6

The more genes you have the more robust the scaling factor is (among the reason why one doesn't normalize to ERCC spike-ins without a compelling reason), so I suppose in theory it's better to filter after determining the scale factor. Having said that, I'd be surprised if the results changed much either way. Unless you end up filtering out a LOT of genes the ...


6

Simulating genotypes with realistic correlation structures is indeed not so simple, and there's quite a few papers dedicated entirely to that (e.g. https://bmcgenet.biomedcentral.com/articles/10.1186/s12863-015-0173-4). Also, DEPICT (https://data.broadinstitute.org/mpg/depict/index.html) comes with a number of simulated GWASs to generate the nulls, so that's ...


6

R supports logistic regression, which would seem to be the most efficient method for tackling this question. Assuming the "Chemo" variable is the type of chemo the code would be something like: glm( (response_to_chemo == "yes") ~ BMI + Chemo + Predictor + DJANGO + gender, family="binomial") EDIT: Corrected a typo (added a missing double quote)


5

FPKM/TPM values are generally log-normally distributed. Reference : Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels


5

I think the problem is that you have time as a linear value rather than a factor. While this naively makes sense (after all, you have to go through 3 hours awake before you can get to 9 hours awake), what you end up doing is fitting a single coefficient to each gene of "linearly changes with time". What you want instead is factor, so you can have a system ...


5

R is a reference to R0, otherwise known as the basic reproductive rate, and means the number of new cases from a single patient infection. R0 has to be above 1 for a disease to persist. It is a classic equation which account for the probability of transmission against immunity. The y-axis is the number of infecteds, very poorly presented. The idea is that ...


4

I don't have enough experience to answer which probabilistic distribution should be used. However, this questions also also asks how to estimate parameters of the distributions. If a binomial distribution is chosen, then Heng Li's paper titled "A statistical framework for SNP calling, mutation discovery, association mapping and population genetical ...


4

As far as I remember the exact probability computation is an open problem. The reason is that potential motifs can overlap, which makes probability computations for an arbitrary string non-trivial, and depends on the motif. For example if he have a binary string of four digits, the probability of "01" will be 11/16, while the probability of "11" will be 8/...


4

Honestly I'd just use multiBigwigSummary and then plotCorrelation from deepTools for this, but I'm a bit biased. There, the idea would be to consider each gene as a unit (you could instead use bins, but I don't think that would as nicely do what you want), namely by giving the tools a BED or GTF file input. It would then calculate the average signal in each ...


4

A reverse image search on google leads to the exact code used to produce the second image: [Xcoarse, Ycoarse] = meshgrid([0 1 2 3], [0 1 2 3]); [Xfine, Yfine] = meshgrid(linspace(0,3,3000), linspace(0,3,3000)); DataCoarse = [ 1 2 4 1; ... 6 3 5 2; ... 4 2 1 5; ... 5 4 2 3]; DataBicubicFine = interp2(Xcoarse, Ycoarse, ...


4

I assume that you are talking about the implementation of these methods in the limma package. Otherwise this answer does not apply. I think that your questions can be answered with some simulations where we can test with some "genes" with a known relationship: library("limma") set.seed(123) # Create some genes and samples nGenes <- 40 nSamples <- ...


4

The most intuitive explanation is also explained in the background section: Conceptually, this methodology can be understood as a change in coordinate systems for gene expression data, from genes to gene sets. Which I think it is explained in Figure 1 of the paper, which is also one of the most informative I found about the methods of an algorithm1. I'...


4

You can easily color 3D pca plots in R based on the code given below: library("scatterplot3d") colors <- c("#999999", "#E69F00", "#56B4E9") # Number of color according to the number of groups colors <- colors[as.numeric(iris$Species)] # you can put here the column containing the name of population or sample etc. pca1 <- prcomp(iris[, -5]) s3d <-...


4

I have noticed a few errors: 1) You define a bunch of variables (Species, Class, ...) but then instead of creating your data frame data with these you create it by reading from text. I don't think this is good practice, you should do so like in your example code. 2) With the way you define, your data frame does not contain columns like Class or Species but ...


4

This may not strike most as a bioinformatics, but getting the key clinical outcome is essential in understanding the molecular basis of pathogenicity. I think the mortality rate is over-reported. This is not to say the situation of 2019-nCov is not serious - it is very serious. The two essential factors missing in your equation for 2019-nCov are: Age. ...


4

There is no such thing as a hypergeometric test, at least in statistical textbooks. It's a fisher test based on hypergeometric distribution. If it is chip-seq for the same target, i.e biological replicates, significance of overlap is not quite meaningful. You get more information by for example checking the correlation of the coverage between your ...


4

Permutation as suggested by @StupidWolf's comment is essential to understand what's going on. If permutation makes this pattern go away, then you have a problem with your model specification, there's something uncorrected. If your data are weird, well, that's just how they are. But this argues to me that something else is going on confounding your ...


3

from Bio import SeqIO for normal, treated in zip(SeqIO.parse("/data/statistic/normal_samples", "fasta"), SeqIO.parse("/data/statistic/with_treatment", "fasta")): ... do stuff... That's generally how you zip iterators together in python.


3

You're misinterpreting the moderated T-statistic, it's basically the fold-change divided by its variance. The p-value comes directly from that, so if you filter by moderated fold-change you're just setting an unknown (unless you go through the trouble of figuring it out) p-value threshold. Instead, do exactly as you've been doing and set appropriate (...


3

Your null hypothesis would be that the fold-changes are 0, so you can either do the T-test accordingly or simply do away with the fold changes and perform the T-test between the raw values in the group (this is preferable to performing a T-test of one group vs. 0, since it allows you to assess the expected variability around 0). Note, however, that 3 samples ...


3

The key point here is whether or not the values are approximately normally distributed and whether any transformations can be applied to make them so. For a t-test, the most important thing is that there is no relationship between the mean and the variance. Its also correct that you don't want to divide by the mean of the controls. You want to retain all ...


3

For MAC OSX, the executable found in arlsumstat_macosx (arlsumstatmac_64bit) seem to work appropriately. The directory for arlsumstat_macosx does not contain any examples but you can use example files from arlecore_macosx/Example files_linux using this syntax. The complication is that arlsumstatmac_64bit will look in the current directory for the file ...


3

It depends whether you want to treat the peak intensities as binary (comparing presence/absence of peaks in the sets) or continuous (comparing the relative magnitudes of the peaks). Binary For starting out, a simple binary comparison may be appropriate. You can use a peak caller of your choosing to identify peaks in each sample according to your desired ...


3

Rather than working on the base level, you could probably work on say gene level counts. Kendall's tau, an ordinal association metric, can then be used as an appropriate correlation measure. If $X$ and $Y$ are your iCLIP replicates, $i$ represents gene index and $(x_i, y_i)$ represents the number of RBP binding sites in $X$ and $Y$ respectively for the $i^{...


3

When the gene expression is scaled and centred you reduce the difference between genes. Imagine you have one gene A that is highly expressed usually and has a standard deviation of 500 units compared to a gene B that is not much expressed and only have a standard deviation of 5. In the scaled and centred genes both contribute the same because A usually ...


3

The PCHeatmap function (renamed DimHeatmap in Seurat v3) can be used to help determine the number of principal components to use in downstream analysis, as well as to visualize the top genes contributing to each PC. Both cells and genes are ordered by their PC scores, and by default the 15 genes with the highest and 15 genes with the lowest PC loadings are ...


3

The general approach would be to loop through every column starting at column 2. You can use numeric indexes to do that. For each column, check its type. If it is a factor, use your chisq.test method. If it is of numeric type, use your t.test approach. Write the results to a list so you can parse them later. This will be a good exercise for you in trying ...


3

R, the reproductive number, relates to the average number of (new) people that will get sick (infected) per person that is already sick. For instance, if $R=2$ and you start with a single infected person, then the next generation will be 2 people, those 2 people will make 4 people sick, those 4 people will make 8 people sick, and so on. Is R a fixed ...


3

The p-value adjustments corrects the inflated number of significant results when performing many significance tests. If you have a test with 5 % false positive rate and but you run it several times the chances of getting a false positive results in any of these tests is far greater than 5 %. For example repeating the test with 5 % false positive rate 20 ...


3

It is not like your conventional frequency which adds up to one. The density is low because the width of your bins is huge, and the number of observations you have is low. From this by Wickham, the basic kernel is: where K is the kernel and h is the bandwidth (Scott, 1992b) If you don't specify it, by default h will revert to nrd0: library(rtracklayer) ...


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