Showing 14 of total 14 results (show query)
bioc
Cardinal:A mass spectrometry imaging toolbox for statistical analysis
Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification.
Maintained by Kylie Ariel Bemis. Last updated 3 months ago.
softwareinfrastructureproteomicslipidomicsmassspectrometryimagingmassspectrometryimmunooncologynormalizationclusteringclassificationregression
48 stars 10.32 score 200 scriptsbioc
flowCore:flowCore: Basic structures for flow cytometry data
Provides S4 data structures and basic functions to deal with flow cytometry data.
Maintained by Mike Jiang. Last updated 5 months ago.
immunooncologyinfrastructureflowcytometrycellbasedassayscpp
10.17 score 1.7k scripts 59 dependents
aphalo
photobiology:Photobiological Calculations
Definitions of classes, methods, operators and functions for use in photobiology and radiation meteorology and climatology. Calculation of effective (weighted) and not-weighted irradiances/doses, fluence rates, transmittance, reflectance, absorptance, absorbance and diverse ratios and other derived quantities from spectral data. Local maxima and minima: peaks, valleys and spikes. Conversion between energy-and photon-based units. Wavelength interpolation. Astronomical calculations related solar angles and day length. Colours and vision. This package is part of the 'r4photobiology' suite, Aphalo, P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.
Maintained by Pedro J. Aphalo. Last updated 16 days ago.
lightphotobiologyquantificationr4photobiology-suiteradiationspectrasun-position
4 stars 9.35 score 604 scripts 12 dependents
tkcaccia
KODAMA:Knowledge Discovery by Accuracy Maximization
An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.
Maintained by Stefano Cacciatore. Last updated 13 days ago.
1 stars 7.00 score 63 scripts 1 dependentsbioc
BulkSignalR:Infer Ligand-Receptor Interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics
Inference of ligand-receptor (LR) interactions from bulk expression (transcriptomics/proteomics) data, or spatial transcriptomics. BulkSignalR bases its inferences on the LRdb database included in our other package, SingleCellSignalR available from Bioconductor. It relies on a statistical model that is specific to bulk data sets. Different visualization and data summary functions are proposed to help navigating prediction results.
Maintained by Jean-Philippe Villemin. Last updated 3 months ago.
networkrnaseqsoftwareproteomicstranscriptomicsnetworkinferencespatial
5.22 score 15 scriptsbioc
scRecover:scRecover for imputation of single-cell RNA-seq data
scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results.
Maintained by Zhun Miao. Last updated 5 months ago.
geneexpressionsinglecellrnaseqtranscriptomicssequencingpreprocessingsoftware
8 stars 5.20 score 9 scriptsbioc
MLSeq:Machine Learning Interface for RNA-Seq Data
This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data.
Maintained by Gokmen Zararsiz. Last updated 5 months ago.
immunooncologysequencingrnaseqclassificationclustering
4.81 score 27 scripts 1 dependents
mengxu98
inferCSN:Inferring Cell-Specific Gene Regulatory Network
An R package for inferring cell-type specific gene regulatory network from single-cell RNA data.
Maintained by Meng Xu. Last updated 2 days ago.
3 stars 4.79 score 6 scripts
cxzdsa2332
idopNetwork:A Network Tool to Dissect Spatial Community Ecology
Most existing approaches for network reconstruction can only infer an overall network and, also, fail to capture a complete set of network properties. To address these issues, a new model has been developed, which converts static data into their 'dynamic' form. 'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, omnidirectional and personalized networks. For more information on functional clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) <doi:10.1080/19490976.2022.2106103>.
Maintained by Ang Dong. Last updated 2 years ago.
4 stars 4.30 score 3 scripts
bioc
fCI:f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics
(f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods.
Maintained by Shaojun Tang. Last updated 5 months ago.
3.30 score 5 scripts
tsukubai
hclusteasy:Determining Hierarchical Clustering Easily
Facilitates hierarchical clustering analysis with functions to read data in 'txt', 'xlsx', and 'xls' formats, apply normalization techniques to the dataset, perform hierarchical clustering and construct scatter plot from principal component analysis to evaluate the groups obtained.
Maintained by Henrique Andrade. Last updated 9 months ago.
3.00 score 1 scripts
cran
MariNET:Build Network Based on Linear Mixed Models from EHRs
Analyzing longitudinal clinical data from Electronic Health Records (EHRs) using linear mixed models (LMM) and visualizing the results as networks. It includes functions for fitting LMM, normalizing adjacency matrices, and comparing networks. The package is designed for researchers in clinical and biomedical fields who need to model longitudinal data and explore relationships between variables For more details see Bates et al. (2015) <doi:10.18637/jss.v067.i01>.
Maintained by Vargas-Fernández Marina. Last updated 8 days ago.
2.00 score
zheng-ww
fmf:Fast Class Noise Detector with Multi-Factor-Based Learning
A fast class noise detector which provides noise score for each observations. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.
Maintained by Wanwan Zheng. Last updated 5 years ago.
1.00 score 1 scripts