Month: January 2016

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Beware of biological variability in your *-Seq experiments

From this excellent paper on biological variability in RNA-Seq experiments (bold highlights are mine):

“Biological variability has important implications for the design, analysis and interpretation of RNA-sequencing experiments. […] If only a few biological replicates are available, it will be impossible to estimate the level of biological variability in expression for each gene in a study. Supplementary Table 1 summarizes a large number of published RNA-sequencing studies over the past three years. In every case, except for the two studies we analyzed here, conclusions were based on a small number (n ≤ 2) of biological replicates. One goal of RNA-sequencing studies may be simply to identify and catalog expression of new or alternative transcripts. However, all of these studies make broader biological statements on the basis of a very small set of biological replicates.

Our analysis has two important implications for studies performed with a small number of biological replicates. First, significant results in these studies may be due to biological variation and may not be reproducible; and second, it is impossible to know whether expression patterns are specific to the individuals in the study or are a characteristic of the study populations. These ideas are now widely accepted for DNA microarray experiments, where a large number of biological replicates are now required to justify scientific conclusions. Our analysis suggests that as biological variability is a fundamental characteristic of gene expression, sequencing experiments should be subject to similar requirements.”

If you are doing RNA-Seq, be very vigilant in your experimental design and find a way to incorporate more replicates, even at the expense of testing fewer comparisons.   It’s better to test one comparison (tissue X vs. Y, for example) with 5 or more replicates than to test three comparisons (Tissue X vs. Y, Y vs. Z, and X vx Z) with only 2 replicates for each tissue type.

 

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Hands-on with cancer mutational signatures, part 2

In this second part of the “Hands On” series, I want to address how to create the input for the MatLab mutational signature framework from the output of my python code to prepare the SNP data for analysis.

First, creating a Matlab .mat file for input to the program.   The code is expecting an input file that contains a set of mutational catalogues and metadata information about the cancer type and the mutational types and subtypes represented in the data.

Fig 1. The required data types within one .mat file to run the framework.
Fig 1. The required data types within one .mat file to run the framework.

As you can see from Fig 1., you need to provide a 96 by X matrix, where X is the number of samples in your mutational catalogue.  You also need an X by 1 cell array specifying sample names, a 96 by 1 cell array specifying the subtypes (ACA, ACC, ACG, etc…) and a 96 by 1 cell array specifying the types (C>A, C>A, C>A, etc…).  These must correspond to the same order as specified in the “originalGenomes” matrix or the results won’t make sense.

My code outputs .csv files for all of these needed inputs.  For example, when you run my python code on your SNP list, you will get a “subtypes.csv”, “types.csv”, “samples.csv”, and “samples_by_counts.csv” matrix (i.e., originalGenomes) corresponding to the above cell arrays in Fig 1.

Now, the challenge is to get those CSV files into MatLab.  You should have downloaded and installed MatLab on your PC.  Open MatLab and select “Import Data.”

Fig 2. Select the "Import Data" button.
Fig 2. Select the “Import Data” button.

Browse to one of the output CSV files and select it.  It will open in a new window like in Fig 3 below:

Fig 3. The data import window from MatLab.
Fig 3. The data import window from MatLab.

Be sure to select the correct data type in the “imported data” section.  Also, select only row 1 for import (row 2 is empty).  Once you’re finished, click Import Selection.  It will create a 1×96 cell called “types.”  It looks like this:

Fig 4. The new imported cell data "types."
Fig 4. The new imported cell data “types.”

We’re almost done, but we have to switch the cell to be 96×1 rather than 1×96.  To do this, just double-click it and select “transpose” in the variable editor.   Now you should repeat this process for the other CSV input files, being sure to select “matrix” as the data type for the “samples_by_counts” file.   Pay special attention to make sure the dimensions and data types are correct.

Once you have everything in place you should be ready do run the mutational analysis framework from the paper.   To do this, open the “example2.m” Matlab script included with the download.  In the “Define parameters” section, change the file paths to the .mat file you just created:

Fig 5. Define your parameters for the signature analysis.
Fig 5. Define your parameters for the signature analysis.

 

Here you can see in Fig 5, I’ve specified 100 iterations per core, a number of possible signatures from 1-8, and the relative path to the input and output files.  The authors say that ~1000 iterations is necessary for accuracy, but I’ve found little difference in the predictions between 50-500 iterations.   I would perform as many iterations as possible, given constraints from time and computational power.

Note also that you may need to change the part of the code that deals with setting up a parallel computing pool.  Since MatLab 2014, I believe, the “matlabpool” processing command has been deprecated.   Substituting the “parpool” command seems to work fine for me (Mac OS X 10.10, 8 core Macbook Pro Retina) as follows:

if ( parpool('local') == 0 )

parpool open; % opens the default matlabpool, if it is not already opened

end

This post is getting lengthy, so I will stop here and post one more part later about how to compare the signatures you calculate with the COSMIC database using the cosine similarity metric.