0000000000087666
AUTHOR
Khadija El Amrani
showing 8 related works from this author
Evaluating Cell Identity from Transcription Profiles
2018
SummaryInduced pluripotent stem cells (iPS) and direct lineage programming offer promising autologous and patient-specific sources of cells for personalized drug-testing and cell-based therapy. Before these engineered cells can be widely used, it is important to evaluate how well the engineered cell types resemble their intended target cell types. We have developed a method to generate CellScore, a cell identity score that can be used to evaluate the success of an engineered cell type in relation to both its initial and desired target cell type, which are used as references. Of 20 cell transitions tested, the most successful transitions were the iPS cells (CellScore > 0.9), while other t…
Additional file 1 of MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression data
2015
Literature-curated marker genes. This file includes marker genes collected from the literature. (104KB PDF)
Additional file 5 of MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression data
2015
Primer sequences. This file includes the list of all primer sequences used by PCR. (55.7KB PDF)
Additional file 2 of MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression data
2015
Plots of Precision/Recall comparing our method to t -test. This file includes Plots of Precision/Recall comparing MGFM to t-test. (462KB PDF)
Additional file 4 of MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression data
2015
Description of the predicted marker genes. (126KB PDF)
Detection of condition-specific marker genes from RNA-seq data with MGFR
2019
The identification of condition-specific genes is key to advancing our understanding of cell fate decisions and disease development. Differential gene expression analysis (DGEA) has been the standard tool for this task. However, the amount of samples that modern transcriptomic technologies allow us to study, makes DGEA a daunting task. On the other hand, experiments with low numbers of replicates lack the statistical power to detect differentially expressed genes. We have previously developed MGFM, a tool for marker gene detection from microarrays, that is particularly useful in the latter case. Here, we have adapted the algorithm behind MGFM to detect markers in RNA-seq data. MGFR groups s…
MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression data
2015
Background Identification of marker genes associated with a specific tissue/cell type is a fundamental challenge in genetic and cell research. Marker genes are of great importance for determining cell identity, and for understanding tissue specific gene function and the molecular mechanisms underlying complex diseases. Results We have developed a new bioinformatics tool called MGFM (Marker Gene Finder in Microarray data) to predict marker genes from microarray gene expression data. Marker genes are identified through the grouping of samples of the same type with similar marker gene expression levels. We verified our approach using two microarray data sets from the NCBI’s Gene Expression Omn…
Additional file 3 of MGFM: a novel tool for detection of tissue and cell specific marker genes from microarray gene expression data
2015
Gel electrophoresis images. This file includes the gel electrophoresis images (Figures S1â S11). (981KB PDF)