singha53-zz
commented
7 years ago some comments to get you started:
first statement is incorrect: Asthma is characterized by chronic inflammation, and affects over 400 million children and adults worldwide (1).
- the paper states: According to the World Health Organization, the number of patients having asthma is 300 million and with the rising trends it is expected to increase to 400 million, by 2025. --> did you mean by 2025? - My PhD was on allergic asthma so this number caught me by surprise ;)
Furthermore, the generation of interactive networks with WGCNA (6) in the aforementioned study may have removed meaningful connections in an attempt to reduce noise, and also has difficulties incorporating heterogeneous data.
- define WGCNA
- how does WGCNA remove meaningful connections and reduce noise? (I have used WGCNA but didnt know about this, can you provide a citation for this claim?)
RNAseq and methylation data from lung epithelial cells in subjects with and without asthma will be analyzed to determine master regulator genes that initiate the Type II inflammatory response in lung epithelial cells.
- what are master regulator genes? Transcription factors? hubs of networks?
- how many subjects have asthma and how many do not?
To begin our analysis, RNAseq data will be processed to remove the effect of confounding variables, and used to construct a co-expression network. Similarly, we will construct a co-expression network with differentially methylated CpGs (DMCs). Both these networks may give us insights into the genetic and epigenetic signatures that influence variation in asthma endotypes.
- state which methods you will use to remove effects of confounding variables
- you will make a co-expression network? WGCNA does that but you said that removes meaningful connections --> which method will you use?
- asthma endotypes? you are only comparing subjects with and without asthma. Are you also comparing different types of asthma? (e.g. allergic asthma vs. occupational asthma vs. exercise-induced asthma)
These include determining whether methylation levels at DMCs are correlated with expression levels of nearby genes and integrating DMC and RNA-Seq data using a network-interaction based approach.
- state which statistical methodology you will use to answer this question.
Remember to add a table stating the division of labour (e.g. which individuals are involved in the study design, obtaining data, preprocessing data, cleaning data, QC of data, exploratory data analysis, statistical analyses, writing etc).
Happy to discuss further at Wednesday's seminar. Cheers
@santina
Team name: Undecided
One paragraph on the basic idea of the project:
Asthma is characterized by chronic inflammation, and affects over 400 million children and adults worldwide (1). The heterogeneity of the disease manifests as variation in clinical onset, responsiveness to treatment and comorbidities (2). Upstream events in the lung epithelial cells of the lower airway have been postulated to initiate Type II inflammation, which is mediated by CD4+ T cells, leading to cytokine production and remodeling of the cellular environment in the lower airway. Recent studies using RNA-seq data have characterized the Type II immune response in CD4+ T cells; however, the upstream events in epithelial cells that initiate this response remain unknown (3,4). A study recently published, which obtained RNA-Seq and methylation profiles for 76 asthma patients, investigated the genetic and epigenetic markers upregulated in lower airway epithelial cells during asthmatic responses (5). However, the conclusions of the study are limited by numerous confounding factors such as medication usage, comorbidities and artefacts of experimentation, which can obscure the detection of meaningful biological signals. Furthermore, the generation of interactive networks with WGCNA (6) in the aforementioned study may have removed meaningful connections in an attempt to reduce noise, and also has difficulties incorporating heterogeneous data. RNAseq and methylation data from lung epithelial cells in subjects with and without asthma will be analyzed to determine master regulator genes that initiate the Type II inflammatory response in lung epithelial cells. To begin our analysis, RNAseq data will be processed to remove the effect of confounding variables, and used to construct a co-expression network. Similarly, we will construct a co-expression network with differentially methylated CpGs (DMCs). Both these networks may give us insights into the genetic and epigenetic signatures that influence variation in asthma endotypes. We may try other analyses too, if we have time. These include determining whether methylation levels at DMCs are correlated with expression levels of nearby genes and integrating DMC and RNA-Seq data using a network-interaction based approach.
References