Transcriptomic And Cell Morphological Profiles - 886 Words

Step I: At this step, we use LINCS online resources to provide transcriptomic and cell morphological profiles of drugs and small compound molecules as a back-end data for our proposed pipeline. Step II: Mapping the query transcriptomic profile against the LINCS repository. We compare the query signature of expressions for Landmark Genes measured by L1000 assay and the reference transcriptomic profiles to find the drugs and small compound molecules that can potentially mimic the gene expression pattern of the query. Step III: Assessing the enrichment of genes and cell morphological phenotypes. We apply cell morphology enrichment analysis to identify genes that are likely to impact cell morphology features, and produce a set of†¦show more content†¦\textit{CellProfiler} software was applied to process images and calculate samples aggregation for features in response to a compound treatment \cite{40}. Gene expression profiles for drug perturbations were obtained from the lincscloud.org, and the strongest signatures were selected based on the meta-data \cite{4}. The expression signatures represent the expression values for 978 landmark genes upon treatment with compound molecules. The direction of regulation for each landmark gene was assigned based on the comparison of expression level in response to the treatment with a compound and DMSO \cite{4}. We marked the drugs and small compound molecules in the intersection of transcriptomic and cell morphological profiles, and collectively, transcriptomic ($T$) and cell morphological ($C$) profiles of 9515 drugs and small compound molecules were provided as a back-end data for our proposed pipeline. Finally, we standardized the transcriptomic and cell morphological profiles via \textit{unitization with zero minimum} ($\frac{x-minimum}{range}$), where $x$ is a non-missing values in a vector of numerical values representing changes of a variable. \begin{eqnarray*} T\hspace{50pt} \hspace{10pt}C \\ \begin{bmatrix} t_{1,1} \cdots \ t_{1,978} \\ \vdots \ddots \ \vdots \\ t_{9515,1} \ \cdots t_{9515,978} \end{bmatrix} , \begin{bmatrix} C_{1,1} \cdots \ C_{1,812} \\ \vdots \ddots \ \vdots \\ C_{9515,1} \ \cdots t_{812,978} \end{bmatrix}Show MoreRelatedEssay On Cell Morphology1260 Words   |  6 Pageschanges in response to perturbations with alterations in cell morphological features. The proposed approach for cell morphology enrichment analysis is composed of five main steps which we briefly describe here, illustrate in Figure 1, and discuss in detail in the rest of the Materials and Methods section. \begin{figure*}[t] \begin{center} \includegraphics[width=0.8\textwidth]{Figure1.png} \end{center} \caption{Overview of the proposed approach for cell morphology enrichment analysis. The input data areRead MoreImplementation Analysis : Cell Morphological Enrichment Analysis1665 Words   |  7 Pagesan R package, called \textit{CMEA} (Cell Morphological Enrichment Analysis). \textit{CMEA} is available through the Bioconductor repository (https://github.com/isarnassiri/CMEA). The \textit{qgraph} package is used for visualization of results. All analysis was performed using the computing environment R (R version 3.3.1) \cite{26}. \section{RESULTS} \subsection{Validation} \subsection{Modeling of cell morphological features based on the transcriptomic profile} We applied the least absolute shrinkageRead MoreWhat Is Canonical Correlation Analysis1139 Words   |  5 Pages LASSO-based predictions outperformed predictions based on Canonical correlation analysis (CCA) and modeling of changes in $CM$ features of a query based on means of $CM$ profiles of similar drugs (MEAN) as alternative methods, and was able to model multivariate responses where the sample size was smaller than the total number of variables (Table 1) (Supplementary Figures 1-23) \cite{27}. The full results of these assessments can be seen in Supplementary Tables 1. We took a random sample of size