General Effect Modelling (GEM) -- Part 3. GEM applied on proteome data of cerebrospinal fluid of multiple sclerosis and clinically isolated syndrome
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914741133246464 |
|---|---|
| author | Mosleth, Ellen Færgestad Myhr, Kjell-Morten Vedeler, Christian Alexander Berven, Frode Steingrimsen Lysenko, Artem Gavasso, Sonia Liland, Kristian Hovde |
| author_facet | Mosleth, Ellen Færgestad Myhr, Kjell-Morten Vedeler, Christian Alexander Berven, Frode Steingrimsen Lysenko, Artem Gavasso, Sonia Liland, Kristian Hovde |
| contents | The novel data analytical platform General Effect Modelling (GEM), is an umbrella platform covering different data analytical methods that handle data with multiple design variables (or pseudo design variables) and multivariate responses. GEM is here demonstrated in an analysis of proteome data from cerebrospinal fluid (CSF) from two independent previously published datasets, one data set comprised of persons with relapsing-remitting multiple sclerosis, persons with other neurological disorders and persons without neurological disorders, and one data set had persons with clinically isolated syndrome (CIS), which is the first clinical symptom of MS, and controls. The primary aim of the present publication is to use these data to demonstrate how patient stratification can be utilised by GEM for multivariate analysis. We also emphasize how the findings shed light on important aspects of the molecular mechanism of MS that may otherwise be lost. We identified proteins involved in neural development as significantly lower for MS/CIS than for their respective controls. This information was only seen after stratification of the persons into two groups, which were found to have different inflammatory patterns and the utilisation of this by GEM. Our conclusion from the study of these data is that disrupted neural development may be an early event in CIS and MS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03034 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | General Effect Modelling (GEM) -- Part 3. GEM applied on proteome data of cerebrospinal fluid of multiple sclerosis and clinically isolated syndrome Mosleth, Ellen Færgestad Myhr, Kjell-Morten Vedeler, Christian Alexander Berven, Frode Steingrimsen Lysenko, Artem Gavasso, Sonia Liland, Kristian Hovde Methodology Applications The novel data analytical platform General Effect Modelling (GEM), is an umbrella platform covering different data analytical methods that handle data with multiple design variables (or pseudo design variables) and multivariate responses. GEM is here demonstrated in an analysis of proteome data from cerebrospinal fluid (CSF) from two independent previously published datasets, one data set comprised of persons with relapsing-remitting multiple sclerosis, persons with other neurological disorders and persons without neurological disorders, and one data set had persons with clinically isolated syndrome (CIS), which is the first clinical symptom of MS, and controls. The primary aim of the present publication is to use these data to demonstrate how patient stratification can be utilised by GEM for multivariate analysis. We also emphasize how the findings shed light on important aspects of the molecular mechanism of MS that may otherwise be lost. We identified proteins involved in neural development as significantly lower for MS/CIS than for their respective controls. This information was only seen after stratification of the persons into two groups, which were found to have different inflammatory patterns and the utilisation of this by GEM. Our conclusion from the study of these data is that disrupted neural development may be an early event in CIS and MS. |
| title | General Effect Modelling (GEM) -- Part 3. GEM applied on proteome data of cerebrospinal fluid of multiple sclerosis and clinically isolated syndrome |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2404.03034 |