GSE134347

Protein-coding and non-coding RNA expression in whole blood leukocytes of healthy subjects and critically-ill patients with sepsis or non-infectious etiology.

Whole-study status Exploratory only

Dataset can inform exploration but is not strong enough for primary analysis.

Whole-study downloads — these do not apply your sample requirements.

Selected cohort requirements

Samples meeting selected requirements: 0 cases / 0 controls

View matching samples

215 case / 83 control samples elsewhere in the study have incomplete or conflicting evidence; they are not included above

Patient pairs verified in indexed sample metadata: 0

A case/control comparison meeting these requirements is not confirmed.

Counts describe samples, not independent patients. Pair verification uses explicit subject identifiers in indexed sample metadata. Zero verified pairs does not establish that the study is unpaired; reported matched-normal design alone is insufficient.

Source
GEO
Assay
microarray
Organism
Homo sapiens
Whole-study samples
298 samples
Specimen
Biofluid
Reported/interpreted study design
Patient biofluid vs healthy control Design classification does not establish patient pairs.
Comparison type
Tumor vs normal
Contributors
Scicluna BP, van der Poll T, Scicluna B
Contact name
Brendon Scicluna
Submission date
2019-07-16
Last update
2022-08-03
Organization
University of Malta
Department
Not available
Public date
2021-01-11
Experiment types
Expression profiling by array
Platforms
GPL17586 [HTA-2_0] Affymetrix Human Transcriptome Array 2.0 [transcript (gene) version]
Series type
SubSeries
SubSeries
Not available
BioProject
PRJNA554858

Summary

Genome-wide gene expression profiling of whole blood leukocytes in critically ill patients with sepsis or non-infectious disease has been used extensively in search of diagnostic biomarkers, as well as prognostic signatures reflecting diseases severity and outcome. Through technological advances in genomics it has become clear that transcription is not limited to protein-coding regions of the genome. Here, we describe a comprehensive analysis of RNA expression in blood leukocytes of critically ill patients with sepsis, a non-infectious condition and healthy subjects

Overall design

Whole-blood transcriptional patterns of protein-coding and non-coding RNA molecules in healthy subjects, critically-ill patients with sepsis or non-infectious conditions were generated by means of the human transcriptome array 2.0 (HTA 2.0) chip. Whole-blood from patients was collected within 24 hours of intensive care unit (ICU) admission. Title legend: healthy, healthy subjects; sepsis, ICU patients with sepsis; noninfectious, ICU patients with non-infectious condition.

Source Evidence

4 sources
Source Contribution Status
GEO

Original dataset metadata, sample metadata, source links, and GEO data availability flags.

Sample records
298 GSM samples
 Primary source
recount3

Planned trusted source for processed count availability.

 No match found
Expression Atlas

Planned source for curated expression experiment links.

 No match found
PubMed

Primary GEO citations and related PubMed articles that mention this dataset.

20 linked publications 20 DOIs

Primary publication

  1. The Long Non-Coding Antisense RNA JHDM1D-AS1 Regulates Inflammatory Responses in Human Monocytes. Frontiers in cellular and infection microbiology, 2022
  2. The leukocyte non-coding RNA landscape in critically ill patients with sepsis. eLife, 2020

Related publications

  1. Identification of key genes and development of an identifying machine learning model for sepsis. Inflammation research : official journal of the European Histamine Research Society ... [et al.], 2025
  2. Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning. Computational and structural biotechnology journal, 2023
  3. Bioinformatics reveals diagnostic potential of cuproptosis-related genes in the pathogenesis of sepsis. Heliyon, 2024
  4. Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for sepsis by integrated bioinformatics analysis and single-cell RNA sequencing analysis. Heliyon, 2024
  5. Exploring the role of pyroptosis and immune infiltration in sepsis based on bioinformatic analysis. Immunobiology, 2024
  6. GRN and KLRB1 define a shared peripheral-blood transcriptomic signature linking SLE and IPF. Journal of translational autoimmunity, 2026
  7. Contribution of prognostic ferroptosis-related subtypes classification and hub genes of sepsis. Transplant immunology, 2022
  8. Integrated analysis reveals NLRC4 as a potential biomarker in sepsis pathogenesis. Genes and immunity, 2024
  9. Single-cell RNA sequencing and transcriptomic analysis reveal key genes and regulatory mechanisms in sepsis. Biotechnology & genetic engineering reviews, 2024
  10. EXPLORING THE PROGNOSTIC NECROPTOSIS-RELATED GENES AND UNDERLYING MECHANISM IN SEPSIS USING BIOINFORMATICS. Shock (Augusta, Ga.), 2024
  11. Identification and validation of key genes related to arginine methylation modification in sepsis using transcriptome combined with Mendelian randomization analysis. Shock (Augusta, Ga.), 2025
  12. Revealing novel pyroptosis-related therapeutic targets for sepsis based on machine learning. BMC medical genomics, 2023
  13. Diagnostic and prognostic value of autophagy-related key genes in sepsis and potential correlation with immune cell signatures. Frontiers in cell and developmental biology, 2023
  14. Identification of Hub Genes With Differential Correlations in Sepsis. Frontiers in genetics, 2022
  15. Identification and validation of a novel mitochondrion-related gene signature for diagnosis and immune infiltration in sepsis. Frontiers in immunology, 2023
  16. A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis. Frontiers in immunology, 2023
  17. Identification and validation of core biomarkers for sepsis: a comprehensive analysis using bioinformatics and machine learning. Frontiers in immunology, 2025
  18. Peripheral Blood Genes Crosstalk between COVID-19 and Sepsis. International journal of molecular sciences, 2023
Available

Metadata Quality Components

Persisted metadata quality

100 pts
Clarity
100%
Weight
100 pts

Sample source clarity: All samples have source metadata and clear GSE/GSM accessions. Group clarity: Rule-based grouping found both case and control samples. Disease clarity: Disease or condition metadata normalized. Tissue clarity: Anatomical source metadata normalized. Treatment clarity: No unclear treatment split detected in the sample metadata. Replicate clarity: No pooled sample or technical replicate flags detected. Data availability clarity: Dataset-level processed or raw data availability is explicit.

Analysis Readiness

Review before analysis
97% DE readiness

Contrast completeness

25/25 pts

Both case and control groups are present in the proposed grouping.

Minimum group size

20/20 pts

Each contrast group has at least 2 samples.

Processed data availability

20/20 pts

Processed data is marked available in the GEO index.

Sample decision clarity

15/15 pts

All samples are assigned to a case or control group.

Blocking warnings

7/10 pts

Warnings are present, but none are critical blockers.

Metadata normalization

10/10 pts

Metadata quality is high enough to support downstream handoff.

Blockers

  • Secondary RNA assay context should be analyzed separately from ordinary bulk RNA-seq.

Next Steps

  • Review blockers and update sample grouping or dataset selection before analysis.

Analysis Preparation Checklist

5/6 ready

Group labels reviewed

Needs review

Siftome proposed case and control labels, but no reviewer confirmation is stored yet.

Open the sample grouping CSV and confirm group labels before analysis.

Minimum sample count met

Ready

Both groups meet the minimum of 2 samples.

Confirm whether the cohort is large enough for the intended statistical analysis.

Processed counts available

Ready

Processed data availability is marked true in the GEO index.

Verify that downloaded processed files contain usable count or expression identifiers.

Ambiguous samples handled

Ready

No review-needed or unassigned samples are present in the proposed grouping.

Review the grouping table for biologically meaningful differences that the automated checks may not detect.

Exclusion decisions recorded

Ready

No hard-excluded samples were detected by the current rules.

Record any manual exclusions in the downstream analysis notes.

Source traceability preserved

Ready

The dataset and sample accessions can be traced back to GEO.

Keep GEO accessions with exported metadata.

Bioinformatician Review

Bioinformatician review required

Ask a bioinformatician to review grouping, count availability, exclusions, and design before DE.

Analysis readiness has blockers that must be resolved before DE.

Assay context may require expert handling before DESeq2-style analysis.

Why This Is Ranked Here

Tumor vs normal
Exploratory only Dataset can inform exploration but is not strong enough for primary analysis.

Applied Review Decisions

No active review decisions affect this ranking.

Derived Classifications

  • Assay differs from search intent Microarray Search intent expects bulk RNA-seq. Assay came from Normalization rules.
  • Disease context Sepsis Disease context came from Normalization rules.
  • Likely case group Sepsis 215 samples
  • Likely control group Healthy control 83 samples

Intent Interpretation

  • Downgrade or exclusion reason Downgraded for tissue search because it is a biofluid cohort. Derived from current-intent ranking reasons.

Ranking Facts

Supporting evidence

  • Comparison: Patient biofluid vs healthy control. Specimen: Biofluid. Comparison suitability: 10%.
  • Interpreted disease metadata: Infectious disease or host response, Sepsis.
  • Processed data is available for downstream review.
  • External evidence: PubMed links 20 publications; 20 DOIs available: PMID 33305733, PMID 35401666, PMID 35787932, PMID 35903199, PMID 36765335.
  • Publication context: The leukocyte non-coding RNA landscape in critically ill patients with sepsis.. eLife, 2020. PMID 33305733. DOI 10.7554/eLife.58597.
  • Publication context: Identification of Hub Genes With Differential Correlations in Sepsis.. Frontiers in genetics, 2022. PMID 35401666. DOI 10.3389/fgene.2022.876514.
  • Publication context: Contribution of prognostic ferroptosis-related subtypes classification and hub genes of sepsis.. Transplant immunology, 2022. PMID 35787932. DOI 10.1016/j.trim.2022.101660.
  • Publication context: 17 additional linked PubMed publications are available.

Cautions

  • Downgraded for tissue search because it is a biofluid cohort.
  • Downgraded assay: microarray cohorts are separated from the RNA-seq intent.
  • Blood, plasma, or serum cohort downgraded because this ranking targets tissue tumor-vs-normal comparisons.
  • Overall score capped because comparison suitability is weak for the selected search intent.
  • Comparison suitability score is below the Phase 1 recommended threshold.

Dataset Warnings

Normalized Fields

showing 500 of 756 fields

Sample Group Preview

showing 100 of 298 preview rows Show Hide
Study totals: Case 215 | Control 83 | Unassigned 0 | Excluded 0 | Ambiguous 0 showing 100 of 298 preview rows
Group Role Accession Title Source / characteristics Reason
Sepsis Case GSM3942706 2539 Blood Age: 40; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942707 3197090 Blood Age: 33; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Noninfectious Case GSM3942708 3204833 Blood Age: 45; Gender: Male; Disease state: Noninfectious; Tissue: Blood Explicit characteristic comparison arm detected.
Noninfectious Case GSM3942709 3613 Blood Age: 84; Gender: Female; Disease state: Noninfectious; Tissue: Blood Explicit characteristic comparison arm detected.
Sepsis Case GSM3942710 3823 Blood Age: 61; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942711 3976 Blood Age: 62; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Noninfectious Case GSM3942712 4046 Blood Age: 72; Gender: Female; Disease state: Noninfectious; Tissue: Blood Explicit characteristic comparison arm detected.
Sepsis Case GSM3942713 4231 Blood Age: 65; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942714 4295 Blood Age: 40; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942715 4373 Blood Age: 44; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942716 4426 Blood Age: 75; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942717 4471 Blood Age: 52; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942718 4484 Blood Age: 62; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942719 4508 Blood Age: 42; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942720 4546 Blood Age: 57; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942721 4586 Blood Age: 80; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942722 4850 Blood Age: 64; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Noninfectious Case GSM3942723 5230 Blood Age: 73; Gender: Male; Disease state: Noninfectious; Tissue: Blood Explicit characteristic comparison arm detected.
Sepsis Case GSM3942724 5254 Blood Age: 42; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942725 6617 Blood Age: 78; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Noninfectious Case GSM3942726 6620 Blood Age: 77; Gender: Male; Disease state: Noninfectious; Tissue: Blood Explicit characteristic comparison arm detected.
Sepsis Case GSM3942727 6653 Blood Age: 75; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942728 6658 Blood Age: 81; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942729 6746 Blood Age: 81; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942730 6776 Blood Age: 71; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942731 6890 Blood Age: 51; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Noninfectious Case GSM3942732 6892 Blood Age: 46; Gender: Male; Disease state: Noninfectious; Tissue: Blood Explicit characteristic comparison arm detected.
Sepsis Case GSM3942746 3546507 Blood Age: 67; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942747 3585283 Blood Age: 55; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942748 3605989 Blood Age: 76; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942749 3769063 Blood Age: 45; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942750 3783662 Blood Age: 60; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942751 3813755 Blood Age: 34; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942752 3815233 Blood Age: 58; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942753 3823266 Blood Age: 26; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942754 3884620 Blood Age: 51; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942755 3944248 Blood Age: 66; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Noninfectious Case GSM3942756 3945035 Blood Age: 73; Gender: Male; Disease state: Noninfectious; Tissue: Blood Explicit characteristic comparison arm detected.
Sepsis Case GSM3942757 3962538 Blood Age: 39; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Noninfectious Case GSM3942758 3976071 Blood Age: 59; Gender: Male; Disease state: Noninfectious; Tissue: Blood Explicit characteristic comparison arm detected.
Sepsis Case GSM3942759 4017646 Blood Age: 62; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942760 4078051 Blood Age: 79; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942761 4729 Blood Age: 46; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942762 4731 Blood Age: 54; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942763 4874 Blood Age: 66; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Noninfectious Case GSM3942764 4950 Blood Age: 58; Gender: Female; Disease state: Noninfectious; Tissue: Blood Explicit characteristic comparison arm detected.
Sepsis Case GSM3942765 5189 Blood Age: 70; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942766 5261 Blood Age: 72; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942767 5409 Blood Age: 42; Gender: Female; Disease state: Sepsis; Tissue: Blood Case marker detected.
Sepsis Case GSM3942768 5530 Blood Age: 69; Gender: Male; Disease state: Sepsis; Tissue: Blood Case marker detected.
165 additional case samples are hidden from this preview.
Healthy control Control GSM3942733 Hv154 Blood Age: 58; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942734 Hv156 Blood Age: 63; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942735 Hv157 Blood Age: 48; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942736 Hv161 Blood Age: 45; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942737 Hv164 Blood Age: 53; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942738 Hv165 Blood Age: 57; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942739 Hv166 Blood Age: 43; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942740 Hv170 Blood Age: 53; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942741 Hv33 Blood Age: 25; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942742 Hv39 Blood Age: 33; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942743 Hv41 Blood Age: 24; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942744 Hv48 Blood Age: 28; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942745 Hv70 Blood Age: 27; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942773 Hv34 Blood Age: 28; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942774 Hv35 Blood Age: 40; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942775 Hv38 Blood Age: 35; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942776 Hv40 Blood Age: 33; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942777 Hv42 Blood Age: 35; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942778 Hv43 Blood Age: 30; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942779 Hv44 Blood Age: 31; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942780 Hv53 Blood Age: 28; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942781 Hv56 Blood Age: 30; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942782 Hv57 Blood Age: 30; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942783 Hv69 Blood Age: 23; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942813 Hv110 Blood Age: 71; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942814 Hv112 Blood Age: 64; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942815 Hv114 Blood Age: 69; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942816 Hv115 Blood Age: 63; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942817 Hv116 Blood Age: 74; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942818 Hv117 Blood Age: 75; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942819 Hv120 Blood Age: 57; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942820 Hv145 Blood Age: 60; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942821 Hv146 Blood Age: 60; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942822 Hv148 Blood Age: 58; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942823 Hv151 Blood Age: 48; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942824 Hv163 Blood Age: 60; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942825 Hv176 Blood Age: 54; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942887 Hv177 Blood Age: 57; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942888 Hv181 Blood Age: 60; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942889 Hv182 Blood Age: 54; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942890 Hv185 Blood Age: 44; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942891 Hv186 Blood Age: 41; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942892 Hv189 Blood Age: 40; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942893 Hv190 Blood Age: 58; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942894 Hv191 Blood Age: 53; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942895 Hv195 Blood Age: 63; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942896 Hv198 Blood Age: 49; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942897 Hv199 Blood Age: 58; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942898 Hv205 Blood Age: 55; Gender: Male; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
Healthy control Control GSM3942899 Hv207 Blood Age: 61; Gender: Female; Disease state: Healthy; Tissue: Blood Sample-local control marker detected.
33 additional control samples are hidden from this preview.

Use as Discovery Cohort

External Validation Finder

Carry Siftome's disease, organism, specimen, assay, data type, and accession into a validation project. Uncertain fields remain Unknown.