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Module : DGE with SCDE

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Jaze8 edited this page Jun 7, 2018 · 4 revisions

Module : DGE with SCDE

This module calculates for each gene the probability of differential expression.

  • Internal name : scde

  • Avalaible : local mode

  • Input Ports :

    • matrix : filtered count matrix (tsv)
    • cells : filtered cells metadata (tsv)
    • genes : genes metadata (tsv)

  • Output Ports :

    • dgeoutput : differential expression result (tsv)

  • Optional parameters :

    • Main Parameters
Parameter Type Description Default Value
n.cores int Number of cores to use 1
model.group.col string Name of the column from the design file if cells must be grouped for model fitting (NULL if no grouping is necessary) NULL
prior.length int Number of points for prior calculation 400
batch.col string Name of the column indicating batches, if batch correction is required (else NULL) NULL
n.randomizations int Number of randomization for testing 150
  • Parameters for model fitting
Parameter Type Description Default Value
min.observation int Minimal number of observations for a gene to be used for model fitting 3
min.genes int Minimum number of genes for model fitting 2000
threshold.segmentation boolean Use or not threshold segmentation to accelerate failure estimation TRUE
failure.threshold int Number of reads indicating a gene failed amplification 4
max.pairs int Maximum number of comparisons that should be performed per group for estimation of dropout rate 5000
min.pairs int Minimum number of comparisons that should be performed per group for estimation of dropout rate 10
poisson.param float Parameter of the Poisson distribution used to model failures 0.1
linear.fit boolean Whether to use linear fit for model fitting (highly recommended) TRUE
min.theta float Minimum for the dispersion parameter of the negative binomial 0.01
max.theta float Maximum for the dispersion parameter of the negative binomial 100
  • Parameters for prior calculation
Parameter Type Description Default Value
save.prior.plot boolean Whether to save or not prior plot TRUE
pseudocount int Pseudocount to add to observation before log transforming them 1
quantile float Quantile used to set maximum expression value 0,999
max.value float Alternative to quantile, maximum expression value NULL
  • Parameters for test
Parameter Type Description Default Value
return.posteriors boolean Whether to return or not the posteriors TRUE
  • Configuration example
<step id="DGE" skip="false">
	<module>scde</module>
	<parameters>
		<parameter>
			<name>prior.length</name>
			<value>400</value>	
		</parameter>
		<parameter>
			<name>n.randomizations</name>
			<value>200</value>	
		</parameter>
		<parameter>
			<name>n.cores</name>
			<value>12</value>	
		</parameter>
	  </parameters>
</step>

Interpreting output files

Introduction to scde

SCDE uses a mixture model : one distribution is for amplification failures (ie, dropouts), the other is for successful amplification (ie, "real" gene expression).

The first step of the algorithm is to fit the model. Briefly, this steps estimates failure by comparing gene expression between cells, and then uses success to fit a negative binomial (thus setting mean and dispersion parameters).

Secondly a prior distribution on mean expression is calculated, using all genes and all cells.

The last step consists in a Bayesian test on the log-fold change in expression values. Its results are gathered in the output file containing :

  • lb: lower bound of the 95% HDI or highest density interval (analogous to 95% CI or confidence interval)
  • mle: maximum likelihood estimate of the fold change
  • ub: upper bound of the 95% HDI (analogous to 95% CI)
  • ce: the bound closer to 0
  • Z: quantile in a norm Gaussian distribution associated with the probability of the ratio being 0
  • cZ: same as Z after correction by Benjamini-Hochberg procedure
  • cProbability: probability associated to cZ

Evaluation of model fitting and misfit removal

In order to evaluate the goodness of fit of the model, the module produces several plots for each cell :

FittingPlot

Plots from left to right :

  • scatter plot of observed count as a function of expected count
  • same plot with correlation and failures according to the model and 95% CI as a dashed line
  • probability of failure as a function of expected count
  • fit of the dispersion as a function of count

The easiest way to find a badly fitted model is without a doubt to use the probability of failure plot :

fail

One of the model's hypothesis is that failure is more likely to occur with few transcripts. So we expect to see high probabilities on the left of the plot, and low probabilities on the right. The left plot shows this kind of distribution, but the right one is nearly uniform indicating a problem with model fitting.

Design file format

This module requires a v2 design file. The design file must contain experiments with the columns attribute, indicating required columns for grouping the cells, and a comparisons attribute, listing and naming the required comparisons.

The module can handle model attributes as for DESeq2, however it will not build a GLM, therefore it will uniquely test factors separately or an interaction of factors.

[Header]
DesignFormatVersion=2
Project=designExample
ProjectDescription=Example of design file for single cell DGE analysis
Owner=Geoffray Brelurut

GenomeFile=genome://mm10
GffFile=gff://mm10


[Experiments]
Exp.1.name=exp1
Exp.1.skip=False
Exp.1.columns=type+day
Exp.1.comparisons=WT1_vs_KO1:typeWT%dayday1_vs_typeKO%dayday1;\
WT2_vs_KO2:typeWT%dayday2_vs_typeKO%dayday2

Exp.2.name=exp2
Exp.2.skip=false
Exp.2.model=~type+day+type:day
Exp.2.comparisons=WT1_vs_KO1:typeWT%dayday1_vs_typeKO%dayday1;\
WT2_vs_KO2:typeWT%dayday2_vs_typeKO%dayday2

[Columns]
SampleId	SampleName	Reads		Condition	Reference	Exp.1.RepTechGroup Exp.2.type		Exp.2.day	Exp.2.RepTechGroup	Exp.3.Condition	Exp.3.RepTechGroup
s1a		Sample1a	sample1a.fastq	WT-day1		1		WT-day1			WT			day1		WT-day1			WT		WT-day1
s1b		Sample1b	sample1a.fastq	WT-day1		0		WT-day1			WT			day1		WT-day1			WT		WT-day1
s2a		Sample2a	sample2a.fastq	WT-day2		2		WT-day2			WT			day2		WT-day2			WT		WT-day2
s2b		Sample2b	sample2b.fastq	WT-day2		0		WT-day2			WT			day2		WT-day2			WT		WT-day2
s3a		Sample3a	sample3a.fastq	KO-day1		3		KO-day1			KO			day1		KO-day1			KO		KO-day1
s3b		Sample3b	sample3b.fastq	KO-day1		0		KO-day1			KO			day1		KO-day1			KO		KO-day1
s4a		Sample4a	sample4a.fastq	KO-day2		0		KO-day2			KO			day2		KO-day2			KO		KO-day2
s4b		Sample4b	sample4b.fastq	KO-day2		0		KO-day2			KO			day2		KO-day2			KO		KO-day2
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