Purpose: This notebook takes data and metadata from refine.bio and identifies differentially expressed genes. This script is very generally applicable to pre-processed RNA-Seq or microarray data.

1) Install libraries

This script uses the bioconductor R package limma to identify differentially expressed genes.
The full guide on limma shows examples of limma functions. Citation: Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47.

if (!("limma" %in% installed.packages())) {
  # Install ComplexHeatmap
  BiocManager::install("limma", update = FALSE)
}

Attach the limma library:

# Magrittr pipe
`%>%` <- dplyr::`%>%`
# Attach library
library(limma)

Create output folders.

# Create the results folder if it doesn't exist
if (!dir.exists("results")) {
  dir.create("results")
}
# Create the plots folder if it doesn't exist
if (!dir.exists("plots")) {
  dir.create("plots")
}

2) Import and set up data

Data downloaded from refine.bio include a metadata tab separated values (“tsv”) file and a data tsv file. This chunk of code will read the both tsv files and add them as data.frames to your environment.

# Read in metadata tsv file
metadata <- readr::read_tsv(file.path("data", "metadata_GSE71270.tsv"))
Parsed with column specification:
cols(
  .default = col_character(),
  refinebio_age = col_logical(),
  refinebio_cell_line = col_logical(),
  refinebio_compound = col_logical(),
  refinebio_disease = col_logical(),
  refinebio_disease_stage = col_logical(),
  refinebio_genetic_information = col_logical(),
  refinebio_race = col_logical(),
  refinebio_sex = col_logical(),
  refinebio_source_archive_url = col_logical(),
  refinebio_subject = col_logical(),
  refinebio_time = col_logical(),
  refinebio_treatment = col_logical(),
  channel_count = col_double(),
  `contact_zip/postal_code` = col_double(),
  data_row_count = col_double(),
  taxid_ch1 = col_double()
)
See spec(...) for full column specifications.
# Read in data tsv file
df <- readr::read_tsv(file.path("data", "GSE71270.tsv")) %>%
  tibble::column_to_rownames("Gene") # Store gene names as the rownames for now
Parsed with column specification:
cols(
  Gene = col_character(),
  GSM1831680 = col_double(),
  GSM1831675 = col_double(),
  GSM1831681 = col_double(),
  GSM1831683 = col_double(),
  GSM1831677 = col_double(),
  GSM1831676 = col_double(),
  GSM1831682 = col_double(),
  GSM1831679 = col_double(),
  GSM1831678 = col_double(),
  GSM1831684 = col_double()
)

Let’s ensure that the metadata and data are in the same sample order.

# Make the data in the order of the metadata
df <- df %>% dplyr::select(metadata$geo_accession)
# Check if this is in the same order
all.equal(colnames(df), metadata$geo_accession)
[1] TRUE

3) Set up design matrix

limma needs a numeric design matrix to signify which are treatment and control samples. Here we are using the treatments supplied in the metadata to create a design matrix where the “none” samples are assigned 0 and the “amputated” samples are assigned 1. Note that the metadata variables that signify the treatment groups might be different in across datasets and might not always be underneath the “treatment” category.

# Create the design matrix from the genotype information
des.mat <- model.matrix(~metadata$`genotype/variation`)

4) Apply linear model

After applying our data to linear model, in this example we apply empirical Bayes smoothing and Benjamini-Hochberg multiple testing correction. The topTable function default is to use Benjamini Hochberg but this can be changed to a different method using the adjust.method argument.

# Apply linear model to data
fit <- lmFit(df, design = des.mat)
# Apply empirical Bayes to smooth standard errors
fit <- eBayes(fit)
# Apply multiple testing correction and obtain stats
stats <- topTable(fit, number = nrow(df)) %>% 
  tibble::rownames_to_column("Gene")
Removing intercept from test coefficients

5) Explore fitness of model

Here we will use two different plots that can be used to assess the quality of your model. A guide to the interpretation of these plots and other statistics for gene expression data can be found here.

# Create Q-Q plot
qq.plot <- qqt(fit$t, pch = 16, cex = 0.2); abline(0,1)
# Save plot to png
png(file.path("plots", "qqplot_GSE71270.png"))
qqt(fit$t, pch = 16, cex = 0.2); abline(0,1)
dev.off()
quartz_off_screen 
                2 

# Print out plot here
qq.plot
$x
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 [853]  0.563638445 -0.434332154 -2.535824431  0.728261540 -1.665212508  0.733584811
 [859]  0.810455727 -1.725210821  2.421761586  0.056746097  1.350895812  0.633854129
 [865] -2.409202447 -0.154045009  1.726415398 -0.675940078  1.298629516 -2.198545531
 [871]  0.489503679  2.086796116 -1.589657680 -1.427161003  0.655762386  0.786908628
 [877]  0.183672301  1.844654625  0.443456594  1.705687199 -1.077591430 -1.884839206
 [883]  0.196397924  1.137543675  1.187195131  1.525257499  1.750421994 -1.484378069
 [889]  0.811965922  0.325936838  1.078077101  2.195510029  0.884180575  0.902809659
 [895] -0.074444326 -2.041087388 -0.532382208 -1.299261143  0.618484166 -0.152395611
 [901] -2.318330067  1.231216438 -2.000286594 -1.419678573  2.306517641  1.945158481
 [907]  0.683642136  0.904853450 -0.061645130  0.520212214  0.106500390  0.503333083
 [913]  1.411538661  0.970208977  1.216564961  1.636555451  0.222364795  2.214035161
 [919]  0.916369307  1.812837255  0.600003013  2.062262571  0.673381633  0.542269626
 [925]  0.865063168  2.278319216  0.488738141  1.030016235  0.890019432  0.725607632
 [931]  0.902401353  0.491342142  1.374617869 -1.848390661  0.995978103 -0.787834685
 [937]  1.250578691 -1.970078320 -1.610671932 -0.809701322  0.526443498  0.968470487
 [943]  1.232086545  1.265867921  0.471360272 -1.297998408  0.895685886  1.516191012
 [949]  0.885989401  0.401863451  0.473334685  0.059739781  0.845091362 -1.762492478
 [955]  0.733407034  0.977411466 -1.500513399 -0.035938972  0.593834186 -1.686792894
 [961]  2.033511558 -1.253851610  1.255941389 -1.069854987  1.707435341  1.351572754
 [967]  0.919060045 -0.814423947  0.917196519  2.173364795  2.000286594  1.500932287
 [973]  1.923978717 -0.436423065  1.237326890 -2.314356622  2.500249727  0.091893061
 [979]  0.873387748  0.603258944  1.278069847 -1.287347715  1.433584485  1.689617083
 [985] -2.442687230  0.418265097  2.404283405  1.053387456  1.456742355  0.378992736
 [991] -2.217197745  1.270421489 -1.546016490 -0.887599683  0.240504336  1.672869874
 [997]  1.202170085  0.473486639  0.901381246 -0.059331521
 [ reached getOption("max.print") -- omitted 17452 entries ]

$y
   [1]  17.69002371  15.37642277  15.34128427  24.49380153   5.19393482   4.56508175
   [7]  -8.36221594  -6.81209520  -1.70923863 -13.64274663   9.56633649 -10.55066367
  [13]  29.43184435   6.46834086  -1.18840363   0.07450938  11.12482526  -9.62529660
  [19]   0.75617988   2.60917848   9.61056468  -1.94651957  10.88247642  -1.05584348
  [25]  23.07697541  -3.45335086  -5.31441030   3.90544004   5.28078491   9.36897806
  [31]   1.31365384  11.29404930   7.70655950  29.67820414  -0.08231736   6.73795724
  [37]  10.71328870  -9.22955474   4.30249357   2.25677313   7.53140800   8.33921323
  [43]   8.32410906  32.08747891   9.08378485   8.69767809  41.11040509   9.92190665
  [49]   5.78300246   9.18038965  -0.86395060  -2.38273936  -7.38515997   5.48293686
  [55]  13.57254005  12.30122518  -9.16199329   1.97454890  -7.68458745   8.27973255
  [61]   5.30976276  12.88142594   8.52449962  13.24380779 -10.08414682  -2.65972700
  [67]   3.59347282  -7.77561149   8.23851005  -1.04322462  -8.48333094   2.59544133
  [73] -10.01592753   1.12785669 -10.79420467   9.97907848   1.23600911   4.45278915
  [79]   2.02164041  14.73138549   2.95766753   4.71901910 -14.00952976  -8.93062374
  [85]  27.65725164  10.35288798   0.52597836  -2.40383133  -7.25407445   6.79160549
  [91]  13.43820291   1.74226946   9.69433842  11.63676119   4.87811685   3.12710211
  [97]   0.92547749   7.10131086  23.58380495  10.84964346   0.94990872  22.58686734
 [103]  20.90085925  -1.55890719  17.98015016  14.38112144  15.95400877  -3.63302687
 [109] -10.87209992   1.71647073  -1.49140316  -2.39221797  11.29112974  -2.92857790
 [115]  -0.43478902  22.62412497  -6.02621882  15.28842211   6.20176414  16.58295627
 [121]   7.42322899   8.72260726   5.53927443   6.46277700  14.31554129   1.70552630
 [127]  -5.29537632   6.93534920  14.66733855  33.48614286   2.50067789  11.07808742
 [133]   5.94996890  13.95002129  -3.78532146   6.01461607  12.32724475   3.76146091
 [139]  12.04987082  -1.99132909  -3.22337486  18.34623210   4.07360880   8.52150004
 [145]  16.66522470  11.28039063   8.30313665   9.37245877   1.72193702   5.08630937
 [151]  19.86471348 -10.08007625 -11.81772500   3.36958662  14.08375763   2.86468948
 [157] -16.08280146  11.56404099 -10.02770756  -3.44623340   7.61535942  -1.90894089
 [163]  25.33091176  10.75745475  -1.89286629  15.94437156  -8.82585661  19.85376836
 [169]   6.69950763   2.21801071   2.36958553  14.32694150   1.08087294  -7.24229280
 [175]   6.20262990   8.27980846  -1.39635536 -10.24078063  -4.87822097  33.31089887
 [181]  11.11006199  -3.89308521  -0.94470478   5.90592966   7.40114095   5.99775245
 [187]  11.16335676  17.26376715   3.75479976   2.39278218  14.87364522  -8.71235659
 [193]  -1.94213099   5.67305131  24.74879423  -5.45607165  17.03615643  14.99476952
 [199]   2.71813843  -0.50417847  11.35717627   8.72279394  -0.08642740  16.93861475
 [205]  -9.07163013   7.68534236  19.02841961  16.83423941  13.16582614  -4.61827274
 [211]  -0.49946677   3.53120218 -10.73731956   2.57284879   1.00387185  12.60556603
 [217]   5.64064118  36.54782940  13.14641618   7.29335499  10.38128262  -1.45150380
 [223]   7.38237406   8.58375592   7.11341874   3.60914570   8.31235085   8.75948476
 [229]  14.47629807  -0.89958569   9.18710622   8.82674637   8.45765087  12.13731016
 [235]  -3.39444931  -2.58178225  -3.44422879  -6.42205132  13.78231815   6.65465339
 [241]   9.00462606 -12.07478428   7.97957793  10.79837373   9.46365655  16.47137447
 [247]   9.34795274   8.24169961  12.53535060  -6.66824523  -6.26480565  27.31904509
 [253]  -1.23768710  -3.07498236 -10.04564907  14.61708780  12.00593149  11.97441116
 [259]   2.99919514   3.41737164  28.56089472   2.81625598  15.57764993  14.72841694
 [265]  11.35982322  29.63775257  15.76026232  14.09585352   7.74267372 -11.80509757
 [271]  11.62239682   5.63824273   4.42277140  11.59966136  -4.16587411  21.10132613
 [277]  24.73985799   6.94094560   2.11951118   4.30481107  10.94938522 -14.33840865
 [283]  -4.37810054   5.31665341   4.38289007  24.69280484   4.51894832   9.62414448
 [289]  -3.83008956  10.03137272  16.59844211 -16.17578565   2.19174828   6.09544126
 [295]  10.81709451   1.18106191 -11.06769395   8.25137272  11.38653496   5.37247551
 [301]   6.07869403  15.63550643   5.11173242  47.87912449  10.81910411   3.73326407
 [307]  -1.64508790   0.28819003   7.78447945  10.91358503  14.35575695   8.67190689
 [313]  29.15814866   1.22670341   6.56172351  31.10587788  11.99871428 -11.55474456
 [319]   2.89108548  -8.15681420   1.84348643   5.95551903   2.77508588 -10.11986568
 [325] -12.11277895   7.63064426   0.13026689  13.18219810  16.24745647  10.64451561
 [331]   5.56537066   2.18759702  13.61460155   5.68359409   4.63786860   0.72464077
 [337]  -2.17800373   0.93023350   8.71272588   4.32239778 -12.60375980   2.91546838
 [343]  21.29311958  19.00193090  12.99654855  28.92358033  23.05655451  26.95811800
 [349]   3.48572669  -2.88090696  -7.29705572  -7.00658314  -0.42501034  23.33096860
 [355]  -3.88184009  30.75060618   3.40663613  15.19649826  13.08914284  -0.66171250
 [361]   9.14745639   0.35971251   9.40744246  18.40392836   9.03403756  13.85169249
 [367]  18.21012683   2.05029157   9.07337174  -9.80008179  14.94531394   3.86017385
 [373]  -9.59064879   7.62719638  -6.54581271  -3.07981727  17.20650036  -2.65223611
 [379]  -6.65771597   3.87418773  38.86351518  -3.45594959   6.15548598   3.03094744
 [385]   2.58561007   1.90811014  -4.09619946   1.61405451   5.46466630  -2.19731909
 [391]  11.65195135  -8.19373854   6.90498739   8.20452067  -6.43841825  -0.87972014
 [397]   0.61616616   4.29606716   5.02075131  14.85328927  -4.32231754  -2.91286912
 [403]   6.61304663   7.35089608  -7.27314311  24.62085013 -14.20343494   8.24557691
 [409]   2.73546774   5.50457415   4.94578469  24.84512665  -0.48179210   1.91202114
 [415]   3.21228183  23.40623518  10.81854151  -7.08831202  25.84906309  -7.28322617
 [421]   8.88319041   2.51146349   6.47525756   8.94901565   9.42577771  17.32965935
 [427]   8.58927816  -2.39004879   9.08467787  10.01907544   3.45897112   6.48632810
 [433]   7.40141156  29.61994896   0.67957821  -2.02480345  -6.05390057   6.63572673
 [439]  -5.22477551   9.36506809  15.39022571   6.31395521  -2.07243219  -7.04302723
 [445]  26.56571837  13.84369620   0.35705462   8.90590243   3.33848855  25.14233246
 [451]  -5.17561343   1.20310070   6.59275442  16.34289212   1.44700362   8.65394711
 [457]   7.35633341   5.68895832  23.63139426   5.66987971  16.63175429   5.20792489
 [463]  -4.82112441  -9.92077725  17.44813428   1.13233081   7.36190307   7.19397905
 [469]  -1.85815678  24.24343924  -0.70804114   6.09072326  10.97182855  -2.32986683
 [475]  31.88119608  11.27709848   1.76089021  10.83800778  31.28450799  12.84965008
 [481]   4.71763892   0.32117203   7.48851018  11.81460722   3.44776867   6.61241426
 [487]  28.61188020   6.43393420  13.24742707  19.24947545  -4.43350396   8.72414491
 [493]   1.26882594  13.00679126   4.19601722   3.71781390   4.05790651  12.78635552
 [499]  -1.33708596   0.56108811   9.73377239   2.70784953   3.45220314  -6.73952868
 [505]  66.01316678  11.96711293  16.13622616   1.28294336  10.14776857  11.57955673
 [511]   4.69026319 -10.31958948   8.18810254   8.20838007   4.50444365  -3.59912285
 [517]   6.86762688  14.01868870  -3.71123537   2.88253837  11.78110704  12.31504905
 [523]  -0.48147724   2.59315500   4.21685009   9.52519362   8.72704922   8.07351226
 [529]   4.21961864   1.38765528  -0.43849904  11.59268340   7.11465243  -4.69074789
 [535] -11.32975860 -19.00512676   8.98701396  83.23915322  29.72232300  17.20709173
 [541]  31.21352035  -4.90038132   8.42596245  12.94728410  15.39998591   8.26437758
 [547]  -8.87483741  -9.49158814  16.13496377  14.02892290   3.75721860   9.81591811
 [553]  10.36133658   5.38292547   5.28186522   6.41299475   2.44142109  -4.40302114
 [559]  14.00692590   3.00540615  38.11310854   8.26405156   9.88617155  -5.87158632
 [565]  11.65836061   4.36748694 -10.19576725   4.28751782 -11.20985912   8.96227284
 [571]  42.41486853 -11.55871305  -1.99129241   2.02541810   5.59464684  19.27789349
 [577]  15.75302341   0.93276376   0.41004613   5.51828521   6.43077443  20.99225301
 [583] -10.56062852   4.76834075  17.84885407   4.45173601  27.50796374   9.29091237
 [589]  31.01081323   7.42568968  -4.62698134   7.14093180  31.36949339  18.44257224
 [595]  -7.84111309   5.09502549  23.14256806  -1.93962585   1.57692462  10.08516091
 [601]  84.45439788  13.90736417   2.87898255  -2.19445739   9.43082533   3.08649446
 [607]   3.34115240  30.77141400  11.06072313  19.40282264  47.16591412  -9.60295935
 [613]  -0.28975875  11.24975903 -12.98065012  25.18000597   5.34314628   6.41031446
 [619]   4.96100027   9.59687155   4.78162078   6.87564776  10.32427931   8.35397167
 [625]  19.54953622   3.40985918  -0.44017219   5.01696566 -12.08390774  15.30124886
 [631]   5.24088849  19.24045896   7.37770956   2.69038371   4.22177318  -7.04023309
 [637]   2.38630968   8.00876597   9.08058723  16.19362700  -6.84269289  10.95997379
 [643]  76.82859203  -7.80708354   1.06558496  -3.37474449   4.44943043  10.79662537
 [649]   5.14629694   7.59109348  20.57130586   5.87688697  -7.62016324  15.85825587
 [655]  -3.71201199  -1.63096270 -10.33750067 -12.48840446  -3.33056885  10.18825113
 [661]   5.31158189 -16.46275254  18.68928116  -2.32865324  -6.75776952  16.31776163
 [667]  -0.60845811 -10.20424410  -0.49909252   8.16521132   3.35228848   3.31948940
 [673]  19.50101283   7.87390190   5.56210034  15.33494653   2.93704041  10.82830267
 [679]  -0.16976745  15.25447548  11.72492224   8.43763342 -12.19048788   4.90334594
 [685]   0.95336334   5.09946195   1.50246449  15.45486740  11.41108714  21.82098622
 [691]   7.90172947 -10.36136051   5.46445299   5.42081062   0.93685399  -3.30883494
 [697]   6.75782850  18.68550992   8.79338248  -0.68825572   1.31296924   2.54390477
 [703]  -9.19465854   2.88760100   9.68130582  14.58380494  25.21121539   8.13455185
 [709]   4.92845374  52.40125494  19.83004941 -11.95947228  12.56412770  -9.44064231
 [715]   6.43450186   6.11079785   6.38695511  -9.02792736  13.75373990  13.30676356
 [721]   2.32611269  20.05133487  -7.57876662  17.58888696   8.58151639   4.61654586
 [727]  12.66049442  11.10059828   9.06202161  46.60995648  -3.98224001   9.23069582
 [733]  -8.91520518   6.55235930   2.10738772   7.90726314   5.31848312  26.03406787
 [739]   5.81761334  -7.55998896   8.57653481  11.78297317  11.52510634   9.16376651
 [745] -10.08013085  -5.38131914   4.09839288  -9.61587584   9.70275348  16.09466606
 [751]  21.65565673   6.45788587  12.51863021   1.45269145  26.95514023   3.33337744
 [757]  -4.05424649  -2.83231535   5.21029361  14.35836066   1.94767779  15.90656415
 [763]   3.10772434   4.19084085  13.42738707   3.98970850   5.72915008   1.54518272
 [769]  21.63031248  12.32060326   3.78710981   1.32754811   6.04291158   2.72847369
 [775]  -7.05498873   2.28091557  38.68558238  25.16158035   9.00508949   4.75524163
 [781]   7.32358177   8.11150110  10.36063430  -7.11371766  12.26950505  -8.73488745
 [787]   7.18770455  10.56536597  26.71915114  13.46854582  -2.23889091  25.00049082
 [793]   2.14271635  23.23763129   1.81074221   3.44686471   8.22773157   5.78950158
 [799]  -2.30414921   0.80485140  14.04347555  10.57396787  -3.83183758  25.81176480
 [805]  -1.84404228   2.17417890  17.63992076  13.48337124  17.63757172   3.32222436
 [811]  -4.30239234 -13.18367290  -1.47294329  18.49758079   1.67414603  24.86588574
 [817]   4.00568221  -9.79766296  23.07658224   2.10411914  -1.42245553   7.07636236
 [823]  19.88200939   6.65475110  -2.51492880   2.27266313   8.62295319   8.49003704
 [829]   9.41251598  14.18914377   7.93369817  25.34932576   6.68680262   5.35052029
 [835]   7.01276638  -7.32143508   1.84559357  -6.21520297   2.79164994  22.33934437
 [841]   2.78977233   4.72989821   6.56850078   7.29529461  -7.65674869  10.18663769
 [847]   9.46784658  27.97540908   3.51950377  -7.69789275   1.06912399   6.90650278
 [853]   4.24304715  -0.42836337 -13.14994460   5.69948893  -6.94805329   5.74910055
 [859]   6.52980512  -7.49676378  40.49709042   1.49795505  13.85832817   4.82685192
 [865] -12.47581070   0.62939560  20.54957675  -1.33166506  13.03091713 -10.98179928
 [871]   3.73181718  29.05573707  -6.24953371  -4.85102772   5.01444794   6.28067699
 [877]   2.05534608  23.20013142   3.44214673  20.08250615  -2.76775969  -8.83375079
 [883]   2.11072767  10.69492173  11.35307410  16.86687016  21.09923522  -5.31735221
 [889]   6.54005287   2.77204608   9.90802356  32.33478136   7.40805614   7.64908469
 [895]   0.94502573  -9.96580480  -0.80331442  -3.94824510   4.69280341   0.63718943
 [901] -11.80856511  11.91422734  -9.69607164  -4.80691010  35.97768324  25.44833569
 [907]   5.27120006   7.67767860   0.99739507   3.93718564   1.70077969   3.81831887
 [913]  14.90663941   8.56075765  11.70940118  18.75682191   2.23838788  33.01663625
 [919]   7.84442894  22.53167176   4.52992685  28.62137695   5.17005925   4.08857113
 [925]   7.18918540  35.24974927   3.72790040   9.30441705   7.47470685   5.68044995
 [931]   7.63409332   3.74174124  14.28130107  -8.44841790   8.87451656  -1.73217873
 [937]  12.22870670  -9.42052975  -6.41324740  -1.79765349   3.97976656   8.54098685
 [943]  11.93389318  12.47668467   3.60966779  -3.93426204   7.56520617  16.70250460
 [949]   7.43098179   3.17885136   3.62277535   1.50759817   6.93593454  -7.81888527
 [955]   5.74753783   8.64253516  -5.43556990   1.10022704   4.46596957  -7.14554234
 [961]  27.83393519  -3.65179582  12.30997628  -2.73355453  20.11286310  13.87964356
 [967]   7.87655593  -1.82189369   7.85086489  31.66197976  26.91714033  16.42650509
 [973]  25.02505622  -0.43510041  12.01345038 -11.78620890  44.16308067   1.63785015
 [979]   7.27881477   4.55517393  12.67237586  -3.86931289  15.31173204  19.76711688
 [985] -12.58948804   3.28401940  39.77363319   9.57681242  15.67495183   3.05711728
 [991] -11.09999454  12.53828073  -5.86015369  -2.04066313   2.33447249  19.38119957
 [997]  11.54720847   3.62399839   7.61694367   1.00496948
 [ reached getOption("max.print") -- omitted 17452 entries ]

Make a volcano plot and save to png

# Create volcano plot 
voc.plot <- volcanoplot(fit, main = "Volcano Plot")
# Save plot to png
png(file.path("plots", "volcano_plot_GSE71270.png"))
voc.plot
NULL
dev.off()
quartz_off_screen 
                2