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To access this count table:
Go to the W:\training\rnaseq\runs\run3_RNAseq\results folder that contains the results from running the nfcore/rnaseq pipeline. The output folders from task 3 look like this:
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The count table can be found in the star_salmon folder. A list of files and folders in the star_salmon folder will look like this:
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a. First create a new folder in H:\workshop\RNAseq . Call it something suitable, such as ‘DE_analysis_workshop’
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gene_id gene_name SRR20622172 SRR20622173 SRR20622174 SRR20622175 SRR20622176 SRR20622177 SRR20622178 SRR20622179 SRR20622180 ENSMUSG00000000001 Gnai3 7086 4470 2457.002 2389 6398 2744 2681 3961 4399 ENSMUSG00000000003 Pbsn 0 0 0 0 0 0 0 0 0 ENSMUSG00000000028 Cdc45 1232.999 827 42 57 1036 55 78 88 89 ENSMUSG00000000031 H19 200 139 2 0 143.622 1 17.082 24 16.077 ENSMUSG00000000037 Scml2 70 57.001 8 8 66.999 16 23 27.999 29 ENSMUSG00000000049 Apoh 0 0 1 0 2 2 1 3 0 ENSMUSG00000000056 Narf 1933 1480 519 497 1730 539 365 458 536 ENSMUSG00000000058 Cav2 6008 3417 1347.001 1344 5482 1367 2669.001 4358 4365.832 ENSMUSG00000000078 Klf6 3809 2732 4413.001 3483.978 3559 4491 3209 3980 4626 |
d. In the same W:\training\rnaseq\runs\run3_RNAseq\results\star_salmon directory there will be a file called metadata.xlsx . Copy this file to your ‘data’ folder as well. This file will normally need to be manually created by you to match your sample IDs and treatment groups, but we created this file already for you to use. This samples table needs 3 columns called ‘sample_name’, containing the sample names seen in the count table (column names), ‘sample_ID’, which is the (less messy) names you want to call the samples in this analysis workflow, and ‘group’, which contains the treatment groups each sample belongs to. The contents of this file look like this:
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#### 4. Import your count data ####
# Make sure you have: a) your count table (salmon.merged.gene_counts.tsv file, if you used Nextflow nfcore/rnaseq to analyse your data). Copy this to a subdirectory called 'data'. b) your metadata file. This should be either an Excel file called 'metadata.xlsx' or a tab-separated text file called 'metadata.txt'. It needs 3 columns called 'sample_name', 'sample_ID' and 'group'. The sample names should be EXACTLY the same as the names in the count table. These names are often uninformative and long, so the 'sample_ID' is the sample labels you want to put on your plots. E.g. if you have a 'high fat' group, you might want to rename the samples HF1, HF2, HF3, etc)
## USER INPUT
# Set working directory.
# Change this to your working directory (In the RStudio menu: Session -> Set working directory -> Choose working directory)
setwd("C:/Users/whatmorp/OneDrive - Queensland University of Technology/Desktop/Projects/RNA-Seq downstream analysisH:\workshop\RNAseq")
# Import your count data. make sure you've created a 'data' subdirectory and put the count table file there.
metacountdata <- read.table("./data/salmon.merged.gene_counts.tsv", header = TRUE, row.names = 1)
# Import metadata. Again, need a metadata.xlsx file in the data subdirectory.
meta <- read_excel("./data/metadata.xlsx")
# Remove 1st columns of metadata (gene_name)
counts <- metacountdata[ ,2:ncol(metacountdata)]
# Rename sample names to new sample IDs
counts <- counts[as.character(meta$sample_name)]
colnames(counts) <- meta$sample_ID
# Counts need to be rounded to integers
counts <- ceiling(counts) |
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