132 research outputs found
Cytokine activity estimation and receptor abundance approximation with multimodal scRNA-seq/CITE-seq data.
No full textSingle-cell RNA-sequencing (scRNA-seq) data enables individual cell resolution quantification of messenger RNA. While revolutionary for revealing key cell type and cell phenotype-specific heterogeneity, scRNA-seq data has important statistical challenges. First, scRNA-seq data is usually more sparse and second, it is more variable than bulk transcriptomics data. Given these challenges, more intuitive and interpretive statistical and computational methods are needed to develop appropriate solutions. Here, we detail the development of three techniques to perform receptor abundance estimation and cytokine activity estimation for scRNA-seq data. While SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) and STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) methods address the sparsity constraints of scRNA-seq data via dimensionality reduction and a thresholding mechanism and co-expression analysis using joint scRNA-seq/CITE-seq data, respectively, SCAPE (Single cell transcriptomics-level Cytokine Activity Prediction and Estimation) aims to leverage a cytokine signaling activity database via a modified gene set testing approach to accommodate negative weights. Our approaches work well in practice and aim to provide more interpretive solutions to statistical challenges of scRNA-seq data. Furthermore, our methods have the potential to be integrated in bioinformatics pipelines for the tasks of cell type and cell state identification
The Loss in Meaning: Influence of Strategy Language’s and Modern Financial Discourse on the Working Concepts in Islamic Banking and Finance
No full textThis paper employs Wittgenstein language-games to analyze strategy language used by leaders of Islamic finance industry to envision its future. The analysis infers that the explicit market orientation of strategy language and modern knowledge of finance has redefined various concepts related of Islamic finance at the cost of its original spirit. This may also have adverse effects on developing ethical and spiritual orientation of Islamic banks. The concerned academia and scholarship therefore need to review such trends and work to prevent the subsequent degradation in the public image of IFIs to avoid disappointments of religiously inspired customers.
STREAK: A supervised cell surface receptor abundance estimation strategy for single cell RNA-sequencing data using feature selection and thresholded gene set scoring.
No full textThe accurate estimation of cell surface receptor abundance for single cell transcriptomics data is important for the tasks of cell type and phenotype categorization and cell-cell interaction quantification. We previously developed an unsupervised receptor abundance estimation technique named SPECK (Surface Protein abundance Estimation using CKmeans-based clustered thresholding) to address the challenges associated with accurate abundance estimation. In that paper, we concluded that SPECK results in improved concordance with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data relative to comparative unsupervised abundance estimation techniques using only single-cell RNA-sequencing (scRNA-seq) data. In this paper, we outline a new supervised receptor abundance estimation method called STREAK (gene Set Testing-based Receptor abundance Estimation using Adjusted distances and cKmeans thresholding) that leverages associations learned from joint scRNA-seq/CITE-seq training data and a thresholded gene set scoring mechanism to estimate receptor abundance for scRNA-seq target data. We evaluate STREAK relative to both unsupervised and supervised receptor abundance estimation techniques using two evaluation approaches on six joint scRNA-seq/CITE-seq datasets that represent four human and mouse tissue types. We conclude that STREAK outperforms other abundance estimation strategies and provides a more biologically interpretable and transparent statistical model
Fig 13 -
No full textGene set scoring versus thresholding sensitivity analysis examining frequency of receptors with highest average rank correlations between CITE-seq data and abundance values estimated using STREAK (i.e., estimation via gene set scoring followed by thresholding) or VAM (i.e., estimation using just gene set scoring) evaluated using the 5-fold cross-validation approach with the indicated training data ranging from 1,000 to 12,000 cells for the Hao data (Fig 13A) 1,000 to 10,000 cells for the Unterman data (Fig 13B) and 1,000 to 1,682 cells for the MALT data (Fig 13C).</p
S13 Fig -
No full textAverage rank correlations between CITE-seq data and receptor abundance values estimated using STREAK and comparative methods as evaluated using the 5-fold cross-validation approach for training data consisting of 12,000 cells from the Unterman dataset. (TIFF)</p
S8 Fig -
No full textTraining data sensitivity analysis examining frequency of receptors with highest average rank correlations between CITE-seq data and abundance values estimated using STREAK and cTP-net via the cross-training strategy. (TIFF)</p
S4 Fig -
No full textCorrelation versus correlation scatter plots for the MPEM data. Each point corresponds to a receptor from a sample size of 46 receptors. (TIFF)</p
S1 Fig -
No full textCorrelation versus correlation scatter plots for the PBMC Unterman data. Each point corresponds to a receptor from a sample size of 167 receptors. (TIFF)</p
Taking a Chance in the Classroom: Using a “Study of Studies” to Help Statistics Students Assess Research Findings
No full textFig 8 -
No full textCorrelation versus correlation scatter plots for the PBMC Hao data. Each point corresponds to a receptor from a maximum sample size of 217 receptors. LOESS (locally estimated scatterplot smoothing) function is applied to smooth out conditional means. Individual correlations are computed using the Spearman rank correlation metric.</p
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