Insert R chunk : Ctrl + Alt + i
Reformat Code: Ctrl + Shift + a
# All these libraries were used in Week 3
library(devtools)
library(Biobase)## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
##
## IQR, mad, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, cbind, colnames,
## do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, lengths, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff,
## sort, table, tapply, union, unique, unsplit, which, which.max,
## which.min## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.library(limma)##
## Attaching package: 'limma'## The following object is masked from 'package:BiocGenerics':
##
## plotMAlibrary(edge)
library(genefilter)
library(snpStats)## Loading required package: survival##
## Attaching package: 'survival'## The following object is masked from 'package:edge':
##
## kidney## Loading required package: Matrixlibrary(broom)
# library(MASS)
# library(DESeq2)
# library(qvalue) # Testing this in final two lectures of weeek 3What are the coefficients for the SNP variable? How are they interpreted? (Hint: Don’t forget to recode the 0 values to NA for the SNP data)
data(for.exercise)
use <- seq(1, ncol(snps.10), 10)
sub.10 <- snps.10[,use]snpdata = sub.10@.Data
status = subject.support$cc
snp1 = as.numeric(snpdata[,3])
snp1[snp1 == 0] = NA
glm1 = glm(status ~ snp1, family = "binomial")
tidy(glm1)## term estimate std.error statistic p.value
## 1 (Intercept) 0.1772078 0.2199584 0.8056424 0.4204491
## 2 snp1 -0.1579378 0.1877400 -0.8412582 0.4002033Linear Model = 0.54 Logistic Model = 0.18 Both models are fit on the additive scale. So in both cases the coefficient is the decrease in probability associated with each additional copy of the minor allele.
Linear Model = 0.54 Logistic Model = 0.18 Both models are fit on the additive scale. So in the linear model case, the coefficient is the decrease in probability associated with each additional copy of the minor allele. In the logistic regression case, it is the decrease in the log odds ratio associated with each additional copy of the minor allele.
Linear Model = -0.04 Logistic Model = -0.16 #### MCC1st-CORRECT Both models are fit on the additive scale. So in the linear model case, the coefficient is the decrease in probability associated with each additional copy of the minor allele. In the logistic regression case, it is the decrease in the log odds ratio associated with each additional copy of the minor allele.
Linear Model = -0.16 Logistic Model = -0.04 Both models are fit on the additive scale. So in the linear model case, the coefficient is the decrease in probability associated with each additional copy of the minor allele. In the logistic regression case, it is the decrease in the log odds ratio associated with each additional copy of the minor allele.
sessionInfo()## R version 3.3.1 (2016-06-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04 LTS
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] 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
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] broom_0.4.2 snpStats_1.24.0 Matrix_1.2-8
## [4] survival_2.41-2 genefilter_1.56.0 edge_2.6.0
## [7] limma_3.30.13 Biobase_2.34.0 BiocGenerics_0.20.0
## [10] devtools_1.13.2
##
## loaded via a namespace (and not attached):
## [1] qvalue_2.6.0 reshape2_1.4.2 splines_3.3.1
## [4] lattice_0.20-35 colorspace_1.3-2 htmltools_0.3.6
## [7] stats4_3.3.1 yaml_2.1.14 mgcv_1.8-17
## [10] XML_3.98-1.7 rlang_0.1.1 nloptr_1.0.4
## [13] foreign_0.8-68 withr_1.0.2 DBI_0.6-1
## [16] snm_1.22.0 plyr_1.8.4 sva_3.22.0
## [19] stringr_1.2.0 zlibbioc_1.20.0 munsell_0.4.3
## [22] gtable_0.2.0 lfa_1.4.0 psych_1.7.5
## [25] memoise_1.1.0 evaluate_0.10 knitr_1.16
## [28] IRanges_2.8.1 AnnotationDbi_1.36.2 jackstraw_1.1
## [31] Rcpp_0.12.11 xtable_1.8-2 corpcor_1.6.8
## [34] scales_0.4.1 backports_1.1.0 S4Vectors_0.12.1
## [37] annotate_1.52.1 lme4_1.1-12 mnormt_1.5-5
## [40] ggplot2_2.2.1 digest_0.6.12 stringi_1.1.5
## [43] dplyr_0.5.0 grid_3.3.1 rprojroot_1.2
## [46] tools_3.3.1 bitops_1.0-6 magrittr_1.5
## [49] lazyeval_0.2.0 RCurl_1.95-4.8 tibble_1.3.3
## [52] RSQLite_1.1-2 tidyr_0.6.3 MASS_7.3-45
## [55] assertthat_0.2.0 minqa_1.2.4 rmarkdown_1.6
## [58] R6_2.2.1 nlme_3.1-131