Statquest笔记3—DEseq2 (No.60)

Tow main problems in library normalization

Problem1 Adjusting for differences in library sizes

Problem1

Problem2 Adjusting for differences in library composition

Problem2

We’ll start with a small dataset to illustrate how DESeq2 scales the different samples.
The goal is to calculate a scaling for each sample. The scaling factor has to take read depth and library coposition into account.

Step 1 Take the log of all values

Step1

Step 2 Average Each Row

Step2

One thing cool about the average of log values is that this average is not easily swayed by outliers. Averages calculated with logs are called “Geometric Averages”.

Step 3 Filter out Genes with Infinity

In general, this step filters out genes with zero read counts in one or more samples.
In theory, this helps focus the scaling factors on the house keeping genes

Step4

Step 5 Calculate the median of the ratios for each sample

Step5

Step 6 Convert the medians to “normal numbers” to get the final scaling factors for each sample

The median values are exponents for e.

Step 7 Divide the original read counts by the scaling factors

Step7

Summary of DESeq2’s Library Size Scaling Factor

Logs eliminate all genes that are only transcribed in one sample type (liver vs. spleen). They also help smooth over outlier read counts (via the Geometric Mean).
The median further downplays genes that soak up a lot of the reads, putting more emphasis on moderately expressed genes.

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