Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

71ce29d8-7b35-4239-932b-13346d3dea8c - samples

ABSTRACT: To model recovery dynamics, using severe COVID-19 as the example, we align heterogeneous recovery trajectories via a novel computational scheme applied to longitudinally sampled blood transcriptomes. We thus generate pseudotime trajectories, which we then link to cellular and molecular mechanisms based on cell deconvolution analysis and molecular pathway prediction, thus presenting a unique framework for studying recovery processes over time.

PROVIDER: EGAD00001008331 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

Unifying recovery dynamics in heterogeneous diseases exemplified by COVID-19

Project description:Studying the recovery dynamics in different diseases poses many difficulties, as patients often display high heterogeneity in their recovery courses. Moreover, most attempts to study disease dynamics focus on disease progression rather than disease recovery mechanisms. To model recovery dynamics, using severe COVID-19 as the example, we align heterogeneous recovery trajectories via a novel computational scheme applied to longitudinally sampled blood transcriptomes. We thus generate pseudotime trajectories, which we then link to cellular and molecular mechanisms based on cell deconvolution analysis and molecular pathway prediction, thus presenting a unique framework for studying recovery processes over time. Specifically, mature neutrophils displayed a gradual decrease during recovery, allowing superior useability for outcome prediction compared to currently used clinical markers. Further, we discovered a recovery-related regulatory change in gene programs resulting in immune rebalancing between interferon and NFkB activity and the restoration of cell homeostasis. We thus propose regulatory mechanisms governing COVID-19 recovery and suggest mature neutrophils and additional gene markers, as novel clinical biomarkers for disease outcome. Overall, we present a novel clinically relevant computational framework for modeling disease recovery, paving the way for future studies of the recovery dynamics in other diseases and tissues.

| EGAS00001005735 | EGA

Transcription profiling of peripheral blood leukocyte genomic response one day post traumatic injury may predict early respiratory recovery

Project description:This data was used as an example to illustrate a computational method for assessing statistical significance in microarray experiments Contributed by 'The Inflammation and the Host Response to Injury Collaborative Research Program.' Keywords: Two group comparison Genomic response one day post traumatic injury was compared between patients having early or late respiratory recovery

2011-12-01 | E-GEOD-13488 | biostudies-arrayexpress

Antibody repertoire sequencing of blood and bone marrow plasma cell compartments following lymphocytic choriomeningitis virus infection

Project description:Antibody repertoire sequencing of blood and bone marrow plasma cell compartments following lymphocytic choriomeningitis virus infection. Heavy chain repertoires of 5 uninfected mice, 5 acutely infected mice and 5 chronically infected mice were longitudinally sequenced over 80 days. All samples are named according to the following scheme: dpi#_cohort$_R&.fastq.gz . dpi = days post infection = for all samples following the start of infection. Those samples starting with dm10 correspond to blood samples taken before the introduction of any virus. # = time point post virus infection cohort=acute (LCMV), chronic (LCMV), or naive (uninfected) animals $= mouse number, 1-5 R&=paired read, either R1 or R2 Those samples with \"bmpc\" in title correspond to sorted plasma cells (CD138hi, CD19lo) from bone marrow Example: dpi70_chronic3_R2.fastq.gz == blood repertoire 70 days post chronic LCMV infection in chronically-infected mouse#3

2020-01-16 | E-MTAB-8585 | biostudies-arrayexpress

eRah: A computational tool integrating spectral deconvolution and alignment with quantification and identification of metabolites in GCMS- based metabolomics

Project description:Gas chromatography coupled to mass spectrometry (GC-MS) has been a long- standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex datasets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information of metabolites across multiple biological samples, integrated computational workflows for data processing are needed. Here we introduce eRah, a free computational tool written in the open language R composed of five core functions: (i) noise filtering and baseline removal of GC-MS chromatograms, (ii) an innovative compound deconvolution process using multivariate analysis techniques based on compound match by local covariance (CMLC) and orthogonal signal deconvolution (OSD), (iii) alignment of mass spectra across samples, (iv) missing compound recovery, and (v) identification of metabolites by spectral library matching using publicly available mass spectra. eRah outputs a table with compound names, matching scores and the integrated area of compounds for each sample. The automated capabilities of eRah are demonstrated by the analysis of GC-TOF MS data from plasma samples of adolescents with hyperinsulinaemic androgen excess and healthy controls. The quantitative results of eRah are compared to centWave, the peak-picking algorithm implemented in the widely used XCMS package, MetAlign and ChromaTOF software. Significantly dysregulated metabolites are further validated using pure standards and targeted analysis by GC-QqQ MS, LC-QqQ and NMR.

2016-09-22 | MTBLS321 | MetaboLights

Expression profiling of CD8+ T-cells in peripheral blood (PB) of AML patients

Project description:1) Transcriptional profiles of highly purified (≥ 98%) PB CD8+ T-cells from newly diagnosed AML patients (pre-treatment) were compared to age-matched healthy controls and transcriptional profiles of PB AML CD8+ T-cells from patients who achieved complete remission (CR) versus non-responders (NR) longitudinally, at pre-treatment and upon recovery following induction chemotherapy (paired samples). 2) Transcriptional profiles of PB AML CD8+ T-cells from patients who achieved complete remission (CR) versus non-responders (NR) longitudinally, at pre-treatment (PRE) and upon recovery following induction chemotherapy (POST) (paired samples).

2018-12-26 | GSE109179 | GEO

Transcriptome analysis of time couse of leafy spurge infection with Xam

Project description:The goal of this study was to identify signaling processes associated with infection and recovery of leafy spurge inoculated with Xanthomonas axonopodis pv. manihotis (Xam). Two dye-swap technical replicates for each of 3 biological replicates for each treatment were analyzed. A time course analysis of Xam infected and mock inoculated leafy spurge was performed. Time points were 1, 7 and 21 days after inoculation (or mock inoculation). Thus, 36 labeled samples were hybridized in a 2 dye rolling circle hybridization scheme to 18 arrays.

2010-06-24 | E-GEOD-15751 | biostudies-arrayexpress

A novel computational complete deconvolution method using RNA-seq data

Project description:A novel computational complete deconvolution method using RNA-seq data

| PRJNA509361 | ENA

cfDNA methylation data of longitudinally collected blood plasma samples from patients with esophageal adenocarcinoma

Project description:This study aimed to evaluate the cost-effective and genome-wide cell-free reduced representation bisulfite sequencing (cfRRBS) method combined with computational deconvolution for effective disease monitoring in patients with esophageal adenocarcinoma (EAC). cfDNA methylation profiling with cfRRBS was performed on 162 blood plasma samples from 33 EAC cancer patients and 28 blood plasma samples from 20 healthy donors. In addition, for reproducibility testing purposes of the method, 9 plasma samples were re-prepped (library was re-made) and re-sequenced once (n=9) or twice (n=1). As a reference for the data deconvolution cfRRBS was performed on 7 EAC tumor tissue (FFPE) samples.

2024-12-04 | E-MTAB-14370 | biostudies-arrayexpress

Tracking Early Mammalian Organogenesis – Prediction and Validation of Differentiation Trajectories at Whole Organism Scale

Project description:This project is based on the extension of the single cell transcriptomics atlas of mouse development towards E9.5. Inference of cell differentiation trajectories was carried out using Waddington-Optimal Transport, and the resulting computational reconstruction was contrasted with grafting experiments as well as complementary scRNA-seq experiments.

2023-01-01 | E-MTAB-11763 | biostudies-arrayexpress

Molecular Basis of Anderson Phage Typing Scheme

Project description:Molecular Basis of Anderson Phage Typing Scheme

| PRJEB48030 | ENA