Upgrading to R 3.2.0 on Windows

If you are using Windows you can easily upgrade to the latest version of R using the installr package. Simply run the following code:

# installing/loading the latest installr package:
install.packages("installr"); library(installr) #load / install+load installr
 
updateR() # updating R.

Running “updateR()” will detect if there is a new R version available, and if so it will download+install it (etc.).

If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here)

NEW FEATURES

• anyNA() gains a recursive argument.
• When x is missing and names is not false (including the default value), Sys.getenv(x, names) returns an object of class "Dlist" and hence prints tidily.
• (Windows.) shell() no longer consults the environment variable SHELL: too many systems have been encountered where it was set incorrectly (usually to a path where software was compiled, not where it was installed). R_SHELL, the preferred way to select a non-default shell, can be used instead.
• Some unusual arguments to embedFonts() can now be specified as character vectors, and the defaults have been changed accordingly.
• Functions in the Summary group duplicate less. (PR#15798)
• (Unix-alikes.) system(cmd, input = ) now uses ‘shell-execution-environment’ redirection, which will be more natural if cmd is not a single command (but requires a POSIX-compliant shell). (Wish of PR#15508)
• read.fwf() and read.DIF() gain a fileEncoding argument, for convenience.
• Graphics devices can add attributes to their description in .Device and .Devices. Several of those included with R use a "filepath" attribute.
• pmatch() uses hashing in more cases and so is faster at the expense of using more memory. (PR#15697)
• pairs() gains new arguments to select sets of variables to be plotted against each other.
• file.info(, extra_cols = FALSE) allows a minimal set of columns to be computed on Unix-alikes: on some systems without properly-configured caching this can be significantly faster with large file lists.
• New function dir.exists() in package base to test efficiently whether one or more paths exist and are directories.
• dput() and friends gain new controls hexNumeric and digits17 which output double and complex quantities as, respectively, binary fractions (exactly, see sprintf("%a")) and as decimals with up to 17 significant digits.
• save(), saveRDS() and serialize() now support ascii = NA which writes ASCII files using sprintf("%a") for double/complex quantities. This is read-compatible with ascii = TRUE but avoids binary->decimal->binary conversions with potential loss of precision. Unfortunately the Windows C runtime’s lack of C99 compliance means that the format cannot be read correctly there in R before 3.1.2.
• The default for formatC(decimal.mark =) has been changed to be getOption("OutDec"); this makes it more consistent with format() and suitable for use in print methods, e.g. those for classes "density", "ecdf", "stepfun" and "summary.lm".

  getOption("OutDec") is now consulted by the print method for class "kmeans", by cut(), dendrogram(), plot.ts() and quantile() when constructing labels and for the report fromlegend(trace = TRUE).

  (In part, wish of PR#15819.)

• printNum() and hence format() and formatC() give a warning if big.mark and decimal.mark are set to the same value (period and comma are not uncommonly used for each, and this is a check that conventions have not got mixed).
• merge() can create a result which uses long vectors on 64-bit platforms.
• dget() gains a new argument keep.source which defaults to FALSE for speed (dput() and dget() are most often used for data objects where this can make dget() many times faster).
• Packages may now use a file of common macro definitions in their help files, and may import definitions from other packages.
• A number of macros have been added in the new ‘share/Rd’ directory for use in package overview help pages, and promptPackage() now makes use of them.
• tools::parse_Rd() gains a new permissive argument which converts unrecognized macros into text. This is used by utils:::format.bibentry to allow LaTeX markup to be ignored.
• options(OutDec =) can now specify a multi-byte character, e.g., options(OutDec = "u00b7") in a UTF-8 locale.
• is.recursive(x) is no longer true when x is an external pointer, a weak reference or byte code; the first enables all.equal(x, x) when x .
• ls() (aka objects()) and as.list.environment() gain a new argument sorted.
• The "source" attribute (which has not been added to functions by R since before R version 2.14.0) is no longer treated as special.
• Function returnValue() has been added to give on.exit() code access to a function’s return value for debugging purposes.
• crossprod(x, y) allows more matrix coercions when x or y are vectors, now equalling t(x) %*% y in these cases (also reported by Radford Neal). Similarly, tcrossprod(x,y) and %*% work in more cases with vector arguments.
• Utility function dynGet() useful for detecting cycles, aka infinite recursions.
• The byte-code compiler and interpreter include new instructions that allow many scalar subsetting and assignment and scalar arithmetic operations to be handled more efficiently. This can result in significant performance improvements in scalar numerical code.
• apply(m, 2, identity) is now the same as the matrix m when it has named row names.
• A new function debuggingState() has been added, allowing to temporarily turn off debugging.
• example() gets a new optional argument run.donttest and tools::Rd2ex() a corresponding commentDonttest, with a default such that example(..) in help examples will run donttest code only if used interactively (a change in behaviour).
• rbind.data.frame() gains an optional argument make.row.names, for potential speedup.
• New function extSoftVersion() to report on the versions of third-party software in use in this session. Currently reports versions of zlib, bzlib, the liblzma from xz, PCRE, ICU, TRE and the iconv implementation.

  A similar function grSoftVersion() in package grDevices reports on third-party graphics software.

  Function tcltk::tclVersion() reports the Tcl/Tk version.

• Calling callGeneric() without arguments now works with primitive generics to some extent.
• vapply(x, FUN, FUN.VALUE) is more efficient notably for large length(FUN.VALUE); as extension of PR#16061.
• as.table() now allows tables with one or more dimensions of length 0 (such as as.table(integer())).
• names(x) now clears the names of call and ... objects.
• library() will report a warning when an insufficient dependency version is masking a sufficient one later on the library search path.
• A new plot() method for class "raster" has been added.
• New check_packages_in_dir_changes() function in package tools for conveniently analyzing how changing sources impacts the check results of their reverse dependencies.
• Speed-up from Peter Haverty for ls() and methods:::.requirePackage() speeding up package loading. (PR#16133)
• New get0() function, combining exists() and get() in one call, for efficiency.
• match.call() gains an envir argument for specifying the environment from which to retrieve the ... in the call, if any; this environment was wrong (or at least undesirable) when thedefinition argument was a function.
• topenv() has been made .Internal() for speedup, based on Peter Haverty’s proposal in PR#16140.
• getOption() no longer calls options() in the main case.
• Optional use of libcurl (version 7.28.0 from Oct 2012 or later) for Internet access:
  • capabilities("libcurl") reports if this is available.
  • libcurlVersion() reports the version in use, and other details of the "libcurl" build including which URL schemes it supports.
  • curlGetHeaders() retrieves the headers for http://, https://, ftp:// and ftps:// URLs: analysis of these headers can provide insights into the ‘existence’ of a URL (it might for example be permanently redirected) and is so used in R CMD check --as-cran.
  • download.file() has a new optional method "libcurl" which will handle more URL schemes, follow redirections, and allows simultaneous downloads of multiple URLs.
  • url() has a new method "libcurl" which handles more URL schemes and follows redirections. The default method is controlled by a new option url.method, which applies also to the opening of URLs via file() (which happens implicitly in functions such as read.table.)
  • When file() or url() is invoked with a https:// or ftps:// URL which the current method cannot handle, it switches to a suitable method if one is available.
• (Windows.) The DLLs ‘internet.dll’ and ‘internet2.dll’ have been merged. In this version it is safe to switch (repeatedly) between the internal and Windows internet functions within an Rsession.

  The Windows internet functions are still selected by flag –internet2 or setInternet2(). This can be overridden for an url() connection via its new method argument.

  download.file() has new method "wininet", selected as the default by –internet2 or setInternet2().

• parent.env<- can no longer modify the parent of a locked namespace or namespace imports environment. Contributed by Karl Millar.
• New function isLoadedNamespace() for readability and speed.
• names(env) now returns all the object names of an environment env, equivalently to ls(env, all.names = TRUE, sorted = FALSE) and also to the names of the corresponding list,names(as.list(env, all.names = TRUE)). Note that although names() returns a character vector, the names have no particular ordering.
• The memory manager now grows the heap more aggressively. This reduces the number of garbage collections, in particular while data or code are loaded, at the expense of slightly increasing the memory footprint.
• New function trimws() for removing leading/trailing whitespace.
• cbind() and rbind() now consider S4 inheritance during S3 dispatch and also obey deparse.level.
• cbind() and rbind() will delegate recursively to methods::cbind2 (methods::rbind2) when at least one argument is an S4 object and S3 dispatch fails (due to ambiguity).
• (Windows.) download.file(quiet = FALSE) now uses text rather than Windows progress bars in non-interactive use.
• New function hsearch_db() in package utils for building and retrieving the help search database used by help.search(), along with functions for inspecting the concepts and keywords in the help search database.
• New function .getNamespaceInfo(), a no-check version of getNamespaceInfo() mostly for internal speedups.
• The help search system now takes keyword entries in Rd files which are not standard keywords (as given in ‘KEYWORDS’ in the R documentation directory) as concepts. For standard keyword entries the corresponding descriptions are additionally taken as concepts.
• New lengths() function for getting the lengths of all elements in a list.
• New function toTitleCase() in package tools, tailored to package titles.
• The matrix methods of cbind() and rbind() allow matrices as inputs which have 2^31 or more elements. (For cbind(), wish of PR#16198.)
• The default method of image() has an explicit check for a numeric or logical matrix (which was always required).
• URLencode() will not by default encode further URLs which appear to be already encoded.
• BIC(mod) and BIC(mod, mod2) now give non-NA numbers for arima() fitted models, as nobs(mod) now gives the number of “used” observations for such models. This fixes PR#16198, quite differently than proposed there.
• The print() methods for "htest", "pairwise.htest" and "power.htest" objects now have a digits argument defaulting to (a function of) getOption("digits"), and influencing all printed numbers coherently. Unavoidably, this changes the display of such test results in some cases.
• Code completion for namespaces now recognizes all loaded namespaces, rather than only the ones that are also attached.
• The code completion mechanism can now be replaced by a user-specified completer function, for (temporary) situations where the usual code completion is inappropriate.
• unzip() will now warn if it is able to detect truncation when unpacking a file of 4GB or more (related to PR#16243).
• methods() reports S4 in addition to S3 methods; output is simplified when the class argument is used. .S3methods() and methods::.S4methods() report S3 and S4 methods separately.
• Higher order functions such as the apply functions and Reduce() now force arguments to the functions they apply in order to eliminate undesirable interactions between lazy evaluation and variable capture in closures. This resolves PR#16093.

More at http://cran.rstudio.com/

Reference: http://www.r-bloggers.com/r-3-2-0-is-released-using-the-installr-package-to-upgrade-in-windows-os/

The MicroScope platform is available at this URL:

https://www.genoscope.cns.fr/agc/microscope

Address of the bookmark: http://microscope.readthedocs.org/en/latest/index.html

Before you get started be sure to check out:

• devtools Google Group – https://groups.google.com/forum/#!forum/rdevtools
• book on “Advanced R programming” – http://adv-r.had.co.nz/
• GitHub repository – https://github.com/hadley/devtools

Address of the bookmark: https://www.rstudio.com/products/rpackages/devtools/

Address of the bookmark: https://github.com/mattharrison/Tiny-Python-3.6-Notebook/blob/master/python.rst

What are SNPs?
SNPs (pronounced "snips") are positions in the genome where individuals differ by a single nucleotide. For example:

Reference: ...A T G C A T G A...
Variant:     ...A T G T A T G A...

Here, the C in the reference genome has been replaced by a T in the variant.

SNPs occur roughly every 300–1,000 bases in the human genome, meaning there are millions of them scattered throughout our DNA. Most SNPs have no effect on health, but some are linked to disease susceptibility, drug response, and other traits.

Why Do We Analyze SNPs?
1. Medical Genetics

Identify disease-associated variants (e.g., BRCA1/2 in breast cancer).

Predict drug response (pharmacogenomics).

Enable precision medicine by tailoring treatments.

2. Population Genetics & Ancestry

Trace human migration and ancestry.

Study genetic diversity within and between populations.

3. Agriculture & Animal Breeding

Select for desirable traits (drought resistance, yield, disease resistance).

Improve breeding efficiency in livestock.

4. Evolutionary Biology

Track natural selection.

Study adaptation in wild populations.

How is SNP Analysis Performed?
SNP analysis can be broadly divided into three steps:

SNP Detection
Genotyping arrays: Chips that test hundreds of thousands of known SNP positions simultaneously. Fast and affordable, widely used in consumer ancestry testing.

Whole-genome or whole-exome sequencing: Can detect known and novel SNPs across the genome.

Targeted sequencing or PCR: For focused analysis of specific regions.

Variant Calling
Sequencing data is aligned to a reference genome. Bioinformatics tools (e.g., GATK, bcftools) identify positions where the sequenced sample differs from the reference.

Annotation and Interpretation
Tools (e.g., SnpEff, VEP) predict the functional impact of SNPs.

Are the SNPs in coding regions? Do they cause amino acid changes? Are they known to be pathogenic?

Databases like dbSNP, ClinVar, and GWAS Catalog provide information on known associations.

Common Tools for SNP Analysis
Alignment: BWA, Bowtie2

Variant Calling: GATK, FreeBayes

Visualization: IGV, UCSC Genome Browser

Annotation: SnpEff, VEP

Statistical Analysis: PLINK, SNPTEST

Challenges in SNP Analysis
False positives/negatives: Sequencing errors, alignment issues.

Population stratification: Confounding in association studies.

Interpretation: Many SNPs have unknown or complex effects.

Researchers address these with rigorous quality control, large datasets, and increasingly sophisticated statistical models.

The Future of SNP Analysis
With advances in sequencing technology and AI-driven analysis, SNP studies are expanding:

Polygenic risk scores predict disease risk based on thousands of SNPs.

Large-scale biobanks (e.g., UK Biobank, All of Us) enable powerful genome-wide association studies (GWAS).

CRISPR and functional assays help validate SNP effects in the lab.

SNP analysis is at the heart of the genomic revolution, promising insights into biology, health, and evolution at unprecedented scale.

Conclusion
From diagnosing rare diseases to designing better crops, SNP analysis is a foundational tool in modern science. As our ability to sequence and interpret genomes improves, so will our understanding of these tiny—but mighty—variations in DNA.

Following are the useful links:

Single Cell RNAseq data analysis Tutorial

A step-by-step workflow for low-level analysis of single-cell RNA-seq data

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

SCell: single-cell RNA-seq analysis software

https://github.com/diazlab/SCell

Beta-Poisson model for single-cell RNA-seq data analyses

https://github.com/nghiavtr/BPSC

Sincera: A Computational Pipeline for Single Cell RNA-Seq Profiling Analysis

https://research.cchmc.org/pbge/sincera.html

SC3 – consensus clustering of single-cell RNA-Seq data

http://biorxiv.org/content/early/2016/09/02/036558

Citrus: A toolkit for single cell sequencing analysis

http://biorxiv.org/content/early/2016/09/14/045070

Single-Cell Resolution of Temporal Gene Expression during Heart Development

http://www.cell.com/developmental-cell/fulltext/S1534-5807(16)30682-7

Scalable latent-factor models applied to single-cell RNA-seq data separate biological drivers from confounding effects

http://biorxiv.org/content/early/2016/11/15/087775

Single cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes

http://genome.cshlp.org/content/early/2016/11/18/gr.212720.116.abstract

SCODE: An efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation

http://biorxiv.org/content/early/2016/11/21/088856

SCOUP is a probabilistic model to analyze single-cell expression data during differentiation

https://github.com/hmatsu1226/SCOUP

scLVM is a modelling framework for single-cell RNA-seq data

https://github.com/PMBio/scLVM

Selective Locally linear Inference of Cellular Expression Relationships (SLICER) algorithm for inferring cell trajectories

https://github.com/jw156605/SLICER

SinQC: A Method and Tool to Control Single-cell RNA-seq Data Quality

http://www.morgridge.net/SinQC.html

TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

https://github.com/zji90/TSCAN

Visualization and cellular hierarchy inference of single-cell data using SPADE

http://www.nature.com/nprot/journal/v11/n7/full/nprot.2016.066.html

OEFinder: Identify ordering effect genes in single cell RNA-seq data

https://github.com/lengning/OEFinder

More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

More tutorial at 

http://calla.rnet.missouri.edu/cheng_courses/mlbioinfo/mlbioinfo.htm

http://www.raetschlab.org/lectures/MLBioinformatics

http://www.raetschlab.org/lectures/bertinoro08

Book at 

https://personal.utdallas.edu/~pradiptaray/teaching/7_deep_learning_bioinfo.pdf

Address of the bookmark: http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/

https://cov-lineages.org/resources/pangolin/tutorial.html

Address of the bookmark: https://cov-lineages.org/resources/pangolin/tutorial.html

1️⃣ Data Analysis for the Life Sciences
A fantastic starting point to learn statistics, R programming, and exploratory data analysis in the context of biology. The best part? It’s available free online from HarvardX.

2️⃣ Practical Computing for Biologists
The very first book I picked up when I started learning computational biology. It’s beginner-friendly and focuses on essential computing skills every biologist needs.

3️⃣ A Primer for Computational Biology
An open-access, hands-on introduction to computational biology concepts and coding techniques. Perfect if you want to learn through real examples.

4️⃣ Computational Genomics with R
For those who already know R and want to dive deeper into genome-scale data analysis, from sequence alignment to gene expression.

5️⃣ The Biologist’s Guide to Computing
Bridges the gap between biological problems and computational thinking, making it easier for life scientists to approach programming and data analysis.

6️⃣ Bioinformatics Data Skills
A must-read to sharpen your bioinformatics toolkit — from command-line skills to reproducible research workflows. Ideal once you’ve covered the basics.

7️⃣ Bioinformatics Workbook
A practical tutorial series to help scientists design bioinformatics projects, analyze data, and understand best practices.

8️⃣ Modern Statistics for Modern Biology
An essential guide to modern statistical methods applied to biology, blending theory with hands-on examples in R.

9️⃣ Algorithms on Strings, Trees, and Sequences by Dan Gusfield
A classic reference for anyone wanting to understand the algorithms behind sequence alignment, genome assembly, and biological data structures.

Address of the bookmark: https://github.com/SUwonglab/COSINE