new version

1 files changed, 35 insertions, 15 deletions

diff --git a/deliverable/main.tex b/deliverable/main.tex
index bfe5bbe..c8be8ac 100644
--- a/deliverable/main.tex
+++ b/deliverable/main.tex
@@ -231,8 +231,11 @@ However, including only the donors for which a vaccine response classification w
 
 \begin{figure}[htpb]
     \includegraphics[width=\textwidth]{demographic}
-    \caption{\textbf{A.} percentage of donors/rows having some Gender, Ethnicity, or \gls{bu:cmv} status within high and low responder groups.
-    \textbf{B.} Age distribution of donors with available response classification.}\label{fig:demoGraph}
+    \caption{
+        \textbf{Demographic attribute distributions and age distribution.}
+        \textbf{A.} percentage of donors/rows having some Gender, Ethnicity, or \gls{bu:cmv} status within high and low responder groups.
+        \textbf{B.} Age distribution of donors with available response classification.
+    }\label{fig:demoGraph}
 \end{figure}
 
 
@@ -261,7 +264,7 @@ Other (\%) & 121  ( 32.5 )\\
 Unknown (\%) & 2  ( 0.5 )\\
 \bottomrule{}
 \end{tabular}
-\caption{\textbf{Demographic statistics of donors with known vaccine response classification.}}\label{tbl:demoStats}
+\caption{Demographic statistics of donors with known vaccine response classification.}\label{tbl:demoStats}
 \end{table}
 
 The data from the clinical studies consisted of 121 CSV files that were imported into the \flup database.
@@ -489,7 +492,13 @@ Those 14 assays have been aggregated in this work to 5 different data types/expe
 
 \begin{figure}[htpb]
     \includegraphics[width=\textwidth]{assay_value_distributions}
-    \caption{noise in 90th \%tile}\label{fig:assayDistr}
+    \caption{
+        \textbf{Distributions of experimental data values.}
+        In this work we grouped experiments into five datatypes, however the phopho(rylation) cytometry data was measured in two different experiments and thus had two different units.
+        The process that is measured is the same between the assays, only the experiment differs.
+        As a result, six different distributions are shown, one for each unit of measurement in the \flup database.
+        Importantly, there were outlier values for the phosphorylation flow cytometry 90th percentile values that were removed to show the overall distribution.
+    }\label{fig:assayDistr}
 \end{figure}
 
 The experimental data table contains all features recorded per donor visit.
@@ -603,8 +612,9 @@ Finally, the datasets were split into train (75\%) and test (25\%) sets, and dat
 20 & 83 x 75 & 56 / 27 ( 0.67 ) & 42 / 21 & 14 / 6\\
 \bottomrule{}
 \end{tabularx}
-    \caption{Datasets generated by applying the mulset algorithm on the \simon
-    \firstvis, and the balanced train test split that was performed.}\label{tbl:mulsetDatasets}
+    \caption{
+        Datasets generated by applying the mulset algorithm on the \firstvis also used in \spaper, and the balanced train test splits that were performed.
+    }\label{tbl:mulsetDatasets}
 \end{table}
 
 A significant number of datasets contained more predictors than samples \autoref{tbl:mulsetDatasets}.
@@ -714,31 +724,41 @@ Lastly, we also calculated the correlation between all features in dataset 14 an
     \centering
     \includegraphics[width=\textwidth]{dataset1_nb_feature_exploration}
     \caption{
-        dataset1-nb-feature-exploration
+        \textbf{Exploration of selected features on dataset 14.}
+        \textbf{A.} Features with a variable importance contribution score greater than 50.
+        \textbf{B.} Distributions of top 3 most important features grouped by vaccine response classification.
+        Thin horizontal bars show the median value.
+        \textbf{C.} Values of the same features as in \textbf{B} compared to their value in \secondvis.
+        Donors/rows that changed classification between their first and second visit are indicated as enlarged diamonds.
     }\label{fig:dataset1-nb-feature-exploration}
 \end{figure}
 
 Firstly, the top ranked feature in dataset 14 was the phosphorylated \gls{bu:stat} transcription factor in unstimulated \gls{bu:bcell}s \autorefsub{fig:dataset1-nb-feature-exploration}{A}.
 However, the difference in the value of this feature between the high and low vaccine responders was not found to be significant (at FDR $<$ 0.01) \autorefsub{fig:dataset2-nb-feature-exploration}{B}.
-In contrast, the other two features, IFNg stimulated \gls{bu:bcell} phosphorylated \gls{bu:stat} and \gls{bu:cd4pos} phosphorylated STAT5, were found to be significantly greater in the high responder group (FDR $<$ 0.01).
+In contrast, the other two features, IFNg stimulated \gls{bu:bcell} phosphorylated \gls{bu:stat} and \gls{bu:cd4pos} phosphorylated \gls{bu:stat}5, were found to be significantly greater in the high responder group (FDR $<$ 0.01).
 A correlation analysis of all features showed that different \gls{bu:stat} protein formed positively correlated clusters as expected \autoref{fig:cor-dataset1} (p \(<\) 0.0001).
 Further, the most important feature had slight negative correlations (pearson's r from -0.2 to -0.5) to a set of stimulated \gls{bu:stat} cell responses (p \(<\) 0.0001 after BH adjustment).
 The second most important feature had similar correlations as the first, likely since they are both \gls{bu:bcell} \gls{bu:stat} features.
 Lastly, the unstimulated \gls{bu:cd4pos} \gls{bu:stat} phosphorylation also belonged in the same cluster as the previous \gls{bu:bcell} features.
-These correlations might indicate an interaction pattern between \gls{bu:stat} and STAT1 phosphorylation in different cell types in response to a vaccine.
+These correlations might indicate an interaction pattern between \gls{bu:stat} and \gls{bu:stat}1 phosphorylation in different cell types in response to a vaccine.
 
 \begin{figure}[htpb]
     \centering
     \includegraphics[width=\textwidth]{dataset2_nb_feature_exploration}
     \caption{
-        dataset2-nb-feature-exploration
+        \textbf{Exploration of selected features on dataset 14.}
+        \textbf{A.} Features with a variable importance contribution score greater than 50.
+        \textbf{B.} Distributions of top 3 most important features grouped by vaccine response classification.
+        Thin horizontal bars show the median value.
+        \textbf{C.} Values of the same features as in \textbf{B} compared to their value in \secondvis.
+        Donors/rows that changed classification between their first and second visit are indicated as enlarged diamonds.
     }\label{fig:dataset2-nb-feature-exploration}
 \end{figure}
 
 In dataset 16 there were only four features that had a variable importance score greater than 50 \autorefsub{fig:dataset2-nb-feature-exploration}{A}.
-The top two features were phospohorylated \gls{bu:stat} in unstimulated \gls{bu:bcell} and phosphorylated STAT1 in unstimulated \gls{bu:cd8pos}.
+The top two features were phospohorylated \gls{bu:stat} in unstimulated \gls{bu:bcell} and phosphorylated \gls{bu:stat}1 in unstimulated \gls{bu:cd8pos}.
 However, only the \gls{bu:bcell} feature was found to be significantly greater in the positive class (FDR \(< 0.01\)) \autorefsub{fig:dataset2-nb-feature-exploration}{B}.
-The \gls{bu:bcell} \gls{bu:stat} feature correlated positively with both unstimulated \gls{bu:cd4pos} and \gls{bu:cd8pos} STAT1 phosphorylation (pearson's r= 0.7 and 0.4, p \(< 0.001\)), and there were mild negative correlations with interferon gamma stimulated \gls{bu:monocyte} STAT3 and STAT5 phosphorylation (pearson's r= 0.3 and 0.2, p \(< 0.001\)) \autoref{fig:cor-dataset2}.
+The \gls{bu:bcell} \gls{bu:stat} feature correlated positively with both unstimulated \gls{bu:cd4pos} and \gls{bu:cd8pos} \gls{bu:stat}1 phosphorylation (pearson's r= 0.7 and 0.4, p \(< 0.001\)), and there were mild negative correlations with interferon gamma stimulated \gls{bu:monocyte} \gls{bu:stat}3 and \gls{bu:stat}5 phosphorylation (pearson's r= 0.3 and 0.2, p \(< 0.001\)) \autoref{fig:cor-dataset2}.
 
 \subsection{Repeat vaccination effect on identified features}
 
@@ -755,11 +775,11 @@ These were left out of visualisations, since outliers made the pattern unclear a
 To see how a repeat vaccination affects immune cell signaling, the distribution of the top three features of dataset 14 were compared to their distribution when measured in a subsequent influenza season \autorefsub{fig:dataset1-nb-feature-exploration}{C}.
 In the 21 donors that had a second measurement of the features in another influenza season that were not left out (outliers and nonsensical values) there was the consistent pattern that the high responders were classified as low responders in their second visit \autorefsub{fig:dataset1-nb-feature-exploration}{C}.
 Although, overall the feature values were consistently greater in the \secondvis \autorefsub{fig:dataset1-nb-feature-exploration}{C, enlarged diamonds}.
-Thus, vaccination might increase activity in general signaling pathways of PBMC in subsequent influenza seasons, but the classification does not reflect this as increasing influenza antibody response.
+Thus, vaccination might increase activity in general signaling pathways of \gls{bu:pbmc} in subsequent influenza seasons, but the classification does not reflect this as increasing influenza antibody response.
 One possibility is that the donor was classified as low responder due to a lack of response to one strain of virus in the vaccine administered in the repeat visit, not necessarily to all strains \autoref{fig:classInconsistent}.
 
 To explore the overall change in the features of dataset 14 between the first and subsequent in influenza seasons the distribution of changes for donors were visualised and ordered by mean of log2 change (negative values were removed) \autoref{fig:second-visit-change1}.
-The overall trend that appeared was that the unstimulated PBMCs had higher values upon a repeated visit.
+The overall trend that appeared was that the unstimulated \gls{bu:pbmc}s had higher values upon a repeated visit.
 And, in general \gls{bu:stat} features increased in value. The values that contributed the most to the model discriminating between high and low responders in the \firstvis also increased the most in a repeat visit.
 Although, there are outliers that increased a lot in the subsequent influenza season \autoref{fig:second-visit-change1}.
 
@@ -1022,7 +1042,7 @@ rest of the columns.}
             CyTOF phenotyping & 4 \\
             HAI & 5 \\
             Human Luminex 51 plex & 6 \\
-            Phospho-flow cytokine stim (PBMC) & 7 \\
+            Phospho-flow cytokine stim (\gls{bu:pbmc}) & 7 \\
             pCyTOF (whole blood) pheno & 9 \\
             pCyTOF (whole blood) phospho & 10 \\
             CBCD & 11 \\