Update proposals.html

proposals.html

@@ -92,28 +92,6 @@ <h6 align="justify">
 					<hr style="width:100%;text-align:left;margin-left:0">
 				</div>
 
-				<div class="row">
-					<div class="span3">
-						<span class="byline"><p style="font-size:23px"><strong><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MSCThesis_compositional_software.pdf">Integration and visualization of compositional cell population data</a></strong></p></span>
-						<h6 align="justify">
-							High-throughput RNA sequencing technologies, such as 16S rRNA and single-cell 
-                            RNA sequencing, allow us to determine the type of every cell or microorganism 
-                            in a biological sample. Unfortunately, different technologies use different 
-                            processing pipelines and data formats on the gene level, often limiting 
-                            implementations of novel analysis methods on the cell level to one pipeline. 
-                            Also, visualization of such data must be done carefully due to its 
-                            compositional nature. <br>
-                            The goal of this M.Sc. Thesis is to develop a data integration and 
-                            visualization package in Python to allow for unified analysis of these 
-                            types of data. You can read the full proposal <strong><a href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MSCThesis_compositional_software.pdf" target="_blank">here</a></strong>
-						</h6>.
-					</div>
-				</div>
-
-				<div class="row">
-					<hr style="width:100%;text-align:left;margin-left:0">
-				</div>
-
 				<div class="row">
 					<div class="span3">
 						<span class="byline"><p style="font-size:23px"><strong><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_microbiome_cardiovascular.pdf">Learning graphical models to explore the relationship between human gut microbiota and cardiovascular diseases</a></strong></p></span>
@@ -196,26 +174,6 @@ <h6 align="justify">
 					<hr style="width:100%;text-align:left;margin-left:0">
 				</div>
 
-				<div class="row">
-					<div class="span3">
-						<span class="byline"><strong><p style="font-size:23px"><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MSCThesis_scCODA_extension.pdf">Bayesian modeling of high-throughput sequencing data</a></strong></p></span>
-						<h6 align="justify">
-							One of the products of our group is scCODA. scCODA is a Bayesian model 
-							can identify compositional changes of cell types. The scCODA model was 
-							specifically designed for use in very low-dimensional settings, 
-							where some properties of high- throughput sequencing data 
-							like zero-inflation and overdispersion are less pronounced. <br>
-							The goal of this M.Sc. Thesis is to extend this framework, so that it can be 
-							used with higher-dimensional data.
-							You can read the full proposal <strong><a href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MSCThesis_scCODA_extension.pdf" target="_blank">here</a></strong>
-						</h6>.
-					</div>
-				</div>
-
-				<div class="row">
-					<hr style="width:100%;text-align:left;margin-left:0">
-				</div>
-
 				<div class="row">
 					<div class="span3">
 						<span class="byline"><strong><p style="font-size:23px"><a target="_blank" href="https://github.com/bio-datascience/bio-datascience.github.io/blob/master/msc_proposals/MScProposal_alpha.pdf">Alpha diversity measures for microbiome data</a></strong></p></span>