How to generate a rank file from gene expression data in R

Previously I wrote about how to make a gene expression rankfile using the unix shell. While this works for me just fine, there are some differences in the behaviour of shell tools like awk an sed that make it unusable for mac users.

Therefore, I think the best solution is to show you how to do this in R, which you can install on Linux, Mac and Windows systems.

The expression data for this demo looks like this (the first 5 columns).


Name                        logFC               logCPM            LR                PValue
ENSG00000134294.9_SLC38A2   0.365464972841137   8.35504063447063  80.9697378748286  2.29200821312451e-19
ENSG00000164692.13_COL1A2   0.369440969815233   6.9371167845581   59.239238358843   1.39621819298599e-14
ENSG00000117152.9_RGS4      0.417512339007825   6.27805181231213  48.690887963755   2.99655418444362e-12
ENSG00000120738.7_EGR1      -0.448872068345368  5.72373823077344  40.5159113813129  1.95021420597484e-10
ENSG00000236552.1_RPL13AP5  -0.263118776572713  7.87437056751926  40.4331725003112  2.03457218801857e-10
ENSG00000104332.6_SFRP1     0.493423326062289   4.80359815087205  38.5863976773987  5.23827228439336e-10
ENSG00000187957.7_DNER      0.470592983554025   4.33515164897497  37.0721600878008  1.13837471974503e-09
ENSG00000111335.8_OAS2      0.506694725778856   4.64035138417216  36.5587612062587  1.48132665393875e-09
ENSG00000137809.12_ITGA11   0.358068410134363   6.32535717802246  35.7230925508279  2.27451845482865e-09

Here is the code for reading a tsv and calculating the rank metric you will need to replace column names with the ones in your dataset.

x<-read.table("expression_edgeR.xls",sep="\t",header=T)
attach(x)
x$fcSign=sign(logFC)
x$logP=-log10(PValue)
x$metric=logP/fcSign
y<-x[,c("Name", "metric")]
write.table(y,file="expression.rnk",quote=F,sep="\t",row.names=F)

Read table command loads the data as a data frame called "x", recognising the data is tab delimited and contains a header line.

Attach command allows R to recognise columns by their name rather than by their index ($1,$2,etc), this just makes things a bt easier.

"x$fcSign=sign(logFC)" generates a new column that is the sign of fold change. 

"x$logP=-log10(PValue)" generates a new column that is the negative log10 of the p-value.

"x$metric=logP/fcSign" generates a new column that is the metric score that we'll use for ranking and pathway analysis

"y<-x[,c("Name", "metric")]" makes a new table called "y" that consists only of gene names and metric scores. 

Write.table command outputs the gene name and rank metric data to a new file called "expression.rnk" gene

In the case above, the gene identifier and gene name are combined and will need to be split before pathway analysis. GSEA can use either Ensembl accession number or gene names. If you use gene names, then take note that many gene names are not unique and will need to be collapsed somehow.
Location: St Kilda VIC 3182, Australia

Popular posts from this blog

Data analysis step 8: Pathway analysis with GSEA

Image
In our RNA-seq series so far we've performed differential analysis and generated some pretty graphs, showing thousands of differentially expressed genes after azacitidine treatment. In order to understand the biology underlying the differential gene expression profile, we need to perform pathway analysis. We use Gene Set Enrichment Analysis ( GSEA ) because it can detect pathway changes more sensitively and robustly than some methods. A 2013 paper compared a bunch of gene set analyses software with microarrays and is worth a look. Generate a rank file The rank file is a list of detected genes and a rank metric score. At the top of the list are genes with the "strongest" up-regulation, at the bottom of the list are the genes with the "strongest" down-regulation and the genes not changing are in the middle. The metric score I like to use is the sign of the fold change multiplied by the inverse of the p-value, although there may be better methods out there
Read more

Two subtle problems with over-representation analysis

Image
Over-representation analysis is a helpful and really frequently used technique for understanding trends from omics data and gene lists more generally. Just to demonstrate this, I tabulated the number of citations of some of the most popular web tools and packages, which reaches a massive 191k citations! But if you have used some of these tools, you will notice that they yield subtly different results. We were curiuous about that and ran a whole bunch of investigations into the internal workings of these tools, in particular clusterProfiler. We identified two subtle problems with clusterProfiler, which we unpack in detail in our new publication , but here I will give you a quick overview. Problem #1: The background problem The first one we call the “background problem,” because it involves the software eliminating large numbers of genes from the background list if they are not annotated as belonging to any category. This results in removing a large number of unannotated genes from the b
Read more

Uploading data to GEO - which method is faster?

If you have had to upload omics data to GEO before, you'll know it's a bit of a hassle and takes a long time. There are a few methods suggested by the GEO team if you are using the Unix command line: Using 'ncftp' ncftp set passive on set so-bufsize 33554432 open  ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov cd uploads/your @mail.com_ yourfolder put -R Folder_with_submission_files Using 'lftp' lftp  ftp://geoftp:yourpasscode@ftp-private.ncbi.nlm.nih.gov cd uploads/ your @mail.com _ yourfolder mirror -R Folder_with_submission_files Using 'sftp' (expect slower transfer speeds since this method encrypts on-the-fly) sftp  geoftp @s ftp-private.ncbi.nlm.nih.gov password: yourpasscode cd uploads/ your @mail.com _ yourfolder mkdir new_geo_submission cd new_geo_submission put file_name Using 'ncftpput' (transfers from the command-line without entering an interactive shell) Usage example: ncftpput -F -R -z -u  geoftp  -p "yourpasscode"
Read more