were downloaded from [[48]19]. These gene expression profiles represent
peripheral blood samples collected from 199 pediatric patients (later
diagnosed with sepsis or septic shock) during the first 24 h of
admission to the pediatric ICU. Out of these 199 pediatric patients, 28
patients are non-survivals. Affymetrix CEL files were downloaded from
NCBI GEO accession number [49]GSE66099 and re-normalized using the
gcRMA method in affy R package [[50]20]. Probe-to-gene mappings were
downloaded from the most recent SOFT files in GEO and the mean of the
probes for common genes were set as the gene expression level.
Differential expression analysis
We used limma R package (Version 3.42.0) [[51]18] to identify the
differentially expressed genes with a Benjamini-Hochberg (BH)
correction method. We calculated the fold change with respect to the
non-survival (i.e., the upregulated genes are the genes with expression
of the non-survival samples that are higher than the expression of
these genes in the survival samples).
Classification methods
We experimented with three commonly used machine learning algorithms
for developing and evaluated binary classifiers for predicting
mortality in pediatric sepsis: i) Random Forest [[52]21] with 100 trees
(RF100); ii) eXtreme Gradient Boosting [[53]22] with 100 weak tree
learners (XGB100); iii) Logistic Regression (LR) [[54]23] with L2
regularization. The three algorithms are implemented in the
Scikit-learn machine learning library (Version 0.21.2) [[55]24].
Feature selection methods
We used two feature selection methods that have been widely used with
gene expression data, Random Forest Feature Importance (RFFI) [[56]21]
and Minimum Redundancy and Maximum Relevance (MRMR) [[57]25]. For the
RFFI method, we trained a RF with 100 trees and then feature importance
scores which quantify the contribution of each feature in the learned
RF model were used to sort and rank the input features and only top
k = 1, 2, …, 10 were selected for training our classifiers. For MRMR
feature selection method, we used the training data to select the top k
features. These features were selected such that the objective function
in Eq. [58]1 is maximized. Let, Ω, S, and Ω[S] denote input, selected,
and non-selected input features, respectively. The first term in Eq.
[59]1 uses a relevance function f(x[i], y) to quantify the relevance of
the feature x[j] for predicting the target output y while the second
term quantifies the redundancy among the selected features in S using
the function g(x[j], x[l]). We implemented the MRMR algorithm [[60]25,
[61]26] as a Scikit-learn feature selection model using Python. In our
experiments, we used the Scipy (Version 1.2.1) implementation of the
Pearson correlation coefficient to compute redundancy between features.
For relevance functions, we considered three functions (implemented in
Scikit-learn): area under ROC curve (MRMR_auc); χ^2 (MRMR_chi2); and
F-Statistic (MRMR_fstat).
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1
Marker genes discovery and performance evaluation
We identified top discriminative features (i.e., marker genes) and
estimated the performance of the machine learning classifiers using 10
runs of the 10-fold cross-validation procedure. Briefly, we repeated
the following procedure 10 times: First, the dataset was randomly
partitioned into 10 equal subsets (each with the same survivals to
non-survivals ratio as the entire dataset). Nine of the 10 subsets were
combined to serve as the feature selection and training set while the
remaining subset was held out for estimating the performance of the
trained classifier. This procedure was repeated 10 times, by setting
aside a different subset of the data as the test set. Overall, we had
100 iterations of train and test experiments. The reported performance
is averaged over the 100 iterations and the score of each feature
represents the fraction of how many times this feature was selected in
the 100 iterations (i.e., a feature with a score of 0.85 means that
this feature had been selected to train the classifier in 85 out of 100
iterations).
We assessed the performance of classifiers using five widely used
predictive performance metrics [[62]27]: Accuracy (ACC), Sensitivity
(Sn); Specificity (Sp); and Matthews correlation coefficient (MCC);
Area under ROC curve (AUC) [[63]28]. AUC is a widely used metric and
summary statistic of the ROC curve. However, when several models have
almost the same AUC score, we can still compare them by examining their
ROC curves to determine if a model has an ROC curve that completely or
partially (in the leftmost region) dominates all other ROC curves.
Pathway enrichment analysis
We used the function find_enriched_pathway in the KEGGprofile R package
(Version 1.28.0) [[64]29] to map the differentially expressed genes in
KEGG pathway database [[65]30]. In our experiments, pathways with
adjusted p-value ≤0.05 and gene count ≥2 were considered significantly
enriched.
Results
Identification of differentially expressed genes and enriched pathways
Based on absolute fold change ≥1.5 and adjusted p-value ≤0.05, 108 from
a total of 10,596 genes were found to be DEGs between survival and
non-survival septic pediatric patients (See Additional file [66]1:
Table S1) and Additional file [67]2: Fig. S1). Table [68]1 shows the
top 10 DEGs when the genes are ranked using the absolute value of their
fold change. Only one gene, TGFBI, is down-regulated while the
remaining nine genes are up-regulated. TGFBI is among the 11 genes that
have been used in the Sepsis MetaScore (SMS) gene expression diagnostic
method [[69]11, [70]31]. The top three upregulated genes are SLC39A8,
RHAG, and DDIT4. SLC39A8 is found in the plasma membrane and
mitochondria and plays a critical role at the onset of inflammation
[[71]32]. Both RHAG (also called SLC42A1) and SLC39A8 belong to solute
carrier (SLC) group of membrane transport proteins. Finally, increased
expressions of DNA Damage Inducible Transcript 4 (DDIT4) gene had been
associated with higher risks of mortality in sepsis patients [[72]10,
[73]19].
Table 1.
List of top 10 DEGs ranked by the absolute value of the fold change
ID FC p-value Adj. p-value Regulation
SLC39A8 2.93 3.80E-07 6.71E-04 Up
RHAG 2.92 2.25E-04 2.60E-02 Up
DDIT4 2.78 1.22E-07 4.32E-04 Up
MPO 2.75 4.56E-04 3.90E-02 Up
RRM2 2.69 1.63E-04 2.26E-02 Up
CCL3 2.67 1.97E-06 1.91E-03 Up
TGFBI −2.59 7.89E-04 5.00E-02 Down
MAFF 2.56 2.45E-05 7.20E-03 Up
TYMS 2.55 5.13E-04 4.12E-02 Up
ENPP2 2.42 7.26E-05 1.33E-02 Up
KIAA0101 2.42 1.57E-04 2.23E-02 Up
[74]Open in a new tab
In order to get biological insights into the functional rules of the
identified 108 DEGs, we used the KEGGProfile R package to identify
enriched human KEGG pathways in this set of genes. In our experiments,
we did not threshold on the p-value, adjusted p-value, or minimum
number of genes in the pathway such that the returned results include
all KEGG pathways that have at least one gene in common with the target
set of genes. The complete set of results is provided in Additional
file [75]1: Table S2. We considered a pathway to be significantly
enriched if its adjusted p-value is ≤0.05 and at least two DEGs are
included in that pathway. Using these criteria, we got 8 significantly
enriched pathways (Table [76]2). Most of these pathways had been linked
to inflammation and/or DNA damage.
Table 2.
List of significantly enriched KEGG pathways
Pathway p-value Adj. p-value
Cell cycle 5.71E-12 1.92E-09
DNA replication 8.02E-09 1.35E-06
Oocyte meiosis 5.02E-06 4.23E-04
Mineral absorption 4.78E-06 4.23E-04
p53 signaling pathway 2.13E-04 1.23E-02
Human T-cell leukemia virus 1 infection 2.18E-04 1.23E-02
Pyrimidine metabolism 9.22E-04 3.89E-02
Progesterone-mediated oocyte maturation 9.16E-04 3.89E-02
[77]Open in a new tab
Additional file [78]2 Fig. S2 shows the heatmap of the correlation
matrix of the 108 DEGs. The figure shows that up-regulated and
down-regulated DEGs are clustered separately. We also noted that within
each cluster, every gene might be highly correlated with multiple other
genes.
Can a small subset of the DEGs discriminate between survivals and
non-survivals?
Here, we report the results of evaluating 120 models obtained using a
combination of three supervised classification algorithms, four feature
selection methods, and 10 possible values for the number of selected
features (k = {1, 2, …, 10}). Additional file [79]1: Table S3 shows the
average performance metrics estimated over 10 runs of 10-fold
cross-validation experiments. Figure [80]1 shows the boxplots of the
average AUC scores for each combination of a classification algorithm
and a feature selection method. Interestingly, MRMR_auc is consistently
the best feature selection method using any of the three classification
algorithms considered in our experiments. Surprisingly, we found that
the models obtained using this feature selection method and LR
algorithm not only have the best performance (in terms of AUC scores)
but also have the lowest variance in estimated AUC (i.e., AUC scores
are between 0.84 and 0.85). Additional file [81]1: Table S4 shows the
results of using the Mann-Whitney U test pairwise comparisons of
classifiers (in Fig. [82]1) for each feature selection method. We found
that the median AUC score for LR is significantly higher than the
median AUC score for RF100 using the four feature selection methods. We
also found that the median AUC score for LR is significantly higher
than the median AUC score for XGB100 using MRMR_auc and MRMR_chi2
feature selection methods.
Fig. 1.
[83]Fig. 1
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Comparisons of LR, RF100, and XGB100 classifiers evaluated using four
different feature selection methods and 10 runs of 10-fold
cross-validation experiments. Each boxplot represents the distribution
of average AUC score of 10 models evaluated using a given
classification algorithm and feature selection method for selecting top
k = 1, 2, …, 10 features
Figure [85]2 shows that (using MRMR_auc feature selection) LR models
outperformed corresponding RF100 and XGB100 models for any choice of
the number of selected features in k = {1, 2, …, 10}. Based on this
figure, one might conclude that we should not use more than 2 features
since adding more features did not yield any improvements in the AUC
score.. However, to accurately identify the best performing LR model,
we inspected the average ROC curves of these LR models (See Additional
file [86]2: Fig. S3). The LR model using only 2 features is dominated
in the leftmost region of the curve (i.e., region corresponds to
specificity greater than 0.80) by all other models. For a target
specificity greater than 0.80, the best ROC curve corresponds to the
model trained using top seven selected DEGs. We concluded that the best
model (out of the 120 models evaluated in this study) is based on LR
algorithm and MRMR_auc method for selecting top seven DEGs. Therefore,
only seven genes are needed to achieve the highest AUC score of 0.85.
Fig. 2.
[87]Fig. 2
[88]Open in a new tab
Performance comparisons of RF100, LR, and XGB100 models using top
k = 1, 2,. .., 10 features selected using MRMR_auc method
Machine learning based re-ranking of DEGs
Due to the small dataset and the instability of feature selection
methods, the top seven DEGs selected in each fold might be different.
Note that we conducted 10 runs of 10-fold cross-validation procedure.
Thus, we chose seven DEGs 100 times to train and evaluate the LR model.
To determine the importance of each gene, we assigned each gene a score
indicating how many times (out of 100) this gene had been selected
among the top seven genes used to train the classifier. Then, we simply
normalized the scores by dividing by 100 such that gene importance
scores of 1.0, 0.87, and 0.0 correspond to genes that have been
selected 100, 87, and zero times, respectively. Additional file [89]1:
Table S5 reports the gene importance scores for the 108 DEGs. Only 31
genes have importance score greater than zero. The top 15 genes and
their importance scores are shown in Fig. [90]3. We noted that three
genes (DDIT4, RHAG, and AREG) had been consistently selected in each
time.
Fig. 3.
[91]Fig. 3
[92]Open in a new tab
Top 15 gene markers identified using proposed machine learning based
DEGs re-ranking method
As a result of the small number of samples in our dataset, the
performance of any predictive model estimated using 10-fold
cross-validation procedure might vary for different random partitioning
of the data into 10 folds. Therefore, the repeated cross-validation is
essential for obtaining more accurate estimates of model performance.
To examine if the repeated cross-validation is also necessary for
obtaining robust estimates of gene importance scores, we repeated the
preceding experiment using a single run of 10-fold cross-validation
procedure. The resulting gene importance scores are reported in
Additional file [93]1: Table S6. Only 15 genes have non-zero scores.
Out of these genes, we found that 12 genes are in the top 15 genes
determined using the repeated 10-fold cross-validation experiment.
In summary, our machine learning based refining of DEGs outcome reduced
the number of DEGs from 108 to 31 and provided an alternative ranking
of these genes. Next, we show how to use this ranking to determine the
minimum set of DEGs that best discriminate between pediatric sepsis
survivals and non-survivals.
A 10-gene signature of mortality in pediatric sepsis
We used the top 15 genes in Fig. [94]3 to search for a minimal set of
genes that best discriminates between pediatric sepsis survivals and
non-survivals. Specifically, for top k = {4, 5, …, 15} genes, we
obtained the average ROC curves of LR models estimated using 10 runs of
10-fold cross-validation procedure (See Additional file [95]2: Fig.
S4). We found no improvement in the ROC curve when using more than top
10 genes. Figure [96]4 shows the boxplots of the normalized gene
expressions of these 10 genes. Interestingly, all 10 genes are
up-regulated. The most expressed genes are COX7B and DDIT4 while the
least expressed genes are PRG2 and AREG.
Fig. 4.
[97]Fig. 4
[98]Open in a new tab
Boxplots for the normalized expressions of the 10 marker genes in
survival and non-survival groups
Using this panel of 10 marker genes, we compared the three machine
learning algorithms considered in this study. We found that the ROC
curve of the LR model almost dominates the two ROC curves for RF100 and
XGB100 classifiers (Fig. [99]5). Performance comparisons of these three
classifiers are provided in Table [100]3. The LR model has an average
AUC score of 0.89 while both RF100 and XGB100 have an average AUC score
of 0.86. Moreover, the LR model has the best sensitivity, specificity,
and MCC.
Fig. 5.
Fig. 5
[101]Open in a new tab
Average ROC curves of RF100, LR, and XGB100 models estimated using 10
runs of 10-fold cross-validation and 10 machine learning identified
marker genes
Table 3.
Performance estimates of different classifiers evaluated using 10 runs
of 10-fold cross-validation procedure
Model ACC Sn Sp MCC AUC
RF100 88.6% 0.31 0.98 0.37 0.86
LR 87.6% 0.55 0.93 0.50 0.89
XGB100 86.9% 0.37 0.95 0.37 0.86
[102]Open in a new tab
Additional file [103]1 Table S7 shows the enriched KEGG pathways of the
10 marker genes. Since these 10 genes are minimally redundant with each
other, it is hard to find pathways that include more than one of these
genes. We found only two pathways, Necroptosis (Genes Found: STAT4 and
TNFAIP3) and PI3K-Akt signaling pathway (Genes Found: AREG and DDIT4),
with more than one hit from the 10 marker genes.
Comparison of different gene ranking methods
We compared the LR model trained using the 108 DEGs to the LR models
trained using only top 10 DEGs obtained using our proposed machine
learning based gene ranking method (top10_ml) and two other ranking
methods based on absolute fold change (top10_fc) and p-values
(top10_pv). The average ROC curves of the four LR models are shown in
Fig. [104]6-a and the performance metrics of these models are reported
in Table [105]4. The model using the 108 DEGs has the worst ROC curve
and the lowest performance estimates. The model based on top 10 genes
obtained using the absolute fold change ranking slightly outperformed
the model based on top 10 genes ranked using the p-values. Finally, the
model obtained using our proposed machine learning based ranking
substantially outperformed all three models. Although all the models
based on the three ranking methods had acceptable performance (i.e.,
AUC score ≥0.84), we found that the three sets of genes were not
substantially overlapping with each other (See Fig. [106]6-b). Every
set of genes had at least 5 unique genes and the only common gene among
the three sets was DDIT4. Figure [107]6 also visualizes the gene
expression profiles for survival and non-survival patients in a 3D
space defined by the top three marker genes in these three lists.
Fig. 6.
[108]Fig. 6
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Comparisons of three gene ranking methods. a ROC curves of LR models
evaluated using 108 DEGs and top 10 marker genes determined using fold
change (top10_fc), p-value (top10_pv), and proposed machine learning
method (top10_ml). b Venn diagram of these three lists of 10 marker
genes. Visualization of survival (green) and non-survival (red) samples
in a three-dimensional space based on the top three genes in (c)
top10_fc, (d) top10_pv, and (e) top10_ml
Table 4.
Performance estimates of LR classifiers evaluated using 10 runs of
10-fold cross-validation procedure and different set of genes
Gene set ACC Sn Sp MCC AUC
DEGs 80.3% 0.41 0.87 0.26 0.75
top10_fc 85.7% 0.41 0.93 0.36 0.85
top10_pv 86.2% 0.40 0.94 0.38 0.84
top10_ml 87.6% 0.55 0.93 0.50 0.89
[110]Open in a new tab
Discussion
Differential expression (DE) analysis has been widely used to analyze
gene expression profiles and uncover the underlying biological
mechanisms for complex diseases [[111]33, [112]34]. In general gene
expression profiles are characterized with high dimensionality (tens of
thousands of genes) and high pairwise correlations between genes.
Therefore, the outcome of DE analysis tools often includes hundred(s)
of highly correlated genes (see Additional file [113]2: Fig. S2).
Therefore, it is impractical to use all DEGs for developing diagnostic
and prognostic prediction tools. In general, identifying a gene
signature (a small set of marker genes) can be done using domain
knowledge or data-driven approaches [[114]14]. In this study, we
presented a data-driven approach to prioritize the marker genes using
an instance of the MRMR feature selection algorithm for selecting genes
with the highest AUC for predicting the pediatric sepsis mortality and
the minimal redundancy among selected genes in terms of Pearson’s
correlation coefficients. The novelty of our work includes the
integration of feature selection methods into the statistical pipeline
for DE analysis, the introduction of a new relevance scoring function
based on AUC scores for the MRMR algorithm, and the identification of a
10-gene signature of mortality in pediatric sepsis.
An interesting observation in our analysis is that the widely used
performance metrics such as sensitivity, specificity, and AUC might not
be sufficient to draw accurate conclusions regarding how different
models compare to each other particularly when models are very
competitive with each other and there is no model with an ROC curve
that dominates the ROC curves for the remaining models. This
underscores the drawback of quantifying the ROC curves using their AUC
scores without visualizing the ROC curves for more accurate
comparisons. Another interesting observation is related to the observed
surprisingly superior performance of LR models compared with RF100 and
XGB100 models. This superior performance combined with the fact that LR
models are linear interpretable models make LR algorithm a preferred
choice for developing prediction models based on gene expression
profiles as long as marker genes can be reliably identified.
It should be noted that supervised machine learning algorithms combined
with feature selection methods could be directly applied to identify
marker genes from the entire transcriptomic profiles. However, this
approach suffers two major limitations. First, the computation time
might be extremely long because some feature selection methods
including: MRMR which often has a run time in hours when applied to
gene expression datasets with tens of thousands genes; feature
selection based on genetic algorithms [[115]35]; and network-based
feature selection [[116]36]) have expensive computational time
proportion to the number of features. Second, it is challenging to
apply functional enrichment analysis to the identified set of marker
genes because of the small number of identified genes and the lack of
significant redundancy among these genes [[117]19]. Therefore, it is
less likely that these genes share any common functional pathways. The
present approach utilizes supervised feature selection to refine the
outcome of statistical DE analysis. It will be interesting to explore
novel approaches for separately applying statistical DE and supervised
feature selection to entire gene expression profiles and then integrate
the outcome of the two methods. For example, NetworkAnalyst tool
[[118]37] supports comprehensive meta-analysis of multiple gene lists
through heatmaps, Venn diagrams, and enrichment networks. One
interesting way for obtaining more than one list of DEGs is to obtain
them using different statistical and machine learning approaches.
Our DE and machine learning analyses suggested three 10-gene marker
lists for predicting mortality in pediatric sepsis with average AUC
score ≥0.86. These three lists had only one gene in common, which
suggests the existence of multiple data-driven gene signatures for
mortality in pediatric sepsis. Similar observation had been reported by
Sweeney et al. [[119]19] where the authors had reported four sets of
sepsis marker genes with only few genes in common. This underscores the
need for independent validation set as well as wet laboratory
experiments to validate some of these markers and confirm the reported
biological insights.
Conclusions
We have identified a signature of 10 marker genes for reliably
predicting mortality in pediatric sepsis. These 10 genes have been
determined using a novel machine learning data-driven approach for
re-ranking and selecting an optimal subset of 108 DEGs identified via a
secondary analysis of, to the best of our knowledge, the largest
publicly available transcriptomic cohort study for pediatric sepsis.
Our on-going work aims at: i) validating our proposed 10-gene signature
using an independent test set; ii) testing and evaluating the proposed
approach for identifying reliable biomarkers for challenging biomarker
discovery tasks in critical care settings such as diagnosing and
endotyping sepsis and Acute Respiratory Distress Syndrome (ARDS); iii)
Adapting our approach for single cell gene expression analysis
[[120]38, [121]39].
Supplementary information
[122]12920_2020_771_MOESM1_ESM.xlsx^ (46.2KB, xlsx)
Additional file 1. Supplementary Tables S1-S4.
[123]12920_2020_771_MOESM2_ESM.pdf^ (907KB, pdf)
Additional file 2. Supplementary Figs. S1-S4.
Acknowledgements