Hi all,
I'm using Linnorm for multiple groups comparison. I succeeded 2 groups comparison, such as WT vs KO, by following example commands that Linnorm developers showed. The commands were the below:
#Obtain example matrix:
data(LIHC)
#Create limma design matrix (first 5 columns are tumor, last 5 columns are normal)
designmatrix <- c(rep(1,5),rep(2,5))
designmatrix <- model.matrix(~ 0+factor(designmatrix))
colnames(designmatrix) <- c("group1", "group2")
rownames(designmatrix) <- colnames(LIHC)
#DEG analysis
DEGResults <- Linnorm.limma(LIHC, designmatrix)
And I got a same output to the one that the developer mentioned in Package 'Linnorm' documents. The results is the below:
logFC XPM t P.Value adj.P.Val B
CACNA1S|779 0.6931234 6.513083e-03 43.944443 8.127946e-12 1.580235e-07 11.7115287634
HOXD4|3233 2.9022970 6.838703e-02 40.271851 1.776754e-11 1.727182e-07 11.5495184015
PYY|5697 2.0850513 3.424445e-02 37.493428 3.369902e-11 2.183921e-07 11.3990864805
: : : :
So, I tried anova analysis of 3 groups using the same data by changing design matrix, like this.
designmatrix <- c(rep(1,3),rep(2,4), rep(3,3))
designmatrix <- model.matrix(~ 0+factor(designmatrix))
colnames(designmatrix) <- c("group1", "group2", "group3")
rownames(designmatrix) <- colnames(LIHC)
and then do "Linnorm.limma(LIHC, designmatrix)", like at 2-groups comparion. But the output has some different columns like this.
XPM group2.group3 AveExpr F P.Value adj.P.Val
PSMB11|122706 1.148511e-02 0.000000000 2.153807 1847.32855 2.117622e-11 3.365242e-07
A2ML1|144568 3.700802e-02 0.000000000 2.474972 1633.54576 3.461827e-11 3.365242e-07
FMO9P|116123 3.481089e-02 0.000000000 2.449994 1323.63062 8.021902e-11 4.019964e-07
: : : :
Were a serial of my commands at the 3-groups analysis correct and the P.value and adj.P.Val in the results are reliable? If it's so, what do the value in the second column mean? Why group2 and group3 were picked up in the all?
> sessionInfo( )
R version 4.1.0 (2021-05-18)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)
Matrix products: default
locale:
[1] LC_COLLATE=Japanese_Japan.932 LC_CTYPE=Japanese_Japan.932 LC_MONETARY=Japanese_Japan.932
[4] LC_NUMERIC=C LC_TIME=Japanese_Japan.932
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] Linnorm_2.16.0 limma_3.48.1 reshape_0.8.8 data.table_1.14.0
loaded via a namespace (and not attached):
[1] fastcluster_1.2.3 gtools_3.9.2 statmod_1.4.36
[4] zoo_1.8-9 modeltools_0.2-23 tidyselect_1.1.1
[7] kernlab_0.9-29 purrr_0.3.4 splines_4.1.0
[10] lattice_0.20-44 colorspace_2.0-2 vctrs_0.3.8
[13] generics_0.1.0 amap_0.8-18 stats4_4.1.0
[16] mgcv_1.8-36 utf8_1.2.2 rlang_0.4.11
[19] pillar_1.6.1 glue_1.4.2 DBI_1.1.1
[22] prabclus_2.3-2 fpc_2.2-9 lifecycle_1.0.0
[25] plyr_1.8.6 robustbase_0.93-8 munsell_0.5.0
[28] gtable_0.3.0 RcppArmadillo_0.10.6.0.0 permute_0.9-5
[31] flexmix_2.3-17 curl_4.3.2 class_7.3-19
[34] fansi_0.5.0 DEoptimR_1.0-9 Rcpp_1.0.7
[37] diptest_0.76-0 scales_1.1.1 BiocManager_1.30.16
[40] gdata_2.18.0 vegan_2.5-7 apcluster_1.4.8
[43] ellipse_0.4.2 ggplot2_3.3.5 gmodels_2.18.1
[46] Rtsne_0.15 dplyr_1.0.7 grid_4.1.0
[49] tools_4.1.0 magrittr_2.0.1 tibble_3.1.3
[52] cluster_2.1.2 ggdendro_0.1.22 crayon_1.4.1
[55] pkgconfig_2.0.3 Matrix_1.3-4 ellipsis_0.3.2
[58] MASS_7.3-54 assertthat_0.2.1 R6_2.5.0
[61] mclust_5.4.7 nlme_3.1-152 nnet_7.3-16
[64] igraph_1.2.6 compiler_4.1.0
