blog.bsmithers.co.uk
Data Analysis |
http://blog.bsmithers.co.uk/category/data-analysis
Rambling thoughts on Programming / Bioinformatics / Personal Life. Category Archives: Data Analysis. Analysing the UK General Election Results. May 11, 2015. On Thursday, 7th May, the UK voted in a Conservative government by a small majority, to the surprise of most. The surprise was not. To present this; this post is about how I did that. Obtaining the overall results proved to be very straightforward, the above page contains the results in a JSON string. So far, so good. Get the constituency name.
suprahex.r-forge.r-project.org
Docs @ supraHex 1.10.1
http://suprahex.r-forge.r-project.org/docs.html
SupraHex 1.10.1. Training and Analysis functions. These functions are used for training and analysis. Function to setup the pipeline for completing ab initio training given the input data. Function to define a supra-hexagonal grid. Function to define the topology of a map grid. Function to initialise a sInit object given a topology and input data. Function to define trainology (training environment). Function to implement training via sequential algorithm. Function to partition a grid map into clusters.
suprahex.r-forge.r-project.org
FAQs @ supraHex 1.10.1
http://suprahex.r-forge.r-project.org/faqs.html
SupraHex 1.10.1. What is the supra-hexagonal map? How to control the layout when using visCompReorder? How to control the device output generated by visCompReorder? Does visDmatCluster serve as a gene cluster legend to the samples visualised by visCompReorder? How to add a legend key to visDmatCluster? How to choose the training algorithm: sequential vs batch? How many neighborhood kernels are supported and how to choose?
suprahex.r-forge.r-project.org
Cite @ supraHex 1.10.1
http://suprahex.r-forge.r-project.org/cite.html
SupraHex 1.10.1. Fang H, Gough J. (2014) supraHex: an R/Bioconductor package for tabular omics data analysis using a supra-hexagonal map. Biochemical and Biophysical Research Communications. 443(1), 285-289. doi:10.1016/j.bbrc.2013.11.103. Computational Genomics Group, Department of Computer Science, University of Bristol, UK.
suprahex.r-forge.r-project.org
Demos @ supraHex 1.10.1
http://suprahex.r-forge.r-project.org/demos.html
SupraHex 1.10.1. Demo for multilayer omics dataset from Hiratani et al. Demo for leukemia patient dataset from Golub et al. Demo for human embryo dataset from Fang et al. Demo for arabidopsis embryo dataset from Xiang et al. Demo for for the artwork in ISMB 2014. Demo for human cell type evolutionary profile dataset from Sardar et al. Demo for postprocessing cellular prevalence of mutations in clonal populations (inferred by PyClone). Demo for analysing RNA-seq dataset (along with edgeR).
suprahex.r-forge.r-project.org
Install @ supraHex 1.10.1
http://suprahex.r-forge.r-project.org/install.html
SupraHex 1.10.1. R ( http:/ www.r-project.org. Is a language and environment for statistical computing and graphics. We assume R version ( = 3.0.2) has been installed in your local machine. The current version can be installed following quick instructions below for different platforms (Windows, Mac, and Linux). Download R for Windows. Download R for Mac OS X 10.6 (Snow Leopard or higher). Below are shell command lines for R installation in Terminal (for. Assume you have a. Should be replaced with yours):.
blog.bsmithers.co.uk
Uncategorized |
http://blog.bsmithers.co.uk/category/uncategorized
Rambling thoughts on Programming / Bioinformatics / Personal Life. July 5, 2010. This is the personal blog for Ben Smithers, though it will probably serve more as a portfolio than as a blog. I will also be using it to document my progress of my summer research project. Bristol Computational Genomics Group. Matching With Don't Cares.
supfam.org
Home @ supraHex 1.7.3
http://supfam.org/supraHex
SupraHex 1.7.3. An open-source R/Bioconductor package for tabular omics data analysis using. The artwork called supraHex. Has won The Best Artwork Award in ISMB 2014. This artwork is automatically done and is reproducible here. Demonstrated in a variety of genome-wide datasets such as:. And clonal population structure. Is a giant hexagon on a 2-dimensional map grid seamlessly consisting of smaller hexagons. For more, see slides. And poster in ISMB2014. Visualisations at and across nodes of the map;.
dnet.r-forge.r-project.org
Home @ dnet 1.0.7
http://dnet.r-forge.r-project.org/index.html
Dnet 1.0.7. An open-source R package for omics data integrative analysis in terms of. Identification of gene-active networks from high-throughput omics data (e.g. mutation. Network-based sample classifications and visualisations on 2D sample landscape;. Random Walk with Restart for network affinity calculation;. Semantic similarity between ontology terms (and between their annotated genes);. Enrichment analysis using a variety of built-in databases;. A wide variety of built-in RData.
dnet.r-forge.r-project.org
FAQs @ dnet 1.0.7
http://dnet.r-forge.r-project.org/faqs.html
Dnet 1.0.7. How to obtain the dnet package and what it can do? What is the built-in data and how to use? How to obtain a list of genes with significance information that can be used for identifying gene-active networks? How many ways are supported to visualise gene-active networks? What is Random Walk with Restart (RWR)? How to extract a subnetwork that revolves around a specific gene of interest? Computational Genomics Group, Department of Computer Science, University of Bristol, UK.
