Analyses biostatistiques de données RNA-seq Nathalie Vialaneix (MIAT, INRA) en collaboration avec Ignacio Gonzàles et Annick Moisan
[email protected] http://www.nathalievialaneix.eu
Toulouse, 11/12 février 2019
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Outline 1
Exploratory analysis Introduction Experimental design Data exploration and quality assessment
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Normalization Raw data filtering Interpreting read counts
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Differential Expression analysis Hypothesis testing and correction for multiple tests Differential expression analysis for RNAseq data Interpreting and improving the analysis
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A typical transcriptomic experiment
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A typical transcriptomic experiment
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Outline 1
Exploratory analysis Introduction Experimental design Data exploration and quality assessment
2
Normalization Raw data filtering Interpreting read counts
3
Differential Expression analysis Hypothesis testing and correction for multiple tests Differential expression analysis for RNAseq data Interpreting and improving the analysis
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A typical RNA-seq experiment
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collect samples
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generate libraries of cDNA fragments
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affect material to one lane in one flow cell
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Steps in RNAseq data analysis
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Part I: Experimental design
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Confounded effects: a simple example Basic experiment: find differences between control/treated plants
control group plant
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treated group plant
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Confounded effects: a simple example Basic experiment: find differences between control/treated plants
control group plant
treated group plant
A bad experimental design: grow all control group plants in one field and grow all treated group plants in another field Field 1
Field 2
because you can not differentiate between differences due to the field and differences due to the treatment ⇒ confounded effects NV2 (INRA)
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Confounded effects: a simple example Basic experiment: find differences between control/treated plants
control group plant
treated group plant
A good experimental design: grow half control group plants (chosen at random) and half treated group plants in one field (and the rest in the other field) Field 1
Field 2
differences due to the field and differences due to the treatment can be estimated separately NV2 (INRA)
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Confounded effects: a simple example Basic experiment: find differences between control/treated plants
control group plant
treated group plant
In summary, what is a good experimental design? Experimental design are usually not as simple as this example: they can include multiple experimental factors (day of experiment, flow cell, . . . ) and multiple covariates (sex, parents, . . . ).
⇒ The experimental design must be carefully thought before starting the experiment and confounded effects must be searched for in a systematic manner.
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Effect & Variation 2 conditions, 2 genes whose expression distribution is:
first gene: different median levels between the two groups but large variance: differences may be non significant second gene: different median levels between the two groups but very small variance: differences may be significant NV2 (INRA)
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Source of variation in RNA-seq experiments
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at the top layer: biological variations (i.e., individual differences due to e.g., environmental or genetic factors)
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at the middle layer: technical variations (library preparation effect)
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at the bottom layer: technical variations (lane and cell flow effects)
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Source of variation in RNA-seq experiments 1
at the top layer: biological variations (i.e., individual differences due to e.g., environmental or genetic factors)
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at the middle layer: technical variations (library preparation effect)
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at the bottom layer: technical variations (lane and cell flow effects)
lane effect < cell flow effect < library preparation effect biological effect NV2 (INRA)
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Advised policy for biological/technical replicates biological replicates: different biological samples, processed separately (advised: ≥ 3 per condition)
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Advised policy for biological/technical replicates biological replicates: different biological samples, processed separately (advised: ≥ 3 per condition)
technical replicates: same biological material but independent replications of the technical steps (from library preparation; not mandatory in most cases)
6 biological replicates are better than 3 biological replicates × 2 technical replicates! See [Liu et al., 2014] for further discussions. NV2 (INRA)
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In summary: a simple two-condition experimental design for RNA-seq Typical RNA-seq design: independent samples are sequenced into different lanes (lane effect cannot be estimated but within sample comparison is preserved) Lane 1 RNA extraction
Library preparation Lane 2
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In summary: a simple two-condition experimental design for RNA-seq Typical RNA-seq design: independent samples are sequenced into different lanes (lane effect cannot be estimated but within sample comparison is preserved) Lane 1 Library preparation
RNA extraction
Lane 2
Example with 2 biological replicates per condition and 4 technical replicates per biological replicate: Flow cell 1
Flow cell 2
Flow cell 3
Flow cell 4
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C11
C21
T11
T21
C12
C22
T12
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C13
C23
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C14
C24
T14
T24
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In summary: a simple two-condition experimental design for RNA-seq Multiplex RNA-seq design: DNA fragments are barcoded so that multiple samples can be included in the same lane Barcode adapters
RNA extraction
Library preparation and barcoding
Lane 1 Pooling Lane 2
Barcoded adapters
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In summary: a simple two-condition experimental design for RNA-seq Multiplex RNA-seq design: DNA fragments are barcoded so that multiple samples can be included in the same lane Barcode adapters
Lane 1
Library preparation and barcoding
RNA extraction
Pooling Lane 2
Barcoded adapters
Example with 2 biological replicates per condition and 4 technical replicates per biological replicate, splitted on 4 flow cells: Flow cell 1
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Flow cell 2
Flow cell 4
Flow cell 3
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C111
C121
C131
C141
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C114
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C144
C211
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C212
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C232
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C213
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C234
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T111
T121
T131
T141
T112
T122
T132
T142
T113
T123
T133
T143
T114
T124
T134
T144
T211
T221
T231
T241
T212
T222
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Part II: Exploratory analysis
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Some features of RNAseq data What must be taken into account? discrete, non-negative data (total number of aligned reads)
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Some features of RNAseq data What must be taken into account? discrete, non-negative data (total number of aligned reads) skewed data
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Some features of RNAseq data What must be taken into account? discrete, non-negative data (total number of aligned reads) skewed data overdispersion (variance mean)
black line is “variance = mean” NV2 (INRA)
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Dataset used in the examples Dataset provided by courtesy of the transcriptomic platform of IPS2 Three files:
D1-counts.txt contains the raw counts of the experiment (13 columns: the first one contains the gene names, the others correspond to 12 different samples; gene names have been shuffled);
D1-genesLength.txt contains information about gene lengths; D1-targets.txt contains information about the sample and the experimental design.
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Dataset used in the examples
These text files are loaded raw_ counts
