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        / /_/  >  | \// __ \|  |  / /_/ | |  ||  |  
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              grep rough audit - static analysis tool
                  v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
r-bioc-monocle-2.18.0/actual_vignette_holder/monocle-vignette-original.Rnw-1342-	
r-bioc-monocle-2.18.0/actual_vignette_holder/monocle-vignette-original.Rnw:1343:The dpFeature procedure works as follows. First, dpFeature excludes genes that only expressed in a very small percentage of cells (by default, $5\%$). Second, dpFeature performs PCA on the remaining genes in order to identify the principal components that explain a substantial amount of variance in the data. These top PCs are then used to initialize t-SNE, which projects the cells into two-dimensional t-SNE space. Next, dpFeature uses a recently developed clustering algorithm, called ``density peak'' clustering \cite{Rodriguez2014-rl} to cluster the cells in the two-dimensional t-SNE space. The density peak clustering algorithm calculates each cell's local density ($\rho$) and its distance ($\delta$) to another cell with higher density. The $\rho$ and $\delta$ values for each cell can be plotted in a so-called ``decision plot'' in order to select thresholds that define ``peaks'' in the t-SNE space. Cells with high local density that are far away from other cells with high local density correspond to the density peaks. These density peaks nucleate clusters: all other cells will be associated with the nearest density peak cell. Finally, we identify genes that differ between the clusters by performing a likelihood ratio test between using a generalized linear model that knows the cluster to which each cell is assigned and a model that doesn't. We then select (by default) the top 1,000 significantly differentially expressed genes as the ordering genes for the trajectory reconstruction. 
r-bioc-monocle-2.18.0/actual_vignette_holder/monocle-vignette-original.Rnw-1344-	
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r-bioc-monocle-2.18.0/vignettes/monocle-vignette-knitr.tex-2151-	
r-bioc-monocle-2.18.0/vignettes/monocle-vignette-knitr.tex:2152:The dpFeature procedure works as follows. First, dpFeature excludes genes that only expressed in a very small percentage of cells (by default, $5\%$). Second, dpFeature performs PCA on the remaining genes in order to identify the principal components that explain a substantial amount of variance in the data. These top PCs are then used to initialize t-SNE, which projects the cells into two-dimensional t-SNE space. Next, dpFeature uses a recently developed clustering algorithm, called ``density peak'' clustering \cite{Rodriguez2014-rl} to cluster the cells in the two-dimensional t-SNE space. The density peak clustering algorithm calculates each cell's local density ($\rho$) and its distance ($\delta$) to another cell with higher density. The $\rho$ and $\delta$ values for each cell can be plotted in a so-called ``decision plot'' in order to select thresholds that define ``peaks'' in the t-SNE space. Cells with high local density that are far away from other cells with high local density correspond to the density peaks. These density peaks nucleate clusters: all other cells will be associated with the nearest density peak cell. Finally, we identify genes that differ between the clusters by performing a likelihood ratio test between using a generalized linear model that knows the cluster to which each cell is assigned and a model that doesn't. We then select (by default) the top 1,000 significantly differentially expressed genes as the ordering genes for the trajectory reconstruction. 
r-bioc-monocle-2.18.0/vignettes/monocle-vignette-knitr.tex-2153-	
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r-bioc-monocle-2.18.0/debian/tests/run-unit-test-5-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-bioc-monocle-2.18.0/debian/tests/run-unit-test:6:    AUTOPKGTEST_TMP=`mktemp -d /tmp/${debname}-test.XXXXXX`
r-bioc-monocle-2.18.0/debian/tests/run-unit-test-7-    trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM