Showing 14 of total 14 results (show query)
zilong-li
vcfppR:Rapid Manipulation of the Variant Call Format (VCF)
The 'vcfpp.h' (<https://github.com/Zilong-Li/vcfpp>) provides an easy-to-use 'C++' 'API' of 'htslib', offering full functionality for manipulating Variant Call Format (VCF) files. The 'vcfppR' package serves as the R bindings of the 'vcfpp.h' library, enabling rapid processing of both compressed and uncompressed VCF files. Explore a range of powerful features for efficient VCF data manipulation.
Maintained by Zilong Li. Last updated 2 days ago.
bioinformaticsfastrhtslibpopulation-geneticspopulation-genomicsvcfvcf-datavisulizationlibdeflatezlibbzip2xz-utilscurlcpp
10.0 match 13 stars 6.70 score 16 scripts
bioc
BloodGen3Module:This R package for performing module repertoire analyses and generating fingerprint representations
The BloodGen3Module package provides functions for R user performing module repertoire analyses and generating fingerprint representations. Functions can perform group comparison or individual sample analysis and visualization by fingerprint grid plot or fingerprint heatmap. Module repertoire analyses typically involve determining the percentage of the constitutive genes for each module that are significantly increased or decreased. As we describe in details;https://www.biorxiv.org/content/10.1101/525709v2 and https://pubmed.ncbi.nlm.nih.gov/33624743/, the results of module repertoire analyses can be represented in a fingerprint format, where red and blue spots indicate increases or decreases in module activity. These spots are subsequently represented either on a grid, with each position being assigned to a given module, or in a heatmap where the samples are arranged in columns and the modules in rows.
Maintained by Darawan Rinchai. Last updated 5 months ago.
softwarevisualizationgeneexpression
4.0 match 4.30 score 5 scripts
duct317
scDHA:Single-Cell Decomposition using Hierarchical Autoencoder
Provides a fast and accurate pipeline for single-cell analyses. The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).
Maintained by Ha Nguyen. Last updated 11 months ago.
2.3 match 40 stars 6.38 score 20 scripts 2 dependentsbioc
CHETAH:Fast and accurate scRNA-seq cell type identification
CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree.
Maintained by Jurrian de Kanter. Last updated 5 months ago.
classificationrnaseqsinglecellclusteringgeneexpressionimmunooncology
1.6 match 44 stars 7.27 score 70 scriptsbioc
stJoincount:stJoincount - Join count statistic for quantifying spatial correlation between clusters
stJoincount facilitates the application of join count analysis to spatial transcriptomic data generated from the 10x Genomics Visium platform. This tool first converts a labeled spatial tissue map into a raster object, in which each spatial feature is represented by a pixel coded by label assignment. This process includes automatic calculation of optimal raster resolution and extent for the sample. A neighbors list is then created from the rasterized sample, in which adjacent and diagonal neighbors for each pixel are identified. After adding binary spatial weights to the neighbors list, a multi-categorical join count analysis is performed to tabulate "joins" between all possible combinations of label pairs. The function returns the observed join counts, the expected count under conditions of spatial randomness, and the variance calculated under non-free sampling. The z-score is then calculated as the difference between observed and expected counts, divided by the square root of the variance.
Maintained by Jiarong Song. Last updated 5 months ago.
transcriptomicsclusteringspatialbiocviewssoftware
1.9 match 4 stars 4.60 score 3 scripts
ubcxzhang
DesignCTPB:Design Clinical Trials with Potential Biomarker Effect
Applying 'CUDA' 'GPUs' via 'Numba' for optimal clinical design. It allows the user to utilize a 'reticulate' 'Python' environment and run intensive Monte Carlo simulation to get the optimal cutoff for the clinical design with potential biomarker effect, which can guide the realistic clinical trials.
Maintained by Yitao Lu. Last updated 4 years ago.
1.8 match 1 stars 3.70 score 5 scriptsbioc
TRESS:Toolbox for mRNA epigenetics sequencing analysis
This package is devoted to analyzing MeRIP-seq data. Current functionalities include 1. detect transcriptome wide m6A methylation regions 2. detect transcriptome wide differential m6A methylation regions.
Maintained by Zhenxing Guo. Last updated 5 months ago.
epigeneticsrnaseqpeakdetectiondifferentialmethylation
1.8 match 3.48 score 5 scripts 1 dependents
yuelyu21
SCIntRuler:Guiding the Integration of Multiple Single-Cell RNA-Seq Datasets
The accumulation of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets. By augmenting sample sizes and enhancing analytical robustness, integration can lead to more insightful biological conclusions. However, challenges arise due to the inherent diversity and batch discrepancies within and across studies. SCIntRuler, a novel R package, addresses these challenges by guiding the integration of multiple scRNA-seq datasets.
Maintained by Yue Lyu. Last updated 5 months ago.
sequencinggeneticvariabilitysinglecellcpp
1.2 match 2 stars 4.85 score 3 scriptsbioc
RNAdecay:Maximum Likelihood Decay Modeling of RNA Degradation Data
RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions.
Maintained by Reed Sorenson. Last updated 5 months ago.
immunooncologysoftwaregeneexpressiongeneregulationdifferentialexpressiontranscriptiontranscriptomicstimecourseregressionrnaseqnormalizationworkflowstep
1.3 match 4.18 score 2 scripts
xcding1212
SIMle:Estimation and Inference for General Time Series Regression
We provide functions for estimation and inference of nonlinear and non-stationary time series regression using the sieve methods and bootstrapping procedure.
Maintained by Xiucai Ding. Last updated 1 years ago.
1.7 match 2.30 score 3 scriptsbioc
ENmix:Quality control and analysis tools for Illumina DNA methylation BeadChip
Tools for quanlity control, analysis and visulization of Illumina DNA methylation array data.
Maintained by Zongli Xu. Last updated 2 days ago.
dnamethylationpreprocessingqualitycontroltwochannelmicroarrayonechannelmethylationarraybatcheffectnormalizationdataimportregressionprincipalcomponentepigeneticsmultichanneldifferentialmethylationimmunooncology
0.6 match 6.01 score 115 scriptsbioc
intansv:Integrative analysis of structural variations
This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package.
Maintained by Wen Yao. Last updated 5 months ago.
geneticsannotationsequencingsoftware
0.5 match 4.48 score 2 scriptsbioc
tomoda:Tomo-seq data analysis
This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots.
Maintained by Wendao Liu. Last updated 5 months ago.
geneexpressionsequencingrnaseqtranscriptomicsspatialclusteringvisualization
0.5 match 4.00 score 2 scripts