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Tutorial
Peer can be either used as standalone tool or using one of it’s interfaces (currently R and Python). Below you can find a brief introduction to either one of these ways to get started with PEER.
As a minimum, PEER requires an expression matrix in csv
(comma-separated) format, specified with the -f option. The matrix is
assumed to have N rows and G columns, where N is the number of
samples, and G is the number of genes. The basic command to apply peer
to such a matrix given in expression.csv, learning K=10 hidden
confounders is
> peer -f expression.csv -n 10
PEER can read comma-separated (.csv) or tab separated (.tab) files, and
assumes single space separation if the extension is not .csv or .tab. If
the expression data file has a header row, you have to give
--has_header switch on the command line.
The output is written to directory peer_out by default, creating csv files for the residuals after accounting for the factors (residuals.csv, NxG matrix), the inferred factors (X.csv, NxK), the weights of each factor for every gene (W.csv, GxK), and the inverse variance of the weights (Alpha.csv, Kx1).
You can change the output directory with the -o option, e.g.
> peer -f expression.csv -n 5 -o peer_k-5
If you are not interested in the posterior estimates of all the variables, you can suppress their output with switches. For example, to only output the residuals, you can use
> peer -f expression.csv --no_a_out --no_w_out --no_x_out
The --no_res_out switch suppresses the output of residuals.
If there are measured experimental variables that may contribute to
variability in the data, they can be included in the inference, and
specified with the -c flag.
> peer -f expression.csv -c covariates.csv
The covariates file should be in csv or tab format, and have N rows and C columns, where N matches the number of samples in the expression file.
By default, PEER includes a covariate for the mean (a vector of ones).
If you do not want this behaviour, you can switch it off with the
--no_mean_covariate flag.
As default, PEER iterates through updates of every variable 100 times. To set it to say, 1000, use
> peer -f expression.csv -i 100
PEER finishes if the increase in lower bound on the model evidence ceases to change, or the variance of the residuals has stabilised. The limiting values (tolerances) can be specified as
`> peer -f expression.csv --bound_tolerance=0.1 --var_tolerance=0.00000001 `
In general you can keep the bound tolerance fairly high, but should keep the variation tolerance quite low compared to the variance of the expression matrix. If unsure, use the default values.
Finally, the prior parameters on the noise and weight precision distributions can also be changed. As these are both gamma distributed, you can specify the a and b parameters of both:
> peer -f expression.csv --e_pa=1.0 --e_pb=0.01 --a_pa=10.0 --a_pb=100
Applying PEER in R amounts to creating the model and setting its parameters, followed by inference. The model object will then hold the posterior distributions of the parameters.
First, load the peer library:
> library(peer)If there are any errors, please see the [installation page](Installation-instructions) or [FAQ](FAQ). You will need data to analyse - we will use the data from the examples directory:
> expr = read.csv('examples/expression.csv', header=FALSE)
> dim(expr)
[1] 200 100The data matrix is assumed to have N rows and G columns, where N is the number of samples, and G is the number of genes.
Now we can create the model object,
> model = VBFA()set the observed data,
> VBFA_setPhenoMean(model,as.matrix(expr))
NULL
> dim(VBFA_getPhenoMean(model))
[1] 200 100(NULL response means no error here), say we want to infer K=10 hidden confounders,
> VBFA_setNk(model,10)
NULL
> VBFA_getNk(model)
[1] 10and perform the inference.
> VBFA_update(model)
XX 200/100 -- 200/100
iteration 0/1000
iteration 1/1000
iteration 2/1000
...
iteration 112/1000
iteration 113/1000
Converged (var(residuals)) after 113 iterations
NULLThe result is the model object with posterior distributions of the variables.
You can get the posterior mean of the inferred confounders (NxK matrix), their weights (GxK matrix), precision (inverse variance) of the weights (Kx1 matrix), and the residual dataset (NxG matrix):
> factors = VBFA_getX(model)
> dim(factors)
[1] 200 11
> weights = VBFA_getW(model)
> dim(weights)
[1] 100 11
> precision = VBFA_getAlpha(model)
> dim(precision)
[1] 11 1
> residuals = VBFA_getResiduals(model)
> dim(residuals)
[1] 200 100
> plot(precision)Note that the weights and factors have 11 components - PEER automatically includes a factor for mean expression. If you do not want this behaviour, use
> VBFA_setAdd_mean(model, FALSE)
NULLIf there are measured experimental variables that may contribute to variability in the data, they can be included in the inference. The C observed covariates are assumed to be in a NxC matrix:
> VBFA_setCovariates(model, as.matrix(covs))
NULLThis sets the first C factors to be fixed to the observed covariates, and extends the hidden factor matrix X to have additional C columns. Remember to cast the covariates as a matrix!
As default, PEER iterates through updates of every variable 1000 times. To set it to say, 100, use
> VBFA_setNmax_iterations(model, 100)
NULLPEER finishes if the increase in lower bound on the model evidence ceases to change, or the variance of the residuals has stabilised. The limiting values (tolerances) can be specified as
> VBFA_setBoundTolerance(model, 1)
NULL
> VBFA_setVarTolerance(model, 0.1)
NULLIn general you can keep the bound tolerance fairly high, but should keep the variation tolerance quite low compared to the variance of the expression matrix. If unsure, use the default values (bound=0.001, variance=0.00001).
Finally, the prior parameters on the noise and weight precision distributions can also be changed. As these are both gamma distributed, you can specify the a and b parameters of both:
> VBFA_setPriorAlpha(model,0.001,0.1)
NULL
> VBFA_setPriorEps(model,0.1,10.)
NULLPEER uses uninformative priors on weight precision and noise precision by default (Alpha a = 0.001, Alpha b = 0.1, Eps a = 0.1, Eps b = 10)
PEER offers an easy API interface to the core functions form PYTHON. Below the same functionality from the standalone tool is mirrored from PYTHON:
import sys import scipy as SP import peer
#1. load expression dataset
Y = SP.loadtxt('expression.csv')
#2. run PEER #use up to 20 factors Kinf = 20 #maximum number of iterations Nmax_iterations = 100
vb = peer.VBFA() #set data and parameters #number of factor for learning vb.setNk(Kinf) #fit mean effect ? vb.setAdd_mean(False) vb.setPhenoMean(Y) #set prior settings #(these are the default settings of PEER) vb.setPriorAlpha(0.001,0.1); vb.setPriorEps(0.1,10); vb.setNmax_iterations(Nmax_iterations) vb.update()
#investigate inferance results #factors: X = vb.getX() #weights: W = vb.getW() #ARD parameters Alpha = vb.getAlpha()
#get corrected dataset: Yc = vb.getResiduals()
#3. plotting (requires matplotlib)
if 1:
import pylab as PL
#plot relevance of factors:
PL.figure(1)
PL.plot(1/Alpha)