6

In two words: incidence and funding I'm not an expert on this topic, but I assume it has something to do with the incidence of breast cancer itself: Breast cancer is the most common cancer in American women, except for skin cancers. Currently, the average risk of a woman in the United States developing breast cancer sometime in her life is about 13%. This ...


5

I assume you're familiar with the various issues surrounding FPKMs, so I'll not expound upon them. As a general rule, you should be using gene IDs rather than gene names, since the former are unique while the latter are not. If you only have access to data quantified on gene names, then the appropriate way to merge RPKMs is with a weighted sum: $FPKM_{gene}...


5

In the linked article the authors formalize microarray analysis as the study of the joint distributions of $\overrightarrow{X}_i$ and $Y_i$, where $\overrightarrow{X}_i$ is a vector of random variables, the distribution of each of which (i.e. each of $X_{ji}$) is determined by the level of expression of gene j in sample i, and $Y$ is some response or ...


4

I have seen many posts regarding counts to RPKM and TPM. There’s your answer then: FPKM = RPKM. It’s simply a more accurate name. Speaking of RPKM for paired-end data is discouraged because the reference to “read” in this context lends itself to ambiguity. But mathematically the quantity is the same: we are counting fragments, not individual reads (of ...


4

On google there are many tutorials about quantile normalzation, for example here. In that tutorial they made a function to calculate quantile normalization. Here an example with that function on your small data set. data gene_id SRR896664 SRR896663 SRR896665 1 ENSG00000000003 46106 36353 40614 2 ENSG00000000005 198 399 ...


4

There is no reason your t-test should reproduce edgeR. In fact, edgeR exists because t-test is inappropriate. edgeR does the tests by pooling information from all genes, because with the low number of replicates your t-test doesn't give sufficient statistical power. What you need to do is: check visually your gene and make a decision yourself based on ...


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

Ma be CQN from Bioconductor will be useful, though it doesn't perform just quantile normalisation.


3

The $log(CPM)$ of any low-moderately expressed gene will be negative. There is nothing unexpected there. Your statistics are inappropriate for a variety of reasons. Firstly, a CPM is not a robust value that's comparable between samples (this is why CPMs aren't used for statistics). edgeR performs more appropriate normalization and incorporates that into its ...


3

The best way to deal with this is to use unique gene IDs, for example ensembl accession numbers. So use the ensemble gtf annotation when quantifying the read counts and not the gene symbols. Just to illustrate, when I look for "5S_rRNA" in ensembl's annotation, i see 18 different "genes" with that gene symbol. But which 2 you have is unclear now. grep "...


3

The answer to this really depends on the type of data you're retrieving from GEO. Microarray data sets should have a normalised matrix of expression values uploaded as part of the entry. getGEO() from the GEOquery package will return a list of ExpressionSets - in many cases this list will have a length of 1. An ExpressionSet has an exprs() accessor for ...


3

There is not going to be a public database with this data. Apart from anything else, generating data for early human development is difficult and ethically tricky. Also, most people who care about development would probably think that a whole body average would be meaningless. The best you could do would be to select tissues you were interested in from ...


3

In general, survival analysis can be said to be composed of two steps; Cox regression, with which you calculate the "hazard ratio" based on your variables, and a "Kaplan-Meier (KM) estimate", which is used to visuazlize the data. Here is a nice tutorial for doing survival analysis with the survival and survminer packages. The latter includes ggplot2 kind ...


3

You should use a proper statistical framework for RNA-seq dfferential analysis (which includes FC calculation). Standard tools for this are (among others) edgeR or DESeq2. You could use tximport to import RSEM outputs into R and then use its output for e.g. DESeq2. The linked manual provides example code for this. Please read the manuals of edgeR or DESeq2 ...


3

If helpful for anyone else, I found that all and much more are available. See below 2 links. https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/dataset.cgi?study_id=phs000424.v8.p2&phv=169091&phd=3910&pha=&pht=2742&phvf=&phdf=&phaf=&phtf=&dssp=1&consent=&temp=1 https://ftp.ncbi.nlm.nih.gov/dbgap/studies/phs000424/...


3

You have a few options: Downsample the fastq files and rerun the entire analysis. You can do this with seqtk sample. Downsample the BAM files, which you can do with samtools view -s. Divide all of the counts in the counts files by some factor and round that to an integer. I personally prefer option 2, since it's quick and doesn't usually have any side-...


3

It's hard to say without knowing how different the subtypes are. If you do a common WGCNA, you may find modules related to the differences between A and B as well as to drivers of expression variation in A and B (common and separate). Since A has fewer samples, the variation will have to be stronger to be reflected by WGCNA modules. You can run the WGCNA, ...


3

Type of data you need depends on the downstream applications and since you would like to carry out DEA with DESeq2, you would need raw counts (non-normalized). There are many ways to import/download TCGA data, one such tool, the TCGAbiolinks package gives a nice interface for not only downloading the read count (or pre-processed) data but also associated ...


3

You ask about which "genes are expressed" and then you mention "if a gene is up or down regulated". These are different, and given your application I think what you actually want to know if these marker genes are expressed. This is not a question of up/down-regulation relative to a control, so you do not need control samples for this. It ...


2

Reading Kaufmann and van Oudenaarden (2007), it seems to validate the first alternative (using results from the Central Limit Theorem): Although biochemical fluctuations influence all stages of gene expression, those involving molecules in extremely low abundance are expected from a statistical standpoint to be larger in magnitude and therefore to ...


2

Your calculations seem right, perhaps there was an error on their side. I also looked at the number of samples reported, but they use the same amount of samples in each case. Because they are testing whether Linc-ZNF469-3 is different in several tissues, they should have used a multiple test correction for the p-value (although reporting the original ...


2

You can use countToFPKM package. This package provides an easy to use function to convert the read count matrix into FPKM matrix; following the equation in The fpkm() function requires three inputs to return FPKM as numeric matrix normalized by library size and feature length: counts A numeric matrix of raw feature counts. featureLength A numeric ...


2

Processed microarray data is commonly represented as log_2(expression). This data transformation is used because the data more closely fit a normal distribution in log space. With such a transformation, negative values represent very small amounts, rather than the absence of something. Larger values mean that more stuff is being produced (presumably ...


2

There has been a lot of work done on this problem already, particularly these two papers: https://www.biorxiv.org/content/10.1101/576827v2 https://www.biorxiv.org/content/10.1101/574574v1


2

For the first part, do you mean that the file is too large to be run on your computer? For the second, if I understood correctly, you can use igraph or ggnet2 to color code the nodes based on the conditions you have and see if communities are formed based on condition. This would be a simple way to do this kind of thing. I personally prefer using ggnet2 ...


2

Assuming that you have downloaded processed file, according to your link, it is either plain text (compressed) or GTF. If you would have plain text you probably would have posted a snapshot or sample of it, so I assume you have the GTF. According to format description for GTF-files: Format consists of one line per feature, each containing 9 columns of data,...


2

To measure if two genes are functional similar I developed the BioCor package. It calculates a similarity score between genes by the measuring the amount of pathways shared. However, it doesn't take into account the strength of the connection between two proteins. You could multiply the similarity by a value between 0 and 1 according to the number of ...


2

You can use the following code to calculate the coefficient of variation: # expr is your expression matrix. SD <- apply(expr, 1, sd) CV <- sqrt(exp(SD^2) - 1) It might be implemented in some package but it is so brief that you can write it again yourself. Then you can filter out those that are below certain percentage of the distribution of CV (like ...


2

There are two topics here to be discussed. Gene Set Enrichment Analysis and a kernel Density plot. GSEA The Wikipedia page about Gene Set Enrichment Analysis covers well the principle and the tools that can be used to calculate it. To cite it: The general steps are summarized below: Calculate the enrichment score (ES) that represents the amount to which the ...


2

Neither of RPKM, FPKM or TPM is a good choice. All these methods are similar, they only perform the operations in a slightly different order. Eventually they correct for gene length and sequencing depth. Correction for sequencing depth is necessary but not sufficient in many cases. Correction for length is typically not done for a differential analysis as it ...


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