Journal: bioRxiv

Article Title: Identification Drug Targets for Oxaliplatin-Induced Cardiotoxicity without Affecting Cancer Treatment through Inter Variability Cross-Correlation Analysis (IVCCA)

doi: 10.1101/2024.02.11.579390

Figure Lengend Snippet: Data is processed through a traditional pipeline of RNA-seq data preprocessing and differential expression genes (DEGs) extraction using specific filter parameters such as False Discovery Rate (FDR) <0.05 and fold change (FC) >1.5. The data are utilized to construct a correlation matrix, its correlation heatmap is generated to visualize DEGs’ correlation distribution. For further analysis, the absolute values of the correlations are ordered. The sorted heatmap aids in the visualization of the top genes. Clustering is performed using Dendrogram, Principal Component Analysis (PCA), and t-distributed Stochastic Neighbor Embedding (t-SNE) methods, followed by distance thresholding (for the Dendrogram results) or K-means (for the PCA and t-SNE results) for finer clustering. Clusters were analyzed via STRING or network analysis to identify potential target genes. All pathways, including generated and existing ones from databases like GO and KEGG, are quantitatively compared using novel indices and ranked for relevance.

Article Snippet: Our implemented t-distributed Stochastic Neighbor Embedding (t-SNE) toolbox that performs t-SNE calculations ( ) uses the results from the correlation matrix and presents the genes based on their correlation values in 3D using a built-in ‘ tsne ’ function implemented in MATLAB.

Techniques: RNA Sequencing Assay, Expressing, Extraction, Construct, Generated

Journal: Neuron

Article Title: Assemblies of Perisomatic GABAergic Neurons in the Developing Barrel Cortex

doi: 10.1016/j.neuron.2019.10.007

Figure Lengend Snippet: KEY RESOURCES TABLE

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Rabbit polyclonal anti-GABA Sigma-Aldrich Cat#: A2052; RRID:AB_477652 Guinea pig polyclonal anti-GAD2 Synaptic Systems Cat#: 198104; RRID: AB_10557995 Guinea pig polyclonal anti-VGluT2 Merck Millipore Cat#: AB2251-I; RRID:AB_2665454 Mouse monoclonal anti-Syt2 Developmental Studies Hybridoma Bank Cat#: A2315626; RRID: AB_2315226 Bacterial and Virus Strains AAV1.syn.GCaMP6s.WPRE.SV40 UPenn viral core Cat#: 100843 Experimental Models: Organisms/Strains Swiss Webster (CFW) C.E Janvier SW-F/ SW-M Gad67.Cre a gift from H. Monyer Heidelberg University N/A RCL-GCaMP6s (Ai96) Jackson Laboratories 02 4106 SST.Cre a gift from N. Heintz, Rockefeller University N/A Lhx6.Cre Jackson Laboratories 026555 5HT3aR.Cre a gift from N. Heintz, Rockefeller University N/A GAD67-GFP-KI a gift from K. Obata Department of Morphological Brain Science, Kyoto N/A 5-HT3aR-GFP-KI a gift from G. Fishell Harvard University N/A Software and Algorithms CaImAn Pnevmatikakis et al., 2016 ; Giovannucci et al., 2018 https://github.com/flatironinstitute/CaImAn-MATLAB NoRMCorre Pnevmatikakis and Giovannucci, 2017 https://github.com/flatironinstitute/NoRMCorre Iterative K-means clustering Malvache et al., 2016 http://gitlab.com/cossartlab t-SNE Van Der Maaten and Hinton, 2008 https://lvdmaaten.github.io/tsne/#implementations Open in a separate window KEY RESOURCES TABLE In the developing barrel cortex, GABAergic neurons form functional assemblies Prospective parvalbumin interneurons segregate into spatially clustered assemblies Somatostatin interneurons display broadly correlated firing GABA assemblies are regulated by sensory inputs

Techniques: Virus, Software

Journal: Methods in enzymology

Article Title: Characterization of the tumor immune infiltrate by multiparametric flow cytometry and unbiased high-dimensional data analysis

doi: 10.1016/bs.mie.2019.11.012

Figure Lengend Snippet: Visualization methods.

Article Snippet: Some of the algorithms used for this purpose are briefly described below (see also ). table ft1 table-wrap mode="anchored" t5 caption a7 Method Environment Description Limitations PhenoGraph R (included in Cytofkit), Phyton, MATLAB ™ , FlowJo Exchange plug-in Based on nearest-neighbor graph, followed by definition of communities as interconnected events Number of clusters not customizable Not so fast ClusterX R (defined in Cytofkit) Based on densities in the tSNE projection map Fast Limited to tSNE ability to define clusters Number of clusters not customizable FlowSOM R package (included in Cytofkit), FlowJo Exchange plug-in Based in Self-Organization Maps (SOM) follow by hierarchical clustering Very fast Number of clusters customizable For exploratory analysis, hard to determine the number of clusters Open in a separate window Clustering methods.

Techniques:

Journal: Methods in enzymology

Article Title: Characterization of the tumor immune infiltrate by multiparametric flow cytometry and unbiased high-dimensional data analysis

doi: 10.1016/bs.mie.2019.11.012

Figure Lengend Snippet: Clustering methods.

Article Snippet: Some of the algorithms used for this purpose are briefly described below (see also ). table ft1 table-wrap mode="anchored" t5 caption a7 Method Environment Description Limitations PhenoGraph R (included in Cytofkit), Phyton, MATLAB ™ , FlowJo Exchange plug-in Based on nearest-neighbor graph, followed by definition of communities as interconnected events Number of clusters not customizable Not so fast ClusterX R (defined in Cytofkit) Based on densities in the tSNE projection map Fast Limited to tSNE ability to define clusters Number of clusters not customizable FlowSOM R package (included in Cytofkit), FlowJo Exchange plug-in Based in Self-Organization Maps (SOM) follow by hierarchical clustering Very fast Number of clusters customizable For exploratory analysis, hard to determine the number of clusters Open in a separate window Clustering methods.

Techniques: