TensorFlow -- an Open Source Software Library for Machine Intelligence. Deepframeworks/README.md at master · zer0n/deepframeworks. Training - Bioinformatics Core Wiki. BIST "Introduction to Biostatistics" Course Course Description This introductory course to statistics and probability theory is modeled after the traditional university course Statistics 101 and will be given by the CRG staff and PhD students. The material is offered in 5 consecutive modules (please see Course Syllabus below), each containing a morning lecture and an afternoon practicum in a computer class.

For practical exercises we will use R programming language and R Studio. However, this course is focused on statistics rather than R; therefore, each practicum is designed with the purpose to demonstrate and reinforce understanding of concepts introduced in the lecture rather than to provide a training in R. Course Objectives To introduce the basic concepts of statistics and probability and to demonstrate how they can be applied to real-life biological problems using R. Course Instructors Time and Location LECTURES: 9:30 - 13:30. Course Syllabus, Schedule, and Materials MODULE 0. LECTURE. Table Browser. Use this program to retrieve the data associated with a track in text format, to calculate intersections between tracks, and to retrieve DNA sequence covered by a track.

For help in using this application see Using the Table Browser for a description of the controls in this form, the User's Guide for general information and sample queries, and the OpenHelix Table Browser tutorial for a narrated presentation of the software features and usage. For more complex queries, you may want to use Galaxy or our public MySQL server. To examine the biological function of your set through annotation enrichments, send the data to GREAT. Send data to GenomeSpace for use with diverse computational tools. Refer to the Credits page for the list of contributors and usage restrictions associated with these data. Bridgecrest Bioinformatics: The Use of Log-Log plots to show technical reproducibility of NGS data.

Reproducibility, the ability for the same input to produce the same output, is an important concept in the sciences. One of the claimed advantages of next-generation sequencing over its microarray predecessor is the high correlation among technical replicates. One common way to show the reproducibility between two replicates is a log-log plot of RPKM values (RPKM normalizes number of reads by transcript length and mapped reads).

I would like to propose an alternative to the log-log plot that may have a clearer interpretation and may be more appropriate for read count data. Log-Log RPKM plots Below are some links showing the log-log RPKM plots between two samples. The log-log plot is created by counting the number of reads that are mapped to the genes in Sample A and Sample B. These reads are then transformed by first creating an RPKM value (normalize by transcript length and number of mapped reads) and taking the log of those RPKM values. The logged RPKM gene values of Sample B vs. Do{ GraphPad Statistics Guide.

Cluster analysis - differences in heatmap/clustering defaults in R (heatplot versus heatmap.2)? User: Pierre Lindenbaum. Major Exome Platforms Compared. UCSC Genome Bioinformatics: FAQ. Question: "What has UCSC done to accommodate the changes to display IDs recently introduced by UniProt (aka Swiss-Prot/TrEMBL)? " Response: Here is a detailed description of the database changes we have made to accommodate the UniProt changes. If you are using the proteinID field in our knownGene table or the Swiss-Prot/TrEMBL display ID for indexing or cross-referencing other data, we strongly suggest you transition to the UniProt accession number.

These changes will also affect anyone who is mirroring our site. The latest UniProt Knowledgebase (Release 46.0, Feb. 1st, 2005) was parsed and the results were stored in a newly created database sp050201. A corresponding database, proteins050201, was constructed based on data in sp050201 and other protein data sources. Two new symbolic database pointers, uniProt and proteome, have been created to point to the two new databases mentioned above.

Data visualization - How to determine best cutoff point and its confidence interval using ROC curve in R? Calculating precision and recall in R. Canopy Dynamics Lab Software. RPubs. Statistics Archives - Musings from an unlikely candidateMusings from an unlikely candidate. Generalized Additive Models in R. Generalized additive models in R GAMs in R are a nonparametric extension of GLMs, used often for the case when you have no a priori reason for choosing a particular response function (such as linear, quadratic, etc.) and want the data to 'speak for themselves'. GAMs do this via a smoothing function, similar to what you may already know about locally weighted regressions. GAMs take each predictor variable in the model and separate it into sections (delimited by 'knots'), and then fit polynomial functions to each section separately, with the constraint that there are no kinks at the knots (second derivatives of the separate functions are equal at the knots).

The number of parameters used for such fitting is obviously more than what would be necessary for a simpler parametric fit to the same data, but computational shortcuts mean the model degrees of freedom is usually lower than what you might expect from a line with so much 'wiggliness'. Ls1 = loess(cover>0~elev,data=dat5) summary(ls1) R - Shading a kernel density plot between two points. Fitting empirical distributions to theoretical models. R - Plot probability with ggplot2 (not density) Square Goldfish » R, the acf function and statistical significance. The R language provides us with a useful method to calculate the autocorrelation function (ACF) of a time series. An example of an environmental time series with a seasonal cycle is shown below, with the resulting plot: corr <- acf(series, lag.max=288,type="correlation",plot=TRUE,na.action=na.pass) (My data set has some missing values, hence the na.action=na.pass parameter.)

The output of the acf function (click to enlarge) As well as the calculated ACF, we can see two blue dashed lines across the plot. These lines indicate the point of statistical significance - values between these lines and zero are not statistically significant, while those above and below the lines (towards one and minus one) are significant. In some cases, it would be useful to know what this value is, so we can determine whether individual values from the ACF are significant or not. A hunt through the source code of the acf function gives us the information we need.

Machine learning - WEKA LibSVM weight parameter for cost. Statistica con R: ANOVA a due vie. L'analisi della varianza ad una via è utile per verificare contemporaneamente se le medie di più gruppi sono uguali. Ma questa analisi può risultare poco utile, ai fini di problemi più complessi. Ad esempio può essere necessario prendere in considerazione due fattori di variabilità, per verificare se le medie tra i gruppi dipendono dal gruppo di classificazione ("zone") o dalla seconda variabile che si va a considerare ("blocco").

In questo caso si ricorre ad una analisi della varianza a due vie (ANOVA a due vie, two-way ANOVA). Cominciamo subito con un esempio, così da rendere più facile la comprensione di questo metodo statistico. I dati raccolti vengono organizzati in tabelle a doppia entrata. Il direttore di una società ha raccolto le entrate (in migliaia di dollari) per 5 anni e in base al mese. Si vuole verificare se le entrate dipendono dall'annata e/o dal mese, oppure se sono indipendenti da questi due fattori. A questo punto si classificano i valori così inseriti. An R Introduction to Statistics. Using R for Multivariate Analysis — Multivariate Analysis 0.1 documentation. Plotting Multivariate Data Once you have read a multivariate data set into R, the next step is usually to make a plot of the data.

A Matrix Scatterplot One common way of plotting multivariate data is to make a “matrix scatterplot”, showing each pair of variables plotted against each other. We can use the “scatterplotMatrix()” function from the “car” R package to do this. To use this function, we first need to install the “car” R package (for instructions on how to install an R package, see How to install an R package). Once you have installed the “car” R package, you can load the “car” R package by typing: You can then use the “scatterplotMatrix()” function to plot the multivariate data.

To use the scatterplotMatrix() function, you need to give it as its input the variables that you want included in the plot. > wine[2:6] V2 V3 V4 V5 V6 1 14.23 1.71 2.43 15.6 127 2 13.20 1.78 2.14 11.2 100 3 13.16 2.36 2.67 18.6 101 4 14.37 1.95 2.50 16.8 113 5 13.24 2.59 2.87 21.0 118 ... A Profile Plot.