Differential Expression Talk - Revision history

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Rf: Created page with "07 Differential expression analysis = Goals = Three overall: Primarily, it's about: * Identify differentially expressed genes in two or more conditions (e.g. normal v cance..."

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Created page with "07 Differential expression analysis = Goals = Three overall: Primarily, it's about: * Identify differentially expressed genes in two or more conditions (e.g. normal v cance..."

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07 Differential expression analysis

= Goals =

Three overall:

Primarily, it's about:
* Identify differentially expressed genes in two or more conditions (e.g. normal v cancer)

Generally, it's about:
* Gain biological insight into which genes cause / respond to a condition

And with eye towards future project: looking for more promising places to look:
* Identify biomarkers for a condition

= Three principal themes =
# Data normalisation
# Data quality control
# Differential expression analysis

= Data filtering =
* Due to random noise / sampling errors, genes with low read counts across all samples cannot be found to be differentially expressed
* Removing these:
:- reduces amount of data
:- improves speed of analysis
:- reduces number of genes to be counted in multiple test correction

= Data normalisation =

What affects read count? Read count not only affected by:
* level of transcription
but also by:
* Between genes
:- length of gene
:- GC content
* Between libraries
:- sequencing depth (library size)
:- RNA composition

= RNA composition =
* A few extremely highly expressed genes may contribute a very large part of the sequenced reads
* Changes in the expression of these change the relative abundance of all other genes

= Normalisation methods =
Dillies et al. "A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis”
Brief Bioinform. 2013 Nov;14(6):671-83.
* Total Count (TC)
:- TC = reads mapping to gene / total reads in library

* Also:
:- Reads per Kilobase per Million mapped reads (RPKM)
:- Upper Quartile (UQ)
:- Median (Med)
:- DESeq
:- Trimmed Mean of M-values (TMM) (used by edgeR)
:- Quantile (Q)

= Normalisation Example =

* Consider two samples
* Almost all genes have identical read counts in library 1 and library 2
* A few genes are highly expressed in library 2
* How should library 2 be normalised to make it comparable to library 1?
* Correct normalisation factor would be 1 (no change)

= Normalisation Example =
Total Count (TC):
TC =

reads mapping to gene
total reads in library

Normalisation factor =0.96

correct normalisation
(normalisation factor = 1)

Because of the three genes that are much more highly expressed in library 2 than in library 1, it looks as if the expression of all other genes has gone down in sample 2.

= Trimmed Mean of M-values (TMM) =
* One library is considered reference library, other(s) test library(s)
* Calculate M-value for each gene (log ratio of counts between test and reference)
* Exclude most expressed genes and genes with largest log ratio
* Calculate weighted mean of M-values
* Apply this normalisation factor (which is 1 in this example) to all read counts

= Normalisation methods =
* Conclusion from Dillies et al.: only TMM and DESeq can cope with large changes in highly expressed genes
* These normalisation methods assume that:
:– most genes are not differentially expressed
:– for those differentially expressed there is an approximately balanced proportion of over- and under-expression

= Data quality control =
* Do (technical and biological) replicates cluster together?
* we can see on an MDS plot:
:- Shows the level of similarity of individual cases of a dataset
:- Distances represent fold-changes
* Dataset: 10 patients
:- Cancerous samples
:- Non-cancerous samples

= Example =

[Image] MDS plot:
Multiple samples from the same
patient cluster together

= Example =
Image] MDS plot:
Cancerous samples cluster together
Non-cancerous samples cluster together
No very tight separation between the two

= Example =

[Image] MDS plot:
* Removing two patients improves the separation
* Two out of ten patients: maybe not justified.

= Differential expression methods =
* For each gene, two measures of expression level will show up:
:- between the two groups of samples
:- within groups of samples
* Might the difference within groups of samples big enough to explain the difference between groups of samples?

= Differential expression methods =

[Image]
Cancer samples:
Mean = 116

Non cancer
samples:
Mean = 132

= Differential expression methods =
* Count based:
:– most tools
* Coverage based:
:– Cuffdiff

* Methods may be parametric or non-parametric
:- non-parametric build up their own parameters from the data, often render too many.
* Some tools allow a variety of experimental designs

= Differential expression methods =
* Parametric methods
:– e.g. edgeR, DESeq
:– assume a negative binomial distribution to account for biological variation
:– have problems when the data don’t fit this distribution

* Non-parametric methods
:– e.g. SAMseq and NOISeq
:– need to learn the distribution from the data
:– may require more replicates

= edgeR =
* assumes that normalised counts for each gene across biological replicates follows a negative binomial distribution with the dispersion representing the biological variation
* calculates a dispersion factor for each gene
* calculates a dispersion factor that fits the data as a whole

= Genes with fewer counts can =
[Image] MA Plot
appear to be highly variable due to sampling errors

= Two types of comparsions =

[Image: comparisonincircles]
* Group comparison

* Matched-pair comparison
:- Reduces variability by eliminating the between-unit (here between-patient) variability

= Grouped comparisons =
[Image: singlegenelogcountsallsamples]
* For a single gene
* Too much overall variability.
* Data don’t provide much evidence for a real difference in expression of this gene between cancerous and non-cancerous samples.

* 20 samples
:- Cancerous samples in red
:- Non-cancerous samples in black

= Matched-pair comparison =
* For a single gene
[Image: singlegenelogcountsallmatchedpairs]
Gene is clearly higher expressed
in cancerous samples.

* 20 samples
:- Cancerous samples in red
:- Non-cancerous samples in black

= edgeR output =
[Image: bluegenetable]

= P-values =

Test 100 genes for DE
[Image 100bluesquares]

= Add FDR to P-valuesR =
[Image 1red100bluesquares]
Test 100 genes for DE P-value:
* uncorrected p-value = 0.01
:- 1 false positive for every 100 genes tested

= P-value and FDR =
[Image 1red20greensquares]
Test 100 genes for DE P-value:
* uncorrected p-value = 0.01
:- 1 false positive for every 100 genes tested

* False Discovery Rate:
:- Of 100 genes tested 20 have a p-value < 0.01
:- 1 of these 20 is likely to be a false positive
* FDR = 1/20 = 0.05

= MA Plot comparison =
[Image twomaplots]
* Two group comparison
:- 2,118 genes differentially expressed (FDR < 0.05)

Matched pair comparison
:- 2,957 genes differentially expressed (FDR < 0.05)

* differentially expressed in red
* non-differentially expressed in black
* blue lines mark 2-fold change

= Summary =
* Before differential expression analysis is done there are multiple initial steps
* Data must be filtered, normalised and outliers removed
* A variety of techniques to both normalise data and call differentially expressed genes are used
* Understanding of the experimental design is important
* Different techniques can give different results, especially for low numbers of replicates, noisy data and lowly expressed genes
* No standard way of doing any of this, best practices are still evolving.

= Further reading =

* Dillies et al "A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis” Brief Bioinform. 2013 Nov;14(6):671-83.
* Soneson and Delorenzi "A comparison of methods for differential expression analysis of RNA-seq data.” BMC Bioinformatics. 2013 Mar 9;14:91.
* Rapaport et al "Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.” Genome Biol. 2013;14(9):R95.
* Huang et al "RNA-Seq analyses generate comprehensive transcriptomic landscape and reveal complex transcript paLerns in hepatocellular carcinoma.” PLoS One 2011 17;6(10):e26168.

@@ Line 160: / Line 160: @@
 * calculates a dispersion factor that fits the data as a whole
-= Genes with fewer counts can =
+= Diversity of low count =
 [[File:bcv1.png]]
-* appear to be highly variable due to sampling errors
+* Genes with fewer counts can appear to be highly variable due to sampling errors
 = Two types of comparsions =

@@ Line 128: / Line 128: @@
 = Differential expression methods =
-[[File:sing1.png]]
+[[File:singg1.png]]
 * Cancer samples in red
 :- Mean logcount is 116
@@ Line 183: / Line 183: @@
 Testing 100 genes for DE ...
-[Image 100bluesquares]
 [[File:pval.png]]
@@ Line 189: / Line 189: @@
 Testing 100 genes for DE ...
 [[File:pvalfdr.png]]
 = MA Plot comparison =
-* differentially expressed in red
+[[File:twoma.png]]
 = Summary =

@@ Line 89: / Line 89: @@
 = Plotting the samples 1 =
-[[File:mds1.png]]
+[[File:mda1.png]]
 * A Multidimensional scaling plot is in fact a PCA Principle Component plot with the first two dimension.
@@ Line 100: / Line 100: @@
 = Plotting the samples 2 =
-[[File:mds2.png]]
+[[File:mda2.png]]
 * Multiple samples from the same patient cluster together
@@ Line 106: / Line 106: @@
 = Plotting the samples 3=
-[[File:mds3.png]]
+[[File:mda3.png]]
 * Cancerous samples cluster together
@@ Line 114: / Line 114: @@
 = Plotting the samples 4=
-[[File:mds3.png]]
+[[File:mda4.png]]
 * Removing two patients improves the separation

← Older revision		Revision as of 15:09, 9 May 2017
Line 70:		Line 70:

	[[File:normcount2.png]]		[[File:normcount2.png]]
		+
		+	= Normalisation conclusion =
		+
		+	* Dillies et al. conclude that only TMM and DESeq can cope with large changes in highly expressed genes.
		+	* These lean on the assumption that:
		+	:- the majority of genes are not differentially expressed
		+	:- for those differentially expressed, there is an approxmiately balanced proportion of over- and under-expression.

	= Data quality control =		= Data quality control =

@@ Line 163: / Line 163: @@
 [[File:bcv1.png]]
-appear to be highly variable due to sampling errors
+* appear to be highly variable due to sampling errors
 = Two types of comparsions =
@@ Line 175: / Line 175: @@
 = Matched-pair comparison =
-[[File:mathccomp.png]]
+[[File:matchcomp.png]]
 = edgeR output =
-[Image: bluegenetable]
+[[File:outp.png]]
 = P-values =
-Test 100 genes for DE
+Testing 100 genes for DE ...
 [Image 100bluesquares]
-= Add FDR to P-valuesR =
+= Add FDR to P-values =
-= P-value and FDR =
+Testing 100 genes for DE ...
-[Image 1red20greensquares]
+[[File:pvalfdr.png]]
 = MA Plot comparison =

@@ Line 124: / Line 124: @@
 :- between the two groups of samples
 :- within groups of samples
-* Might the difference within groups of samples big enough to explain the difference between groups of samples?
+* Might the difference within groups of samples be big enough to explain the difference between groups of samples?
 = Differential expression methods =
-[Image]
+[[File:sing1.png]]
-Cancer samples:
+* Cancer samples in red
-Mean = 116
+:- Mean logcount is 116
+* Non cancer samples in black
-Non cancer
+:- Mean logcount is 132
 = Differential expression methods =
@@ Line 143: / Line 141: @@
 * Methods may be parametric or non-parametric
-:- non-parametric build up their own parameters from the data, often render too many.
+:- non-parametric build up their own parameters from the data.
 * Some tools allow a variety of experimental designs
@@ Line 163: / Line 161: @@
 = Genes with fewer counts can =
-[Image] MA Plot
 appear to be highly variable due to sampling errors
 = Two types of comparsions =
-[Image: comparisonincircles]
+[[File:expdes.png]]
 = Grouped comparisons =
-* 20 samples
+[[File:groupcomp.png]]
 = Matched-pair comparison =
-* 20 samples
+[[File:mathccomp.png]]
 = edgeR output =

@@ Line 87: / Line 87: @@
 :- Non-cancerous samples
-= Example =
+= Plotting the samples 1 =
-[Image] MDS plot:
+[[File:mds1.png]]
-= Example =
+* A Multidimensional scaling plot is in fact a PCA Principle Component plot with the first two dimension.
-Image] MDS plot:
+* These are the dimensions internal to the data where most variation in values is seen.
-Cancerous samples cluster together
+* The distances here represent fold-changes.
-Non-cancerous samples cluster together
+* Ten patients
-No very tight separation between the two
+:- Cancerous samples in red
-= Example =
+= Plotting the samples 2 =
 * Removing two patients improves the separation
 * Two out of ten patients: maybe not justified.
 = Differential expression methods =
 * For each gene, two measures of expression level will show up:
 :- between the two groups of samples

@@ Line 1: / Line 1: @@
 = Goals =
@@ Line 11: / Line 9: @@
 * Gain biological insight into which genes cause / respond to a condition
-And with eye towards future project: looking for more promising places to look:
+And with an eye towards future project: looking for more promising places to look:
 * Identify biomarkers for a condition
@@ Line 43: / Line 41: @@
 = Normalisation methods =
-Dillies et al. "A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis”
-Brief Bioinform. 2013 Nov;14(6):671-83.
+[[File:Dillies.png]]
 * Total Count (TC)
 :- TC = reads mapping to gene / total reads in library
-* Also:
+* Other methods of normalising counts:
 :- Reads per Kilobase per Million mapped reads (RPKM)
 :- Upper Quartile (UQ)
@@ Line 65: / Line 64: @@
 = Normalisation Example =
-reads mapping to gene
+[[File:normcount1.png]]
 = Trimmed Mean of M-values (TMM) =
-= Normalisation methods =
+[[File:normcount2.png]]
 = Data quality control =