Data processing and quality control

What we produced: FASTQ files (if we don't have them), FASTQC reports, BAM and SAM files.

Figure 1: schematic overview of the pipeline for RNA-seq data analysis.

Differential expression analysis

What we produced: script for differential expression analysis, table with read counts (tab separated format, 7 columns, ENSG ids).

RNA-seq data can be difficult to interpret (especially in terms of differential expression quantitation). Thus, we decided to adopt a simple method for the analysis, based on counting, for each gene and for each sample, the number of available reads and then testing for significant differences between two experimental conditions or groups.

We wrote an R script that automatically creates a PDF file (in the current directory) with all the figures necessary for visual inspection and result interpretation. The input is a tab separated file with reads counts.

ensembl_id	melanocyte_1	melanocyte_2	melanoma_1	melanoma_2
ENSG00000000003	1964	2409	2328	2451
ENSG00000000005	0	2	10	12
ENSG00000000419	15122	19592	38225	36654
ENSG00000000457	12129	14893	7483	7812
ENSG00000000460	21930	25575	13123	13840
ENSG00000000938	48	58	26	42
ENSG00000000971	125	229	124	236
ENSG00000001036	11611	14125	14067	13518
ENSG00000001084	11429	13795	3549	3279

Figure 2: Example input format for DE analysis.

We tested two designs, as illustrated in the tables below: normal cells vs cancerous cells (4 samples), cancerous cells vs cancerous drug treated (4 samples).

Table 1 and 2: tested designs.

Visual exploration of the samples

Prior to checking distances between our samples, we applied a regularized-logarithm transformation (rlog) to stabilise the variance across the mean. The effects of the transformation are shown in the figure below.

Figure 3: Effect of the regularized-logarithm transformation on 'melanocyte_1' and 'melanocyte_2' samples.

We noticed that this step was particularly important for genes with low read counts.

We then checked the distances between our samples by performing Principal Components Analysis of the count data.

Figure 4: Principal Components Analysis (PCA) plot, normal vs cancerous cells.

We observed that differences between groups (normal vs cancerous cells represented in the PCA plot above) were greater than intra-groups differences, which is expected in this kind of design. However, as the inter-group differences were so pronounced, we figured that a great amount of genes would appear as differentially expressed. This is why we decided to apply really stringent thresholds for the detection:

• - log2 fold change (logFC) > 5 for upregulated genes or log2 fold change (logFC) < -5 for downregulated genes.
• - AND adjusted-p-value < 0.01

Differential expression analysis

Firstly, we took a look at the raw data (prior to any kind of normalization). We calculated mean counts for each gene and by condition and then the log2 fold change.

Figure 5: Principal Components Analysis (PCA) plot, normal vs cancerous cells.

Figure 6: Principal Components Analysis (PCA) plot, normal vs cancerous cells.