+

Predicting Sepsis Patient Resilience Jing Tong

Insight Data Science Program

+Predicting sepsis resilience

Sepsis is a whole-body inflammatory response to an infection.

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Treatments: Standardized treatment More aggressive treatment

Sepsis patients: Survivors Non-survivors

Predicting sepsis resilience

+Data

Dataset1(Training)

Dataset2(Validation)

Dataset3(Validation)

+ Data

Each sample has a microarray gene expression profile (24840 transcripts 12293 genes)

#Samples

Sepsis Survivors

96

Non-survivors 31

Public dataset from one research paper (integrated by Sage) with 163 samples and 53 patients.

Dataset 1

+Randomly selected 2 genes

+Dataset1

5-Fold Cross

Validation Top500 Genes (p-value

< 0.001) from ANOVA

212 Consensus

Genes

X51/3

Validating

2/3 Training 4/5 Training

1/5 Testing Top500

x 5 x 5

+Feature selection

Randomly selected 2 genes

Top 2 genes in 212 genes

212 consensus genes found.

+Prediction model Algorithm: Support Vector Machine (SVM) Features: 212 consensus gene expressions Testing on 1/3 validation dataset

Measurement ResultAccuracy 0.93Precision 0.97

Recall 0.94F1-score 0.95

+Validation on other datasets Dataset2: Microarray Dataset3:

RNAseqDataset2 #Samples

Sepsis Survivors

51

Non-survivors 26

Dataset3 #Samples

Sepsis Survivors

78

Non-survivors 28

Construct their own SVM models by using 212 consensus genes.

Measurement Dataset2 Dataset3Accuracy 0.73 0.69Precision 0.81 0.83

Recall 0.76 0.73F1-score 0.79 0.78

+What biological processes are involved in these biomarkers?

http://amp.pharm.mssm.edu/Enrichr/enrich

Gene list enrichment analysis tool – “Enrich”

Gene name

Protein encoded

Biological process

ALOX15 LipoxygenaseInhibit diverse inflammatory diseases

including sepsis

PRKCD Protein kinase C Involved in B lymphocyte signaling

+Blog postsepsis-re.com

+About me

Computational Biology, Ph.D.

+

Thanks for your attention!

+Dataset1

Each sample has a gene expression profile (24840 transcripts 12293 genes)

#Samples

Sepsis Survivors

96

Non-survivors 31Healthy Control 36

Public dataset from one research paper (integrated by Sage) with 163 samples and 53 patients.

+Dataset – Feature Engineering 212 consensus genes found.

Survivors

Non-Survivors

+Dataset – Feature Engineering

Survivors

Non-Survivors

Top 50 (of 212) Gene expressions

Down-regulated

+Dataset – Feature Engineering

Survivors

Non-Survivors

Top 50 (of 212) Gene expressions

Up-regulated

+Dataset – GDS4971

Biomarker selections: 1) 2/3 training data, 1/3 validation data 2) Among training data, 5-fold cross validation 3) Each CV round, use ANOVA to find top500 (p-

value<0.001) genes. 4) Find the final consensus genes as potential

biomarkers.

#Samples

Sepsis Survivors

96

Non-survivors 31Healthy Control 36

+What are these biomarkers?

+What biological processes are involved in these biomarkers?

http://amp.pharm.mssm.edu/Enrichr/enrich

Gene list enrichment analysis tool – “Enrich”

+What biological process is involved in these biomarkers?

http://amp.pharm.mssm.edu/Enrichr/enrich

GenesALOX15PRKCDSTAT3ADRM1ITGB2ARHGAP1NEDD9AQP3RXRAITGADRASSF5CCR9ULK1CSKJAK1

+What biological processes are involved in these biomarkers?

Gene name

Protein encoded

Biological process

ALOX15 LipoxygenaseMetaboliting act to inhibit diverse inflammatory diseases including sepsis

PRKCD Protein kinase C

Involved in B cell signaling (a type of lymphocyte in the humoral immunity of the adaptive immune system)

STAT3 Transcription factor Associating with recurrent infections

ADRM1Adhesion-regulating molecule

Mediating lymphocyte adhesion in endothelial cells

ITGB2 IntegrinParticipating cell adhesion and cell-surface mediated signalling.