ClustIRR - Clustering of Immune Receptor Repertoires
ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment.
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clusteringimmunooncologysinglecellsoftwareclassificationbayesianbiomedicalinformaticsmathematicalbiologyb-cell-receptorbioinformaticsimmunoinformaticsimmunologyquantitative-methodsrep-seqrepertoire-analysist-cell-receptoronetbbcpp
6.70 score 5 stars 16 scripts 362 downloadscellmig - Uncertainty-aware quantitative analysis of high-throughput live cell migration data
High-throughput cell imaging facilitates the analysis of cell migration across many wells treated under different biological conditions. These workflows generate considerable technical noise and biological variability, and therefore technical and biological replicates are necessary, leading to large, hierarchically structured datasets, i.e., cells are nested within technical replicates that are nested within biological replicates. Current statistical analyses of such data usually ignore the hierarchical structure of the data and fail to explicitly quantify uncertainty arising from technical or biological variability. To address this gap, we present cellmig, an R package implementing Bayesian hierarchical models for migration analysis. cellmig quantifies condition- specific velocity changes (e.g., drug effects) while modeling nested data structures and technical artifacts. It further enables synthetic data generation for experimental design optimization.
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singlecellcellbiologybayesianexperimentaldesignsoftwarebatcheffectregressionclusteringonetbbcpp
5.76 score 2 stars 19 scripts 286 downloadsscBubbletree - Quantitative visual exploration of scRNA-seq data
scBubbletree is a quantitative method for the visual exploration of scRNA-seq data, preserving key biological properties such as local and global cell distances and cell density distributions across samples. It effectively resolves overplotting and enables the visualization of diverse cell attributes from multiomic single-cell experiments. Additionally, scBubbletree is user-friendly and integrates seamlessly with popular scRNA-seq analysis tools, facilitating comprehensive and intuitive data interpretation.
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visualizationclusteringsinglecelltranscriptomicsrnaseqbig-databigdatascrna-seqscrna-seq-analysisvisualvisual-exploration
5.68 score 8 stars 30 scripts 410 downloadsIgGeneUsage - Differential gene usage in immune repertoires
Detection of biases in the usage of immunoglobulin (Ig) genes is an important task in immune repertoire profiling. IgGeneUsage detects aberrant Ig gene usage between biological conditions using a probabilistic model which is analyzed computationally by Bayes inference. With this IgGeneUsage also avoids some common problems related to the current practice of null-hypothesis significance testing.
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differentialexpressionregressiongeneticsbayesianbiomedicalinformaticsimmunooncologymathematicalbiologyb-cell-receptorbcr-repertoiredifferential-analysisdifferential-gene-expressionhigh-throughput-sequencingimmune-repertoireimmune-repertoire-analysisimmune-repertoiresimmunogenomicsimmunoglobulinimmunoinformaticsimmunological-bioinformaticsimmunologytcr-repertoirevdj-recombinationonetbbcpp
5.38 score 6 stars 426 downloads