Tag: z-score

Single-cell analyses define a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer

Mapping molecular changes across malignant transformation We generated single-cell data for 81 samples collected from eight FAP and seven non-FAP donors (Fig. 1a and Supplementary Tables 1 and 2). For each tissue, we performed matched scATAC-seq and snRNA-seq (10x Genomics). We obtained high-quality single-cell chromatin accessibility profiles for 447,829 cells…

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Transcriptomic and proteomic profiling of peptidase expression in Fasciola hepatica eggs developing at host’s body temperature

From the bovine liver, we isolated 97 live F. hepatica adults. After overnight cultivation, we recovered approx. 228,000 laid eggs, which we divided in three groups. The first group (T0) was immediately frozen at − 80 °C, while the other two groups (T5 and T10) were incubated for 5 and 10 days at…

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InteractiveComplexHeatmap on DESeq2 object with more than 2 groups

InteractiveComplexHeatmap on DESeq2 object with more than 2 groups 1 Hello all, I’m writing with the hope someone can clarify a doubt I have about the differential heatmap generated by the package InteractiveComplexHeatmap via the simple command interactivate(dds). I read the package documentation at bioconductor.org/packages/release/bioc/html/InteractiveComplexHeatmap.html, but I couldn’t find the…

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r – RNA-Seq Data Heatmap: Is it necessary to do a log2 transformation of RPKM values before doing the Z-score standardisation?

I am making a heatmap using RNA-Seq data in R. The heatmap shows gene expression values (RPKM) in different brain regions. I have the following code: library(tidyverse) library(pheatmap) library(matrixStats) read_csv(“prenatal_heatmap_data.csv”) -> all_data all_data %>% column_to_rownames(“Brain Region”) -> heatmap_data heatmap_data %>% pheatmap() Which generates the following heatmap: I want to do…

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AttCRISPR: a spacetime interpretable model for prediction of sgRNA on-target activity | BMC Bioinformatics

Dataset The dataset we used for training, validation and testing is the DeepHF dataset [17]. We extracted 55604, 58617, 56888 sgRNAs with activity (represented by insertion/deletion (indel)) for WT-SpCas9, eSpCas9(1.1) and SpCas9-HF1, respectively, from its source data, and use the same partition method to divide train set and test set….

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