Operates on the statistic tests results obtained from msms.glm.pois()
,
msms.glm.qlll()
or msms.edgeR()
. The following variables are
computed: Raw expression mean values for each condition (control and treatment),
log fold change based on these expression levels and taking into account the
normalizing divisors (div
), multitest adjusted pvalues with FDR control,
and a post test filter based on minimum spectral counts and minimum absolute log
fold change as estimated by the statistic test. According to the results of this
posttest filter, features are flagged as T or F depending on whether they
result relevant or not, beyond their statistic signicance.
1 2  test.results(test, msnset, gpf, gp1, gp2, div, alpha = 0.05,
minSpC = 2, minLFC = 1, method = "BH")

test 
The dataframe obtained from either 
msnset 
A MSnSet object with spectral counts in the expression matrix. 
gpf 
The factor used in the tests. 
gp1 
The treatment level name. 
gp2 
The control level name. Should be the factor's reference level.
See R function 
div 
The weights used as divisors (offsets) in the GLM model. Usually the sum of spectral counts of each sample. 
alpha 
The multi test adjusted pvalue significance threshold. 
minSpC 
The minimum spectral counts considered as relevant in the most abundant condition. This filter aims at reaching good reproducibility. 
minLFC 
The minimum absolute log fold change considered both, relevant and biologically significant. This filter aims at assuring enough biological effect size and at reaching good reproducibility. 
method 
One among 
No feature is removed in the filter, but instead they are flagged as TRUE or FALSE
depending on whether they are considered as differentially expressed or not, in the DEP
column, taking into account statistic significance and
reproducibility metrics.
A data frame with the following columns:
first column 
Column named as the treatment level with the mean raw spectral counts observed for this condition 
second column 
Column named as the control level with the mean raw spectral counts observed for this condition 
lFC.Av 
Log fold change computed from the mean expression levesl taking into account the given normalization factors. 
logFC 
Log fold change estimated by fitting the given GLM model. The reference level of the main factor is taken as control. 
D or LR 
The statistic obtained from the tests. The residual deviance

p.val 
The unadjusted pvalues obtained from the tests. 
adjp 
The multitest adjusted pvalues with FDR control. 
DEP 
A logical flagging the features considered both as statistically significant and relevant for reproducibility. 
Josep Gregori i Font
Josep Gregori, Laura Villareal, Alex Sanchez, Jose Baselga, Josep Villanueva (2013). An Effect Size Filter Improves the Reproducibility in Spectral Countingbased Comparative Proteomics. Journal of Proteomics, DOI http://dx.doi.org/10.1016/j.jprot.2013.05.030
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289300.
Alan Dabney, John D. Storey and with assistance from Gregory R. Warnes. qvalue: Qvalue estimation for false discovery rate control. R package version 1.30.0.
pval.by.fc
,
p.adjust
,
qvalue
,
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24  library(msmsTests)
data(msms.dataset)
# Preprocess expression matrix
e < pp.msms.data(msms.dataset)
# Factors
pData(e)
# Control condition
levels(pData(e)$treat)[1]
# Treatment condition
levels(pData(e)$treat)[2]
# Models and normalizing condition
null.f < "y~batch"
alt.f < "y~treat+batch"
div < apply(exprs(e),2,sum)
#Test
res < msms.glm.qlll(e,alt.f,null.f,div=div)
# Posttest filter
lst < test.results(res,e,pData(e)$treat,"U600","U200",div,
alpha=0.05,minSpC=2,minLFC=1,
method="BH")
str(lst)
lst$cond
head(lst$tres)
rownames(lst$tres)[which(lst$tres$DEP)]

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