Hello;

I have a simple design, but can't seem to find anything on Bioconductor support that addresses the error I am getting. Any help would be much appreciated.

I have a paired design as shown below:

> pData(data.norm)
        Treatment Patient Age Gender
HD 3.1    control       3  25      F
HD 3.2     miR92a       3  25      F
HD 4.1    control       4  41      F
HD 4.2     miR92a       4  41      F
HD 19.1   control      19  22      F
HD 19.2    miR92a      19  22      F
HD 24.1   control      24  35      F
HD 24.2    miR92a      24  35      F
HD 25.1   control      25  23      F
HD 25.2    miR92a      25  23      F

I am trying to find gene expression associated with Treatment while also controlling for age. I used the following design but end up getting NAs for the age coefficient.  Is there a better way of modeling this?

# Setup design matrix
age <- pData(data.norm)$Age
treat <- pData(data.norm)$Treatment
sample <- pData(data.norm)$Patient
design <- model.matrix(~ sample + treat)

# Fit model
fit <- lmFit(exprs(data.norm), design)

Coefficients not estimable: age 
Warning message:
Partial NA coefficients for 47323 probe(s) 

Many thanks,

Meeta

> sessionInfo()
R version 3.2.5 (2016-04-14)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu precise (12.04.5 LTS)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] splines   parallel  stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] pheatmap_1.0.8             gProfileR_0.5.3            treemap_2.4                gridExtra_2.2.1           
 [5] knitr_1.12.3               CHBUtils_0.1               dplyr_0.4.3                reshape_0.8.5             
 [9] arrayQualityMetrics_3.26.1 RColorBrewer_1.1-2         lattice_0.20-33            limma_3.26.8              
[13] beadarray_2.20.1           ggplot2_2.1.0              Biobase_2.30.0             BiocGenerics_0.16.1   

The code you have shown would not include an Age coefficient in the design at all, so I'm not sure where that warning is coming from. However, controlling for inter-patient differences already controls for the age differences between the patients, so there's no need to add an Age coefficient to your model.

Take a look at the design matrix to ensure you have several columns designated for patient ID. If Patient is an integer/real value, you'll get something like this:

treatment <- c('ctrl', 'trt', 'ctrl', 'trt', 'ctrl', 'trt', 'ctrl', 'trt')
patient <- c(1, 1, 2, 2, 3, 3, 4, 4)
model.matrix(~ patient + treatment)
#   (Intercept) patient treatmenttrt
#             1       1            0
#             1       1            1
#             1       2            0
#             1       2            1
#             1       3            0
#             1       3            1
#             1       4            0
#             1       4            1

But you really want

patient <- factor(patient)
model.matrix(~ patient + treatment)
# (Intercept) patient2 patient3 patient4 treatmenttrt
#           1        0        0        0            0
#           1        0        0        0            1
#           1        1        0        0            0
#           1        1        0        0            1
#           1        0        1        0            0
#           1        0        1        0            1
#           1        0        0        1            0
#           1        0        0        1            1

Sorry, that code is incorrect, it shoudl read:

# Setup design matrix
age <- pData(data.norm)$Age
treat <- pData(data.norm)$Treatment
sample <- pData(data.norm)$Patient
design <- model.matrix(~ sample + age + treat)

# Fit model
fit <- lmFit(exprs(data.norm), design)

I just realized what you meant. Age is already being controlled for within each pair since the samples for treatment and control come from the same individual. Should have seen that, sorry, Thanks!