Matrix factorization - R-bloggers

http://www.r-bloggers.com/testing-recommender-systems-in-r/

http://www.r-bloggers.com/matrix-factorization/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed%3A+RBloggers+%28R+bloggers%29

Latent Factor Matrix Factorization.
Singular value decomposition 
optim() function in R for optimization

http://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/ 
In-depth introduction to machine learning in 15 hours of expert videos 

The Elements of Programming Style

http://en.wikipedia.org/wiki/The_Elements_of_Programming_Style

1. Write clearly -- don't be too clever.
2. Say what you mean, simply and directly.
3. Use library functions whenever feasible.
4. Avoid too many temporary variables.
5. Write clearly -- don't sacrifice clarity for efficiency.
6. Let the machine do the dirty work.
7. Replace repetitive expressions by calls to common functions.
8. Parenthesize to avoid ambiguity.
9. Choose variable names that won't be confused.
10. Avoid unnecessary branches.
11. If a logical expression is hard to understand, try transforming it.
12. Choose a data representation that makes the program simple.
13. Write first in easy-to-understand pseudo language; then translate into whatever language you have to use.
14. Modularize. Use procedures and functions.
15. Avoid gotos completely if you can keep the program readable.
16. Don't patch bad code -- rewrite it.
17. Write and test a big program in small pieces.
18. Use recursive procedures for recursively-defined data structures.
19. Test input for plausibility and validity.
20. Make sure input doesn't violate the limits of the program.
21. Terminate input by end-of-file marker, not by count.
22. Identify bad input; recover if possible.
23. Make input easy to prepare and output self-explanatory.
24. Use uniform input formats.
25. Make input easy to proofread.
26. Use self-identifying input. Allow defaults. Echo both on output.
27. Make sure all variables are initialized before use.
28. Don't stop at one bug.
29. Use debugging compilers.
30. Watch out for off-by-one errors.
31. Take care to branch the right way on equality.
32. Be careful if a loop exits to the same place from the middle and the bottom.
33. Make sure your code does "nothing" gracefully.
34. Test programs at their boundary values.
35. Check some answers by hand.
36. 10.0 times 0.1 is hardly ever 1.0.
37. 7/8 is zero while 7.0/8.0 is not zero.
38. Don't compare floating point numbers solely for equality.
39. Make it right before you make it faster.
40. Make it fail-safe before you make it faster.
41. Make it clear before you make it faster.
42. Don't sacrifice clarity for small gains in efficiency.
43. Let your compiler do the simple optimizations.
44. Don't strain to re-use code; reorganize instead.
45. Make sure special cases are truly special.
46. Keep it simple to make it faster.
47. Don't diddle code to make it faster -- find a better algorithm.
48. Instrument your programs. Measure before making efficiency changes.
49. Make sure comments and code agree.
50. Don't just echo the code with comments -- make every comment count.
51. Don't comment bad code -- rewrite it.
52. Use variable names that mean something.
53. Use statement labels that mean something.
54. Format a program to help the reader understand it.
55. Document your data layouts.
56. Don't over-comment

The Elements of Style

Vigorous writing is concise. A sentence should contain no unnecessary words, a paragraph no unnecessary sentences, for the same reason that a drawing should have no unnecessary lines and a machine no unnecessary parts. This requires not that the writer make all his sentences short, or that he avoid all detail and treat his subjects only in outline, but that he make every word tell.

—"Elementary Principles of Composition", The Elements of Style^[10]

Research methods: Know when your numbers are significant

http://www.nature.com/nature/journal/v492/n7428/fig_tab/492180a_T1.html  

When N is only 2 or 3, it would be more transparent to just plot the independent data points, and let the readers interpret the data for themselves, rather than showing possibly misleading P values or error bars and drawing statistical inferences. 

All experimental biologists and all those who review their papers should know what sort of sampling errors are to be expected in common experiments, such as determining the percentages of live and dead cells or counting the number of colonies on a plate or cells in a microscope field. Otherwise, they will not be able to judge their own data critically, or anyone else's.   Table 1: Statistics glossary: Some common statistical concepts and their uses in analysing experimental results.

Term	Meaning	Common uses
N, number of independent samples; t, the t-statistic; p, probability.
Standard deviation (s.d.)	The typical difference between each value and the mean value.	Describing how broadly the sample values are distributed.  s.d. = √−(∑ (x − mean)2/(N − 1))
Standard error of the mean (s.e.m.)	An estimate of how variable the means will be if the experiment is repeated multiple times.	Inferring where the population mean is likely to lie, or whether sets of samples are likely to come from the same population.  s.e.m. = s.d./√−N
Confidence interval (CI; 95%)	With 95% confidence, the population mean will lie in this interval.	To infer where the population mean lies, and to compare two populations.  CI = mean ± s.e.m. × t(N−1)
Independent data	Values from separate experiments of the same type that are not linked.	Testing hypotheses about the population.
Replicate data	Values from experiments where everything is linked as much as possible.	Serves as an internal check on performance of an experiment.
Sampling error	Variation caused by sampling part of a population rather than measuring the whole population.	Can reveal bias in the data (if it is too small) or problems with conduct of the experiment (if it is too big). In binomial distributions (such as live and dead cell counts) the expected s.d. is √−(N × p × (1 − p)); in Poisson dist

Points of significance: Power and sample size

Points of significance: Power and sample size

    Martin Krzywinski  
    & Naomi Altman  

    Nature Methods
    10,
    1139–1140
    (2013)
    doi:10.1038/nmeth.2738

http://www.nature.com/nmeth/journal/v10/n12/full/nmeth.2738.html

Figure 3: Decreasing specificity increases power.

(a) Observations are assumed to be from the null distribution (H₀) with mean μ₀. We reject H₀ for values larger than x^* with an error rate α (red area). (b) The alternative hypothesis (H_A) is the competing scenario with a different mean μ_A. Values sampled from H_A smaller than x^* do not trigger rejection of H₀ and occur at a rate β. Power (sensitivity) is 1 − β (blue area). (c) Relationship of inference errors to x^*. The color key is same as in Figure 1.

Figure 4: Impact of sample (n) and effect size (d) on power.
 

H₀ and H_A are assumed normal with σ = 1. (a) Increasing n decreases the spread of the distribution of sample averages in proportion to 1/√n. Shown are scenarios at n = 1, 3 and 7 for d = 1 and α = 0.05. Right, power as function of n at four different α values for d = 1. The circles correspond to the three scenarios. (b) Power increases with d, making it easier to detect larger effects. The distributions show effect sizes d = 1, 1.5 and 2 for n = 3 and α = 0.05. Right, power as function of d at four different a values for n = 3.

Anaconda / Miniconda - Python package managers

http://docs.continuum.io/anaconda/index.html
http://conda.pydata.org/miniconda.html

Anaconda is a free collection of powerful packages for Python that enables large-scale data management, analysis, and visualization for Business Intelligence, Scientific Analysis, Engineering, Machine Learning, and more.

Convert SRA to FASTQ

http://www.ncbi.nlm.nih.gov/sites/books/NBK158900/

./sratoolkit.2.4.2-ubuntu64/bin/fastq-dump --split-3 -L info --gzip -v SRR1131659

Oracle Health Sciences Translational Research Center: A Translational Medicine Platform to Address the Big Data Challenge

http://www.oracle.com/us/industries/healthcare/translational-medicine-platform-wp-1840042.pdf

Oracle, on the Oracle Health Sciences Translational Research Center, a scalable informatics solution for translational research. Its back-end data components seamlessly integrate clinical and omics data from diverse clinical data sources as well as from vendor-specific and modality-specific omics data silos, providing standardized data readily available for the front-end application. 

Finding overlap between two lists in Excel

Function

=COUNTIF(C:C,K4)

Where C:C contains one list and K4 is the value. Simply drag and drop this formula for all other values in the "K" column!

Eclipse misbehaving

If eclipse is misbehaving for some reasons, context menu not appearing, right-click does nothing ...

backup and remove ~/workspace/.metadata/.plugins

Or

Switch to a new workspace

Compound heterozygote

http://www.medicinenet.com/script/main/art.asp?articlekey=33675

Compound heterozygote: The presence of two different mutant alleles at a particular gene locus, one on each chromosome of a pair.

The human genome contains two copies of each gene, a paternal and a maternal allele. A mutation affecting only one allele is called heterozygous. Ahomozygous mutation is the presence of the identical mutation on both alleles of a specific gene. However, when both alleles of a gene harbor mutations, but the mutations are different, these mutations are called compound heterozygous. Also called a genetic compound.

http://ghr.nlm.nih.gov/glossary=compoundheterozygote

An individual who has two different abnormal alleles at a particular locus, one on each chromosome of a pair; usually refers to individuals affected with an autosomal recessive disorder

or they can be mutations at different locus of the same gene.

Syndromic vs non-syndromic (not associated with anything else, specific)

http://ghr.nlm.nih.gov/condition/nonsyndromic-deafness

Nonsyndromic deafness is hearing loss that is not associated with other signs and symptoms. In contrast, syndromic deafness involves hearing loss that occurs with abnormalities in other parts of the body. Different types of nonsyndromic deafness are named according to their inheritance patterns.

Javascript Closures -- callbacks in a loop

http://geekabyte.blogspot.ca/2013/04/callback-functions-in-loops-in.html

Solution for callbacks in a loop:
1. callbacks
2. recursive loop