Dependencies
This document depends on the following packages:
library(devtools)
library(Biobase)## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
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## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following objects are masked from 'package:stats':
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## IQR, mad, xtabs## The following objects are masked from 'package:base':
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## 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
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## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.library(broom)module 2, quiz question #3
con =url("http://bowtie-bio.sourceforge.net/recount/ExpressionSets/bodymap_eset.RData")
load(file=con)
close(con)
bm = bodymap.eset
edata = exprs(bm)
pdata_bm=pData(bm)
ls()## [1] "bm" "bodymap.eset" "con" "edata"
## [5] "pdata_bm"Question #4
Fit a linear model relating he first gene’s counts to the age of the person and the sex of the samples. What is the value and interpretation of the coefficient for age?
A. -23.91. This coefficient means that for each additional year of age, the count goes up by an average of 23.91 for a fixed sex. XX
B. -207.26. This coefficient means that for each additional year of age, the count goes down by an average of 207.26 for a fixed sex. ??
C. -23.91. This coefficient means that for each additional year of age, the count goes down by an average of 23.91 for a fixed sex. MCC1st
D. 2187.91. This means that for a person that is zero years old, the average count is 2187.91 XX
edata = as.matrix(edata)
lm1 = lm(edata[1,] ~ pdata_bm$age)
tidy(lm1)## term estimate std.error statistic p.value
## 1 (Intercept) 2187.90554 402.998816 5.429062 8.884114e-05
## 2 pdata_bm$age -23.24999 6.390269 -3.638343 2.686225e-03table(pdata_bm$gender)##
## F M
## 8 8edata = as.matrix(edata)
lm1 = lm(edata[1,] ~ pdata_bm$gender)
tidy(lm1)## term estimate std.error statistic p.value
## 1 (Intercept) 837.000 228.8055 3.6581292 0.002582905
## 2 pdata_bm$genderM -105.625 323.5798 -0.3264264 0.748930374edata = as.matrix(edata)
lm1 = lm(edata[1,] ~ pdata_bm$gender + pdata_bm$age)
tidy(lm1)## term estimate std.error statistic p.value
## 1 (Intercept) 2331.5814 438.181127 5.3210448 0.000138739
## 2 pdata_bm$genderM -207.2565 236.430512 -0.8766066 0.396610372
## 3 pdata_bm$age -23.9133 6.488058 -3.6857414 0.002743642sessionInfo()## R version 3.3.1 (2016-06-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04 LTS
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## 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
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## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] broom_0.4.2 Biobase_2.34.0 BiocGenerics_0.20.0
## [4] devtools_1.13.2
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## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.11 knitr_1.16 magrittr_1.5 mnormt_1.5-5
## [5] lattice_0.20-35 R6_2.2.1 rlang_0.1.1 stringr_1.2.0
## [9] plyr_1.8.4 dplyr_0.5.0 tools_3.3.1 grid_3.3.1
## [13] nlme_3.1-131 psych_1.7.5 DBI_0.6-1 withr_1.0.2
## [17] htmltools_0.3.6 yaml_2.1.14 rprojroot_1.2 digest_0.6.12
## [21] assertthat_0.2.0 tibble_1.3.3 tidyr_0.6.3 reshape2_1.4.2
## [25] memoise_1.1.0 evaluate_0.10 rmarkdown_1.5 stringi_1.1.5
## [29] backports_1.1.0 foreign_0.8-68