Count normalization with DESeq2

Author

Meeta Mistry, Radhika Khetani, Mary Piper, Jihe Liu

Published

May 20, 2020

Approximate time: 60 minutes

Learning Objectives

List different “uninteresting factors” considered during normalization
Describe some common normalization methods and recommendations for its use
Create a DESeqDataSet object and discuss the structure of the object
Demonstrate how to normalize counts using DESeq2

Normalization

The first step in the DE analysis workflow is count normalization, which is necessary to make accurate comparisons of gene expression between samples.

The counts of mapped reads for each gene is the result of RNA expression (“interesting” part) in addition to many other factors (“uninteresting” part). Normalization is the process of adjusting raw count values to account for the “uninteresting” factors. In this way the expression levels are more comparable between and/or within samples.

The main “uninteresting” factors often considered during normalization are:

Sequencing depth: Accounting for sequencing depth is necessary for comparison of gene expression between samples. In the example below, each gene appears to have twice the expression in Sample A relative to Sample B. However, this is the consequence of Sample A having doubled sequencing depth.

Note

In the figure above, each pink and green rectangle represents a read aligned to a gene. Reads connected by dashed lines connect a read spanning an intron.

Gene length: Accounting for gene length is necessary for comparing expression between different genes within the same sample. In the example below, Gene X and Gene Y have similar levels of expression, but the number of reads mapped to Gene X would be many more than the number mapped to Gene Y because Gene X is longer.
RNA composition: A few highly differentially expressed genes between samples, differences in the number of genes expressed between samples, or presence of contamination can skew some types of normalization methods. Accounting for RNA composition is recommended for accurate comparison of expression between samples, and is particularly important when performing differential expression analyses [1].

In the example below, imagine the sequencing depths are similar between Sample A and Sample B, and every gene except for gene DE presents similar expression level between samples. The counts in Sample B would be greatly skewed by the DE gene, which takes up most of the counts. Other genes for Sample B would therefore appear to be less expressed than those same genes in Sample A.

While normalization is essential for differential expression analyses, it is also necessary for exploratory data analysis, visualization of data, and whenever you are exploring or comparing counts between or within samples.

Common normalization methods

Several common normalization methods have been developed to account for these differences:

Normalization method	Description	Accounted factors	Recommendations for use
CPM (counts per million)	counts scaled by total number of reads	sequencing depth	gene count comparisons between replicates of the same sample group; NOT for within sample comparisons or DE analysis
TPM (transcripts per kilobase million)	counts per length of transcript (kb) per million reads mapped	sequencing depth and gene length	gene count comparisons within a sample or between samples of the same sample group; NOT for DE analysis
RPKM/FPKM (reads/fragments per kilobase of exon per million reads/fragments mapped)	similar to TPM	sequencing depth and gene length	gene count comparisons between genes within a sample; NOT for between sample comparisons or DE analysis
DESeq2’s median of ratios [1]	counts divided by sample-specific size factors determined by median ratio of gene counts relative to geometric mean per gene	sequencing depth and RNA composition	gene count comparisons between samples and for DE analysis; NOT for within sample comparisons
EdgeR’s trimmed mean of M values (TMM) [2]	uses a weighted trimmed mean of the log expression ratios between samples	sequencing depth and RNA composition	gene count comparisons between samples and for DE analysis; NOT for within sample comparisons

RPKM/FPKM: not recommended for between sample comparisons

While TPM and RPKM/FPKM normalization methods both account for sequencing depth and gene length, RPKM/FPKM are not recommended. The reason is that the normalized count values output by the RPKM/FPKM method are not comparable between samples.

Using RPKM/FPKM normalization, the total number of RPKM/FPKM normalized counts for each sample will be different. Therefore, you cannot compare the normalized counts for each gene equally between samples.

RPKM-normalized counts table

gene	sampleA	sampleB
XCR1	5.5	5.5
WASHC1	73.4	21.8
…	…	…
Total RPKM-normalized counts	1,000,000	1,500,000

For example, in the table above, sampleA has a greater proportion of counts associated with XCR1 (5.5/1,000,000) than sampleB (5.5/1,500,000), even though the RPKM count values are the same. Therefore, we cannot directly compare the counts for XCR1 (or any other gene) between sampleA and sampleB, because the total number of normalized counts are different between samples.

Note

This video by StatQuest shows in more detail why TPM should be used in place of RPKM/FPKM if needing to normalize for sequencing depth and gene length.

DESeq2-normalized counts: Median of ratios method

Since tools for differential expression analysis are comparing the counts of the same gene between sample groups, gene length does not need to be accounted for by the tool. However, sequencing depth and RNA composition do need to be taken into account.

To normalize for sequencing depth and RNA composition, DESeq2 uses the median of ratios method. On the user-end there is only one step, but on the back-end there are multiple steps involved, as described below.

Note

The steps below describe in detail some of the steps performed by DESeq2 when you run a single function to get DE genes. Basically, for a typical RNA-seq analysis, you would not run these steps individually.

Step 1: creates a pseudo-reference sample (row-wise geometric mean)

For each gene, a pseudo-reference sample is created that is equal to the geometric mean across all samples.

gene	sampleA	sampleB	pseudo-reference sample
EF2A	1489	906	sqrt(1489 * 906) = 1161.5
ABCD1	22	13	sqrt(22 * 13) = 17.7
…	…	…	…

Step 2: calculates ratio of each sample to the reference

For every gene in every sample, the ratios (sample/ref) are calculated (as shown below). Since the majority of genes are not differentially expressed, the majority of genes in each sample should have similar ratios within the sample.

gene	sampleA	sampleB	pseudo-reference sample	ratio of sampleA/ref	ratio of sampleB/ref
EF2A	1489	906	1161.5	1489/1161.5 = 1.28	906/1161.5 = 0.78
ABCD1	22	13	16.9	22/16.9 = 1.30	13/16.9 = 0.77
MEFV	793	410	570.2	793/570.2 = 1.39	410/570.2 = 0.72
BAG1	76	42	56.5	76/56.5 = 1.35	42/56.5 = 0.74
MOV10	521	1196	883.7	521/883.7 = 0.590	1196/883.7 = 1.35
…	…	…	…

Step 3: calculate the normalization factor for each sample (size factor)

The median value (column-wise for the above table) of all ratios for a given sample is taken as the normalization factor (size factor) for that sample, as calculated below. Notice that the differentially expressed genes should not affect the median value:

normalization_factor_sampleA <- median(c(1.28, 1.3, 1.39, 1.35, 0.59))

normalization_factor_sampleB <- median(c(0.78, 0.77, 0.72, 0.74, 1.35))

The figure below illustrates the median value for the distribution of all gene ratios for a single sample (y-axis is the frequency).

The median of ratios method makes the assumption that not ALL genes are differentially expressed; therefore, the normalization factors should account for sequencing depth and RNA composition of the sample (large outlier genes will not affect the median ratio values). This method is robust to imbalance in up-/down-regulation and large numbers of differentially expressed genes.

Note

Usually these size factors are around 1. If you see large variations between samples, it is important to take note as it might indicate the presence of extreme outliers.

Step 4: calculate the normalized count values using the normalization factor

This is performed by dividing each raw count value in a given sample by that sample’s normalization factor, generating normalized count values. This is performed for all count values (every gene in every sample). For example, if the median ratio for sampleA was 1.3 and the median ratio for sampleB was 0.77, you could calculate normalized counts as follows:

Raw Counts

gene	sampleA	sampleB
EF2A	1489	906
ABCD1	22	13
…	…	…

Normalized Counts

gene	sampleA	sampleB
EF2A	1489 / 1.3 = 1145.39	906 / 0.77 = 1176.62
ABCD1	22 / 1.3 = 16.92	13 / 0.77 = 16.88
…	…	…

Note

Please note that normalized count values are not whole numbers.

Count normalization of Mov10 dataset using DESeq2

Now that we know the theory of count normalization, we will normalize the counts for the Mov10 dataset using DESeq2. This requires a few steps:

Ensure that the row names of the metadata dataframe are present and are in the same order as the column names of the counts dataframe.
Create a DESeqDataSet object
Generate the normalized counts

1. Match the metadata and counts data

We should always make sure that we have sample names that match between the two files, and that the samples are in the same order. DESeq2 will output an error if this is not the case.

# Check that sample names match in both files
all(colnames(txi$counts) %in% rownames(meta))

[1] TRUE

all(colnames(txi$counts) == rownames(meta))

[1] TRUE

If your data does not match, you could use the match() function to rearrange them.

Exercise

Suppose we have sample names matching in the counts matrix and metadata file, but they are in different order. Write the line(s) of code to create a new matrix with columns re-ordered such that they are identical to the row names of the metadata.

2. Create DESEq2 object

Bioconductor software packages often define and use a custom class within R for storing data (input data, intermediate data and also results). These custom data structures are similar to lists in that they can contain multiple different data types/structures. But unlike lists, they have pre-specified data slots, which hold specific types/classes of data. The data stored in these pre-specified slots can be accessed by using specific package-defined functions.

Let’s start by creating the DESeqDataSet object, and then we can talk a bit more about what is stored inside it. To create the object, we will need the count matrix and the metadata table as input. We will also need to specify a design formula. The design formula specifies the column(s) in the metadata table and how they should be used in the analysis. For our dataset we only have one column we are interested in, which is ~sampletype. This column has three factor levels, which tells DESeq2 that for each gene we want to evaluate gene expression change with respect to these different levels.

Our count matrix input is stored in the txi list object. So we need to specify that using the DESeqDataSetFromTximport() function, which will extract the counts component and round the values to the nearest whole number.

# Create DESeq2Dataset object
dds <- DESeqDataSetFromTximport(txi, colData = meta, design = ~ sampletype)

Note

Since we had created a data variable in the last lesson that contains the counts, we could have also used that as input. However, in that case we would want to use the DESeqDataSetFromMatrix() function.

You can use DESeq-specific functions to access the different slots and retrieve information. For example, suppose we want to retrieve the original count matrix, we would use counts() function (Note: we nest it within the head() function so that we can see just the first few lines of the matrix. You could also use the View() function to view the result in the script editor rather than in the console):

head(counts(dds))

                Irrel_kd_1 Irrel_kd_2 Irrel_kd_3 Mov10_kd_2 Mov10_kd_3
ENSG00000000003       4376       3645       2968       6178       3688
ENSG00000000005         27         29         23         37         13
ENSG00000000419       1478       1288        884       2369       1340
ENSG00000000457        508        405        357        934        571
ENSG00000000460       1394       1164        850       2172       1217
ENSG00000000938          1          1          0          2          0
                Mov10_oe_1 Mov10_oe_2 Mov10_oe_3
ENSG00000000003       3343       3114       2079
ENSG00000000005         25         38         22
ENSG00000000419       1890       1766       1271
ENSG00000000457        646        591        354
ENSG00000000460       1183       1139        673
ENSG00000000938          0          0          0

As we go through the workflow, we will use relevant functions to check what information is stored inside our object.

3. Generate the Mov10 normalized counts

The next step is to normalize the count data in order to make fair gene comparisons between samples.

To perform the median of ratios method of normalization, DESeq2 has a single estimateSizeFactors() function that will generate size factors. We will demonstrate this function in the example below, but in a typical RNA-seq analysis, this step is automatically performed by the DESeq() function, which we will discuss later.

# Estimate size factors
dds <- estimateSizeFactors(dds)

By assigning the results back to the dds object, we are filling in the slots of the DESeqDataSet object with the appropriate information. We can take a look at the normalization factors of each sample using:

# Look at size factors
sizeFactors(dds)

Irrel_kd_1 Irrel_kd_2 Irrel_kd_3 Mov10_kd_2 Mov10_kd_3 Mov10_oe_1 Mov10_oe_2 
 1.1150371  0.9606366  0.7493552  1.5634128  0.9359082  1.2257749  1.1406863 
Mov10_oe_3 
 0.6541689

Now, to retrieve the normalized counts matrix from dds, we use the counts() function and add the argument normalized=TRUE.

# Get normalized counts matrix
normalized_counts <- counts(dds, normalized=TRUE)

We can save this normalized data matrix to file for later use:

# Save normalized counts matrix
write.table(normalized_counts, file="../data/normalized_counts.txt", sep="\t", quote=FALSE, col.names=NA)

Note

DESeq2 doesn’t actually use normalized counts; rather it uses the raw counts and models the normalization inside the Generalized Linear Model (GLM). These normalized counts will be useful for downstream visualization of results, but cannot be used as input to DESeq2 or any other tools that perform differential expression analysis that use the negative binomial model.

Next Lesson

--- title: "Count normalization with DESeq2" author: "Meeta Mistry, Radhika Khetani, Mary Piper, Jihe Liu" date: "May 20, 2020" --- Approximate time: 60 minutes ```{r data_setup} #| echo: false # load libraries needed to render this lesson library(ggplot2) library(DESeq2) # load objects needed to render this lesson txi <- readRDS("../data/intermediate_txi.RDS") meta <- readRDS("../data/metadata.RDS") ``` ## Learning Objectives * List different "uninteresting factors" considered during normalization * Describe some common normalization methods and recommendations for its use * Create a `DESeqDataSet` object and discuss the structure of the object * Demonstrate how to normalize counts using DESeq2 ## Normalization The first step in the DE analysis workflow is count normalization, which is necessary to make accurate comparisons of gene expression between samples. <img src="../img/de_workflow_salmon_normalization.png" width="400"> The counts of mapped reads for each gene is the result of RNA expression ("interesting" part) in addition to many other factors ("uninteresting" part). Normalization is the process of adjusting raw count values to account for the "uninteresting" factors. In this way the expression levels are more comparable between and/or within samples. The main "uninteresting" factors often considered during normalization are: - **Sequencing depth:** Accounting for sequencing depth is necessary for comparison of gene expression between samples. In the example below, each gene appears to have twice the expression in *Sample A* relative to *Sample B*. However, this is the consequence of *Sample A* having doubled sequencing depth. <img src="../img/normalization_methods_depth.png" width="400"> ::: callout-note In the figure above, each pink and green rectangle represents a read aligned to a gene. Reads connected by dashed lines connect a read spanning an intron. ::: - **Gene length:** Accounting for gene length is necessary for comparing expression between different genes within the same sample. In the example below, *Gene X* and *Gene Y* have similar levels of expression, but the number of reads mapped to *Gene X* would be many more than the number mapped to *Gene Y* because *Gene X* is longer. <img src="../img/normalization_methods_length.png" width="200"> - **RNA composition:** A few highly differentially expressed genes between samples, differences in the number of genes expressed between samples, or presence of contamination can skew some types of normalization methods. Accounting for RNA composition is recommended for accurate comparison of expression between samples, and is particularly important when performing differential expression analyses [[1](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-10-r106)]. In the example below, imagine the sequencing depths are similar between Sample A and Sample B, and every gene except for gene DE presents similar expression level between samples. The counts in Sample B would be greatly skewed by the DE gene, which takes up most of the counts. Other genes for Sample B would therefore appear to be less expressed than those same genes in Sample A. <img src="../img/normalization_methods_composition.png" width="400"> _**While normalization is essential for differential expression analyses, it is also necessary for exploratory data analysis, visualization of data, and whenever you are exploring or comparing counts between or within samples.**_ ### Common normalization methods Several common normalization methods have been developed to account for these differences: | Normalization method | Description | Accounted factors | Recommendations for use | | :----: | :----: | :----: | :----: | | **CPM** (counts per million) | counts scaled by total number of reads | sequencing depth | gene count comparisons between replicates of the same sample group; **NOT for within sample comparisons or DE analysis** | | **TPM** (transcripts per kilobase million) | counts per length of transcript (kb) per million reads mapped | sequencing depth and gene length | gene count comparisons within a sample or between samples of the same sample group; **NOT for DE analysis** | | **RPKM/FPKM** (reads/fragments per kilobase of exon per million reads/fragments mapped) | similar to TPM | sequencing depth and gene length | gene count comparisons between genes within a sample; **NOT for between sample comparisons or DE analysis** | | DESeq2's **median of ratios** [[1](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-10-r106)] | counts divided by sample-specific size factors determined by median ratio of gene counts relative to geometric mean per gene | sequencing depth and RNA composition | gene count comparisons between samples and for **DE analysis**; **NOT for within sample comparisons** | | EdgeR's **trimmed mean of M values (TMM)** [[2](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2010-11-3-r25)] | uses a weighted trimmed mean of the log expression ratios between samples | sequencing depth and RNA composition | gene count comparisons between samples and for **DE analysis**; **NOT for within sample comparisons** | ### RPKM/FPKM: not recommended for between sample comparisons While TPM and RPKM/FPKM normalization methods both account for sequencing depth and gene length, RPKM/FPKM are not recommended. **The reason is that the normalized count values output by the RPKM/FPKM method are not comparable between samples.** Using RPKM/FPKM normalization, the total number of RPKM/FPKM normalized counts for each sample will be different. Therefore, you cannot compare the normalized counts for each gene equally between samples. **RPKM-normalized counts table** | gene | sampleA | sampleB | | :-----: |:-----:|:-----:| | XCR1 | 5.5 | 5.5 | | WASHC1 | 73.4 | 21.8 | | ... | ... | ... | |Total RPKM-normalized counts | 1,000,000 | 1,500,000 | For example, in the table above, sampleA has a greater proportion of counts associated with XCR1 (5.5/1,000,000) than sampleB (5.5/1,500,000), even though the RPKM count values are the same. Therefore, we cannot directly compare the counts for XCR1 (or any other gene) between sampleA and sampleB, because the total number of normalized counts are different between samples. ::: callout-note [This video by StatQuest](http://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/) shows in more detail why TPM should be used in place of RPKM/FPKM if needing to normalize for sequencing depth and gene length. ::: ### DESeq2-normalized counts: Median of ratios method Since tools for differential expression analysis are comparing the counts of the same gene between sample groups, gene length does not need to be accounted for by the tool. However, **sequencing depth** and **RNA composition** do need to be taken into account. To normalize for sequencing depth and RNA composition, DESeq2 uses the median of ratios method. On the user-end there is only one step, but on the back-end there are multiple steps involved, as described below. ::: callout-note The steps below describe in detail some of the steps performed by DESeq2 when you run a single function to get DE genes. Basically, for a typical RNA-seq analysis, **you would not run these steps individually**. ::: **Step 1: creates a pseudo-reference sample (row-wise geometric mean)** For each gene, a pseudo-reference sample is created that is equal to the geometric mean across all samples. | gene | sampleA | sampleB | pseudo-reference sample | | :-----: |:-----:|:-----:|:-----:| | EF2A | 1489 | 906 | sqrt(1489 * 906) = **1161.5** | | ABCD1 | 22 | 13 | sqrt(22 * 13) = **17.7** | | ... | ... | ... | ... | **Step 2: calculates ratio of each sample to the reference** For every gene in every sample, the ratios (sample/ref) are calculated (as shown below). Since the majority of genes are not differentially expressed, the majority of genes in each sample should have similar ratios within the sample. | gene | sampleA | sampleB | pseudo-reference sample | ratio of sampleA/ref | ratio of sampleB/ref | | :-----: |:-----:|:-----:|:-----:| :-----: | :-----: | | EF2A | 1489 | 906 | 1161.5 | 1489/1161.5 = **1.28** | 906/1161.5 = **0.78** | | ABCD1 | 22 | 13 | 16.9 | 22/16.9 = **1.30** | 13/16.9 = **0.77** | | MEFV | 793 | 410 | 570.2 | 793/570.2 = **1.39** | 410/570.2 = **0.72** | BAG1 | 76 | 42 | 56.5 | 76/56.5 = **1.35** | 42/56.5 = **0.74** | MOV10 | 521 | 1196 | 883.7 | 521/883.7 = **0.590** | 1196/883.7 = **1.35** | | ... | ... | ... | ... | **Step 3: calculate the normalization factor for each sample (size factor)** The median value (column-wise for the above table) of all ratios for a given sample is taken as the normalization factor (size factor) for that sample, as calculated below. Notice that the differentially expressed genes should not affect the median value: `normalization_factor_sampleA <- median(c(1.28, 1.3, 1.39, 1.35, 0.59))` `normalization_factor_sampleB <- median(c(0.78, 0.77, 0.72, 0.74, 1.35))` The figure below illustrates the median value for the distribution of all gene ratios for a single sample (y-axis is the frequency). <img src="../img/deseq_median_of_ratios.png" width="400"> The median of ratios method makes the assumption that not ALL genes are differentially expressed; therefore, the normalization factors should account for sequencing depth and RNA composition of the sample (large outlier genes will not affect the median ratio values). **This method is robust to imbalance in up-/down-regulation and large numbers of differentially expressed genes.** ::: callout-note Usually these size factors are around 1. If you see large variations between samples, it is important to take note as it might indicate the presence of extreme outliers. ::: **Step 4: calculate the normalized count values using the normalization factor** This is performed by dividing each raw count value in a given sample by that sample's normalization factor, generating normalized count values. This is performed for all count values (every gene in every sample). For example, if the median ratio for sampleA was 1.3 and the median ratio for sampleB was 0.77, you could calculate normalized counts as follows: **Raw Counts** | gene | sampleA | sampleB | | :-----: |:-----:|:-----:| | EF2A | 1489 | 906 | | ABCD1 | 22 | 13 | | ... | ... | ... | **Normalized Counts** | gene | sampleA | sampleB | | :-----: |:-----:|:-----:| | EF2A | 1489 / 1.3 = **1145.39** | 906 / 0.77 = **1176.62** | | ABCD1 | 22 / 1.3 = **16.92** | 13 / 0.77 = **16.88** | | ... | ... | ... | ::: callout-note Please note that normalized count values are not whole numbers. ::: *** ## Count normalization of Mov10 dataset using DESeq2 Now that we know the theory of count normalization, we will normalize the counts for the Mov10 dataset using DESeq2. This requires a few steps: 1. Ensure that the row names of the metadata dataframe are present and are in the same order as the column names of the counts dataframe. 2. Create a `DESeqDataSet` object 3. Generate the normalized counts ### 1. Match the metadata and counts data We should always make sure that we have sample names that match between the two files, and that the samples are in the same order. DESeq2 will output an error if this is not the case. ```{r check_sample_names} # Check that sample names match in both files all(colnames(txi$counts) %in% rownames(meta)) all(colnames(txi$counts) == rownames(meta)) ``` If your data does not match, you could use the `match()` function to rearrange them. ::: callout-tip # Exercise Suppose we have sample names matching in the counts matrix and metadata file, but they are in different order. Write the line(s) of code to create a new matrix with columns re-ordered such that they are identical to the row names of the metadata. ::: ### 2. Create DESEq2 object Bioconductor software packages often define and use a custom class within R for storing data (input data, intermediate data and also results). These custom data structures are similar to `lists` in that they can contain multiple different data types/structures. But unlike lists, they have pre-specified **data slots**, which hold specific types/classes of data. The data stored in these pre-specified slots can be accessed by using specific package-defined functions. Let's start by creating the `DESeqDataSet` object, and then we can talk a bit more about what is stored inside it. To create the object, we will need the **count matrix** and the **metadata** table as input. We will also need to specify a **design formula**. The design formula specifies the column(s) in the metadata table and how they should be used in the analysis. For our dataset we only have one column we are interested in, which is `~sampletype`. This column has three factor levels, which tells DESeq2 that for each gene we want to evaluate gene expression change with respect to these different levels. **Our count matrix input is stored in the `txi` list object**. So we need to specify that using the `DESeqDataSetFromTximport()` function, which will extract the counts component and round the values to the nearest whole number. ```{r create_dds} # Create DESeq2Dataset object dds <- DESeqDataSetFromTximport(txi, colData = meta, design = ~ sampletype) ``` ::: callout-note Since we had created a `data` variable in the last lesson that contains the counts, we could have also used that as input. However, in that case we would want to use the `DESeqDataSetFromMatrix()` function. ::: ![DESeq2 object structure](../img/deseq_obj1.png) You can use DESeq-specific functions to access the different slots and retrieve information. For example, suppose we want to retrieve the original count matrix, we would use `counts()` function (*Note: we nest it within the `head()` function so that we can see just the first few lines of the matrix. You could also use the `View()` function to view the result in the script editor rather than in the console*): ```{r view_dds_counts} head(counts(dds)) ``` As we go through the workflow, we will use relevant functions to check what information is stored inside our object. ### 3. Generate the Mov10 normalized counts The next step is to normalize the count data in order to make fair gene comparisons between samples. <img src="../img/de_workflow_salmon_normalization.png" width="400"> To perform the **median of ratios method** of normalization, DESeq2 has a single `estimateSizeFactors()` function that will generate size factors. We will demonstrate this function in the example below, but **in a typical RNA-seq analysis, this step is automatically performed by the `DESeq()` function**, which we will discuss later. ```{r size_factors_run} # Estimate size factors dds <- estimateSizeFactors(dds) ``` By assigning the results back to the `dds` object, we are filling in the slots of the `DESeqDataSet` object with the appropriate information. We can take a look at the normalization factors of each sample using: ```{r size_factors_viz} # Look at size factors sizeFactors(dds) ``` Now, to retrieve the normalized counts matrix from `dds`, we use the `counts()` function and add the argument `normalized=TRUE`. ```{r normalize} # Get normalized counts matrix normalized_counts <- counts(dds, normalized=TRUE) ``` We can save this normalized data matrix to file for later use: ```{r save_normalized} # Save normalized counts matrix write.table(normalized_counts, file="../data/normalized_counts.txt", sep="\t", quote=FALSE, col.names=NA) ``` ::: callout-note DESeq2 doesn't actually use normalized counts; rather it uses the raw counts and models the normalization inside the Generalized Linear Model (GLM). These normalized counts will be useful for downstream visualization of results, but cannot be used as input to DESeq2 or any other tools that perform differential expression analysis that use the negative binomial model. ::: ```{r data_save} #| echo: false # save objects needed to render future lessons saveRDS(dds, "../data/intermediate_dds.RDS") ``` *** [Next Lesson](03_DGE_QC_analysis.html) ***