0.1 Basic lab for Bioconductor

Exercise 1
- How do you download any package from Bioconductor ?
- How do you access the vignettes for a given package ?
- How do you find help for a function (say sortSeqlevels)

Execise 2 : Hone your R skills
The Broad Institute has the EpigenomeRoadMap Project Metadata for the project has been made available for you.
- Read the file into R.
- What does R read it in as ?
- How do you see the first few or last few rows of the dataset ?
- How many rows and columns are there inside this file ?
- What are the column headers ?
- What are the data types of each column ?
- Can you summarize the whole data (Hint: ?summary) Summarize the number of males and females in this dataset. - The column called GROUP contains the source of the sample. Can you subset the data.frame to get all samples belonging to BRAIN and DIGESTIVE

Exercise 3 : Getting Comfortable with GenomicRanges
Using the given GRanges , do the following
- Extract ranges only from chromosome 3
- Extract the first five ranges from the GRanges.
- Extract the score and gc column of the GRanges
- Keep only the standard chromosomes (i.e.) from chromosome 1 to 22, X,Y,M.
- Change the chromosome naming style i.e. this GRanges contains UCSC style of chromosome names, change them to NCBI style of chromosome names.
- How do you find out the ranges contained in the gaps of this GRanges object?
- How do you find out the degree of overlap for all the ranges in a GRanges object ? ( Hint: ?coverage)

library(GenomicRanges)
gr <-
 GRanges(seqnames = paste0("chr", c(1:22, tail(letters, 11))),
 ranges = IRanges(start=1:33, width = 1000 ),
 strand = c(rep("+", 10), rep("-", 23)),
 score = 1:33,
 GC = seq(1, 0, length=33))

Exercise 4: Create and Manipulate a SummarizeExperiment Object
In this small exercise, We have data for 20 genes from 9 highly talented individuals and we will create our first SummarizedExperiment object.

The data for 20 genes from 9 individuals results in a matrix

data <- matrix(1:180, ncol=9,  byrow=TRUE)

The data from the 20 genes can be represented as a GRanges

gr_20gene <-
    GRanges(seqnames = paste0("gene", 1:20),   
    ranges = IRanges(start=1:20, width = 1000 ),  
    strand = c(rep("+", 10), rep("-", 10)),  
    score = 1:20,  
    GC = seq(1, 0, length=20))

The data about the 20 individuals is stored in a data.frame

sample_df <- data.frame( names=c("Martin", "Herve", "Dan",   
    "Marc", "Valerie", "Jim", "Nate","Paul", "Sonali"),   
    sex=c(rep("Male", 4), "Female", rep("Male", 3), "Female"))

Create a SummarizedExperiment from the three objects listed above.
Check the dimensions of the created SummarizedExperiment
How would you access the data matrix containing numbers?
How would you access the information about the genes ?
How would you access the information about the samples?
Subset the SummarizedExperiment to create a new one which contains information only about the female core team.
Subset the SummarizedExperiment to create a new one containing only the first two genes.

0.2 Solutions

Answer 1
If the package we are trying to download is called GenomeInfoDb then

source("http://bioconductor.org/biocLite.R")
biocLite("GenomeInfoDb")

vignette(package="GenomeInfoDb")

?sortSeqlevels

Answer 2

##  Reading the data 
fname <- system.file("extdata", "epi_metadata.txt", package="BioC2015Introduction")
df <- read.delim(fname, stringsAsFactors=FALSE)

##  Exploring the data
class(df)

## [1] "data.frame"

head(df)

##    EID    GROUP   COLOR          MNEMONIC                                   STD_NAME
## 1 E001      ESC #924965            ESC.I3                                ES-I3 Cells
## 2 E002      ESC #924965           ESC.WA7                               ES-WA7 Cells
## 3 E003      ESC #924965            ESC.H1                                   H1 Cells
## 4 E004 ES-deriv #4178AE ESDR.H1.BMP4.MESO H1 BMP4 Derived Mesendoderm Cultured Cells
## 5 E005 ES-deriv #4178AE ESDR.H1.BMP4.TROP H1 BMP4 Derived Trophoblast Cultured Cells
## 6 E006 ES-deriv #4178AE       ESDR.H1.MSC          H1 Derived Mesenchymal Stem Cells
##                                   EDACC_NAME     ANATOMY           TYPE AGE    SEX SOLID_LIQUID
## 1                            ES-I3_Cell_Line         ESC PrimaryCulture  CL Female         <NA>
## 2                           ES-WA7_Cell_Line         ESC PrimaryCulture  CL Female         <NA>
## 3                               H1_Cell_Line         ESC PrimaryCulture  CL   Male         <NA>
## 4 H1_BMP4_Derived_Mesendoderm_Cultured_Cells ESC_DERIVED     ESCDerived  CL   Male         <NA>
## 5 H1_BMP4_Derived_Trophoblast_Cultured_Cells ESC_DERIVED     ESCDerived  CL   Male         <NA>
## 6          H1_Derived_Mesenchymal_Stem_Cells ESC_DERIVED     ESCDerived  CL   Male         <NA>
##   ETHNICITY SINGLEDONOR_COMPOSITE
## 1      <NA>                    SD
## 2      <NA>                    SD
## 3      <NA>                    SD
## 4      <NA>                    SD
## 5      <NA>                    SD
## 6      <NA>                    SD

tail(df)

##      EID      GROUP   COLOR      MNEMONIC                                      STD_NAME
## 122 E124 ENCODE2012 #000000 BLD.CD14.MONO         Monocytes-CD14+ RO01746 Primary Cells
## 123 E125 ENCODE2012 #000000       BRN.NHA                 NH-A Astrocytes Primary Cells
## 124 E126 ENCODE2012 #000000   SKIN.NHDFAD NHDF-Ad Adult Dermal Fibroblast Primary Cells
## 125 E127 ENCODE2012 #000000     SKIN.NHEK     NHEK-Epidermal Keratinocyte Primary Cells
## 126 E128 ENCODE2012 #000000      LNG.NHLF            NHLF Lung Fibroblast Primary Cells
## 127 E129 ENCODE2012 #000000    BONE.OSTEO                      Osteoblast Primary Cells
##                           EDACC_NAME ANATOMY           TYPE AGE     SEX SOLID_LIQUID ETHNICITY
## 122          Monocytes-CD14+_RO01746   BLOOD    PrimaryCell      Female                       
## 123                  NH-A_Astrocytes   BRAIN PrimaryCulture     Unknown                       
## 124 NHDF-Ad_Adult_Dermal_Fibroblasts    SKIN PrimaryCulture      Female                       
## 125     NHEK-Epidermal_Keratinocytes    SKIN PrimaryCulture     Unknown                       
## 126            NHLF_Lung_Fibroblasts    LUNG PrimaryCulture     Unknown                       
## 127                      Osteoblasts    BONE PrimaryCulture     Unknown                       
##     SINGLEDONOR_COMPOSITE
## 122                    SD
## 123                    SD
## 124                    SD
## 125                    SD
## 126                    SD
## 127                    SD

dim(df)

## [1] 127  13

colnames(df)

##  [1] "EID"                   "GROUP"                 "COLOR"                 "MNEMONIC"             
##  [5] "STD_NAME"              "EDACC_NAME"            "ANATOMY"               "TYPE"                 
##  [9] "AGE"                   "SEX"                   "SOLID_LIQUID"          "ETHNICITY"            
## [13] "SINGLEDONOR_COMPOSITE"

sapply(df, class)

##                   EID                 GROUP                 COLOR              MNEMONIC 
##           "character"           "character"           "character"           "character" 
##              STD_NAME            EDACC_NAME               ANATOMY                  TYPE 
##           "character"           "character"           "character"           "character" 
##                   AGE                   SEX          SOLID_LIQUID             ETHNICITY 
##           "character"           "character"           "character"           "character" 
## SINGLEDONOR_COMPOSITE 
##           "character"

## Summarize the data 
summary(df)

##      EID               GROUP              COLOR             MNEMONIC           STD_NAME        
##  Length:127         Length:127         Length:127         Length:127         Length:127        
##  Class :character   Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##   EDACC_NAME          ANATOMY              TYPE               AGE                SEX           
##  Length:127         Length:127         Length:127         Length:127         Length:127        
##  Class :character   Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##  SOLID_LIQUID        ETHNICITY         SINGLEDONOR_COMPOSITE
##  Length:127         Length:127         Length:127           
##  Class :character   Class :character   Class :character     
##  Mode  :character   Mode  :character   Mode  :character

table(df$SEX)

## 
##         Female Female/Unknown           Male   Male/Unknown          Mixed        Unknown 
##             38              1             51              1             12             24

##  Subset the data 
df[df$GROUP %in% c("Brain", "Digestive"),]

##      EID     GROUP   COLOR            MNEMONIC                             STD_NAME
## 65  E067     Brain #C5912B         BRN.ANG.GYR                  Brain Angular Gyrus
## 66  E068     Brain #C5912B        BRN.ANT.CAUD               Brain Anterior Caudate
## 67  E069     Brain #C5912B        BRN.CING.GYR                Brain Cingulate Gyrus
## 68  E070     Brain #C5912B        BRN.GRM.MTRX                Brain Germinal Matrix
## 69  E071     Brain #C5912B        BRN.HIPP.MID             Brain Hippocampus Middle
## 70  E072     Brain #C5912B         BRN.INF.TMP         Brain Inferior Temporal Lobe
## 71  E073     Brain #C5912B BRN.DL.PRFRNTL.CRTX Brain_Dorsolateral_Prefrontal_Cortex
## 72  E074     Brain #C5912B         BRN.SUB.NIG               Brain Substantia Nigra
## 73  E075 Digestive #C58DAA          GI.CLN.MUC                       Colonic Mucosa
## 75  E077 Digestive #C58DAA          GI.DUO.MUC                      Duodenum Mucosa
## 77  E079 Digestive #C58DAA              GI.ESO                            Esophagus
## 79  E081     Brain #C5912B           BRN.FET.M                     Fetal Brain Male
## 80  E082     Brain #C5912B           BRN.FET.F                   Fetal Brain Female
## 82  E084 Digestive #C58DAA        GI.L.INT.FET                Fetal Intestine Large
## 83  E085 Digestive #C58DAA        GI.S.INT.FET                Fetal Intestine Small
## 90  E092 Digestive #C58DAA         GI.STMC.FET                        Fetal Stomach
## 92  E094 Digestive #C58DAA        GI.STMC.GAST                              Gastric
## 99  E101 Digestive #C58DAA      GI.RECT.MUC.29               Rectal Mucosa Donor 29
## 100 E102 Digestive #C58DAA      GI.RECT.MUC.31               Rectal Mucosa Donor 31
## 104 E106 Digestive #C58DAA          GI.CLN.SIG                        Sigmoid Colon
## 107 E109 Digestive #C58DAA            GI.S.INT                      Small Intestine
## 108 E110 Digestive #C58DAA         GI.STMC.MUC                       Stomach Mucosa
##                       EDACC_NAME      ANATOMY          TYPE        AGE          SEX SOLID_LIQUID
## 65           Brain_Angular_Gyrus        BRAIN PrimaryTissue   75Y, 81Y        Mixed        SOLID
## 66        Brain_Anterior_Caudate        BRAIN PrimaryTissue   75Y, 81Y        Mixed        SOLID
## 67         Brain_Cingulate_Gyrus        BRAIN PrimaryTissue   75Y, 81Y        Mixed        SOLID
## 68         Brain_Germinal_Matrix        BRAIN PrimaryTissue       20GW         Male        SOLID
## 69      Brain_Hippocampus_Middle        BRAIN PrimaryTissue   81Y, 73Y         Male        SOLID
## 70  Brain_Inferior_Temporal_Lobe        BRAIN PrimaryTissue   75Y, 81Y        Mixed        SOLID
## 71        Brain_Mid_Frontal_Lobe        BRAIN PrimaryTissue   75Y, 81Y        Mixed        SOLID
## 72        Brain_Substantia_Nigra        BRAIN PrimaryTissue   75Y, 81Y        Mixed        SOLID
## 73                Colonic_Mucosa     GI_COLON PrimaryTissue        73Y       Female        SOLID
## 75               Duodenum_Mucosa  GI_DUODENUM PrimaryTissue        76Y         Male        SOLID
## 77                     Esophagus GI_ESOPHAGUS PrimaryTissue        34Y         Male        SOLID
## 79              Fetal_Brain_Male        BRAIN PrimaryTissue 17GW, 17GW Male/Unknown        SOLID
## 80            Fetal_Brain_Female        BRAIN PrimaryTissue 17GW. 17GW       Female        SOLID
## 82         Fetal_Intestine_Large GI_INTESTINE PrimaryTissue       15GW         Male        SOLID
## 83         Fetal_Intestine_Small GI_INTESTINE PrimaryTissue       15GW         Male        SOLID
## 90                 Fetal_Stomach   GI_STOMACH PrimaryTissue                  Female        SOLID
## 92                       Gastric   GI_STOMACH PrimaryTissue        34Y         Male        SOLID
## 99        Rectal_Mucosa.Donor_29    GI_RECTUM PrimaryTissue        50Y       Female        SOLID
## 100       Rectal_Mucosa.Donor_31    GI_RECTUM PrimaryTissue        61Y       Female        SOLID
## 104                Sigmoid_Colon     GI_COLON PrimaryTissue    3Y, 34Y         Male        SOLID
## 107              Small_Intestine GI_INTESTINE PrimaryTissue    3Y, 34Y         Male        SOLID
## 108               Stomach_Mucosa   GI_STOMACH PrimaryTissue        59Y         Male        SOLID
##                                 ETHNICITY SINGLEDONOR_COMPOSITE
## 65                                Unknown                     C
## 66                                Unknown                     C
## 67                                Unknown                     C
## 68                                Unknown                    SD
## 69                                Unknown                     C
## 70                                Unknown                     C
## 71                                Unknown                     C
## 72                                Unknown                     C
## 73                              Caucasian                    SD
## 75                              Caucasian                    SD
## 77                              Caucasian                    SD
## 79                                Unknown                     C
## 80                                Unknown                     C
## 82                                Unknown                    SD
## 83                                Unknown                    SD
## 90                                                            C
## 92                              Caucasian                    SD
## 99                              Caucasian                    SD
## 100                             Caucasian                    SD
## 104 Caucasian/African American, Caucasian                     C
## 107 Caucasian/African American, Caucasian                     C
## 108                             Caucasian                    SD

Answer 3

library(GenomicRanges)
gr <-
 GRanges(seqnames = paste0("chr", c(1:22, tail(letters, 11))),
 ranges = IRanges(start=1:33, width = 1000 ),
 strand = c(rep("+", 10), rep("-", 23)),
 score = 1:33,
 GC = seq(1, 0, length=33))

## extract ranges only from chromosome 3 
gr[seqnames(gr) %in% "chr3",]

## GRanges object with 1 range and 2 metadata columns:
##       seqnames    ranges strand |     score        GC
##          <Rle> <IRanges>  <Rle> | <integer> <numeric>
##   [1]     chr3 [3, 1002]      + |         3    0.9375
##   -------
##   seqinfo: 33 sequences from an unspecified genome; no seqlengths

## extract the first five ranges from the GRanges.
gr[1:5, ]

## GRanges object with 5 ranges and 2 metadata columns:
##       seqnames    ranges strand |     score        GC
##          <Rle> <IRanges>  <Rle> | <integer> <numeric>
##   [1]     chr1 [1, 1000]      + |         1         1
##   [2]     chr2 [2, 1001]      + |         2   0.96875
##   [3]     chr3 [3, 1002]      + |         3    0.9375
##   [4]     chr4 [4, 1003]      + |         4   0.90625
##   [5]     chr5 [5, 1004]      + |         5     0.875
##   -------
##   seqinfo: 33 sequences from an unspecified genome; no seqlengths

## extract the score and sequence column from a GRanges
mcols(gr)

## DataFrame with 33 rows and 2 columns
##         score        GC
##     <integer> <numeric>
## 1           1   1.00000
## 2           2   0.96875
## 3           3   0.93750
## 4           4   0.90625
## 5           5   0.87500
## ...       ...       ...
## 29         29   0.12500
## 30         30   0.09375
## 31         31   0.06250
## 32         32   0.03125
## 33         33   0.00000

## keep only the standard chromosomes (i.e.) from chromosome 1 to 22, x, y,m
keepStandardChromosomes(gr)

## GRanges object with 22 ranges and 2 metadata columns:
##        seqnames     ranges strand   |     score        GC
##           <Rle>  <IRanges>  <Rle>   | <integer> <numeric>
##    [1]     chr1  [1, 1000]      +   |         1         1
##    [2]     chr2  [2, 1001]      +   |         2   0.96875
##    [3]     chr3  [3, 1002]      +   |         3    0.9375
##    [4]     chr4  [4, 1003]      +   |         4   0.90625
##    [5]     chr5  [5, 1004]      +   |         5     0.875
##    ...      ...        ...    ... ...       ...       ...
##   [18]    chr18 [18, 1017]      -   |        18   0.46875
##   [19]    chr19 [19, 1018]      -   |        19    0.4375
##   [20]    chr20 [20, 1019]      -   |        20   0.40625
##   [21]    chr21 [21, 1020]      -   |        21     0.375
##   [22]    chr22 [22, 1021]      -   |        22   0.34375
##   -------
##   seqinfo: 22 sequences from an unspecified genome; no seqlengths

## change the chromosome naming style to NCBI
seqlevelsStyle(gr) <- "NCBI"
gr

## GRanges object with 33 ranges and 2 metadata columns:
##        seqnames     ranges strand   |     score        GC
##           <Rle>  <IRanges>  <Rle>   | <integer> <numeric>
##    [1]        1  [1, 1000]      +   |         1         1
##    [2]        2  [2, 1001]      +   |         2   0.96875
##    [3]        3  [3, 1002]      +   |         3    0.9375
##    [4]        4  [4, 1003]      +   |         4   0.90625
##    [5]        5  [5, 1004]      +   |         5     0.875
##    ...      ...        ...    ... ...       ...       ...
##   [29]     chrv [29, 1028]      -   |        29     0.125
##   [30]     chrw [30, 1029]      -   |        30   0.09375
##   [31]     chrx [31, 1030]      -   |        31    0.0625
##   [32]     chry [32, 1031]      -   |        32   0.03125
##   [33]     chrz [33, 1032]      -   |        33         0
##   -------
##   seqinfo: 33 sequences from an unspecified genome; no seqlengths

## gaps in the ranges
gaps(gr)

## GRanges object with 32 ranges and 0 metadata columns:
##        seqnames    ranges strand
##           <Rle> <IRanges>  <Rle>
##    [1]        2    [1, 1]      +
##    [2]        3    [1, 2]      +
##    [3]        4    [1, 3]      +
##    [4]        5    [1, 4]      +
##    [5]        6    [1, 5]      +
##    ...      ...       ...    ...
##   [28]     chrv   [1, 28]      -
##   [29]     chrw   [1, 29]      -
##   [30]     chrx   [1, 30]      -
##   [31]     chry   [1, 31]      -
##   [32]     chrz   [1, 32]      -
##   -------
##   seqinfo: 33 sequences from an unspecified genome; no seqlengths

## find degree of overlap for ranges.
coverage(gr)

## RleList of length 33
## $`1`
## integer-Rle of length 1000 with 1 run
##   Lengths: 1000
##   Values :    1
## 
## $`2`
## integer-Rle of length 1001 with 2 runs
##   Lengths:    1 1000
##   Values :    0    1
## 
## $`3`
## integer-Rle of length 1002 with 2 runs
##   Lengths:    2 1000
##   Values :    0    1
## 
## $`4`
## integer-Rle of length 1003 with 2 runs
##   Lengths:    3 1000
##   Values :    0    1
## 
## $`5`
## integer-Rle of length 1004 with 2 runs
##   Lengths:    4 1000
##   Values :    0    1
## 
## ...
## <28 more elements>

Answer4 summarizedExperiment exercise

library(SummarizedExperiment)

## data for the SummarizedExperiment object
sample_df <- data.frame( names=c("Martin", "Herve", "Dan",   
    "Marc", "Valerie", "Jim", "Nate","Paul", "Sonali"),   
    sex=c(rep("Male", 4), "Female", rep("Male", 3), "Female"))
gr_20genes <-
    GRanges(seqnames = paste0("gene", 1:20),
    ranges = IRanges(start=1:20, width = 1000 ),
    strand = c(rep("+", 10), rep("-", 10)),
    score = 1:20,
    GC = seq(1, 0, length=20))
data <- matrix(1:180, ncol=9, byrow=TRUE)

## create a SummarizedExperiment object
core_se <- SummarizedExperiment(assays=data,
           rowRanges=gr_20genes, 
           colData=DataFrame(sample_df))
core_se

## class: RangedSummarizedExperiment 
## dim: 20 9 
## metadata(0):
## assays(1): ''
## rownames: NULL
## rowRanges metadata column names(2): score GC
## colnames: NULL
## colData names(2): names sex

## exploring the SummarizedExperiment object
dim(core_se)

## [1] 20  9

head(assay(core_se))  # data matrix

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
## [1,]    1    2    3    4    5    6    7    8    9
## [2,]   10   11   12   13   14   15   16   17   18
## [3,]   19   20   21   22   23   24   25   26   27
## [4,]   28   29   30   31   32   33   34   35   36
## [5,]   37   38   39   40   41   42   43   44   45
## [6,]   46   47   48   49   50   51   52   53   54

rowRanges(core_se)  # information about the genes

## GRanges object with 20 ranges and 2 metadata columns:
##        seqnames     ranges strand   |     score                 GC
##           <Rle>  <IRanges>  <Rle>   | <integer>          <numeric>
##    [1]    gene1  [1, 1000]      +   |         1                  1
##    [2]    gene2  [2, 1001]      +   |         2  0.947368421052632
##    [3]    gene3  [3, 1002]      +   |         3  0.894736842105263
##    [4]    gene4  [4, 1003]      +   |         4  0.842105263157895
##    [5]    gene5  [5, 1004]      +   |         5  0.789473684210526
##    ...      ...        ...    ... ...       ...                ...
##   [16]   gene16 [16, 1015]      -   |        16  0.210526315789474
##   [17]   gene17 [17, 1016]      -   |        17  0.157894736842105
##   [18]   gene18 [18, 1017]      -   |        18  0.105263157894737
##   [19]   gene19 [19, 1018]      -   |        19 0.0526315789473685
##   [20]   gene20 [20, 1019]      -   |        20                  0
##   -------
##   seqinfo: 20 sequences from an unspecified genome; no seqlengths

colData(core_se)  # sample information

## DataFrame with 9 rows and 2 columns
##      names      sex
##   <factor> <factor>
## 1   Martin     Male
## 2    Herve     Male
## 3      Dan     Male
## 4     Marc     Male
## 5  Valerie   Female
## 6      Jim     Male
## 7     Nate     Male
## 8     Paul     Male
## 9   Sonali   Female

## subset the SummarizedExperiment object
## subsetting the sample information
core_se[, core_se$sex == "Female"]

## class: RangedSummarizedExperiment 
## dim: 20 2 
## metadata(0):
## assays(1): ''
## rownames: NULL
## rowRanges metadata column names(2): score GC
## colnames: NULL
## colData names(2): names sex

## subsetting the gene information
core_se[,1:2]

## class: RangedSummarizedExperiment 
## dim: 20 2 
## metadata(0):
## assays(1): ''
## rownames: NULL
## rowRanges metadata column names(2): score GC
## colnames: NULL
## colData names(2): names sex

0.3 `sessionInfo()`

sessionInfo()

## R version 3.2.1 (2015-06-18)
## Platform: x86_64-unknown-linux-gnu (64-bit)
## Running under: Ubuntu 14.04.2 LTS
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       
##  [4] LC_COLLATE=C               LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    parallel  stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] org.Hs.eg.db_3.1.2                      RSQLite_1.0.0                          
##  [3] DBI_0.3.1                               TxDb.Hsapiens.UCSC.hg19.knownGene_3.1.3
##  [5] GenomicFeatures_1.21.13                 AnnotationDbi_1.31.17                  
##  [7] AnnotationHub_2.1.30                    RNAseqData.HNRNPC.bam.chr14_0.7.0      
##  [9] GenomicAlignments_1.5.11                Rsamtools_1.21.14                      
## [11] Biostrings_2.37.2                       XVector_0.9.1                          
## [13] SummarizedExperiment_0.3.2              Biobase_2.29.1                         
## [15] GenomicRanges_1.21.16                   GenomeInfoDb_1.5.8                     
## [17] IRanges_2.3.14                          S4Vectors_0.7.10                       
## [19] BiocGenerics_0.15.3                     ggplot2_1.0.1                          
## [21] BiocStyle_1.7.4                        
## 
## loaded via a namespace (and not attached):
##  [1] reshape2_1.4.1               colorspace_1.2-6             htmltools_0.2.6             
##  [4] rtracklayer_1.29.12          yaml_2.1.13                  interactiveDisplayBase_1.7.0
##  [7] XML_3.98-1.3                 BiocParallel_1.3.34          lambda.r_1.1.7              
## [10] plyr_1.8.3                   stringr_1.0.0                zlibbioc_1.15.0             
## [13] munsell_0.4.2                gtable_0.1.2                 futile.logger_1.4.1         
## [16] codetools_0.2-14             evaluate_0.7                 labeling_0.3                
## [19] knitr_1.10.5                 biomaRt_2.25.1               httpuv_1.3.2                
## [22] BiocInstaller_1.19.8         curl_0.9.1                   proto_0.3-10                
## [25] Rcpp_0.11.6                  xtable_1.7-4                 scales_0.2.5                
## [28] formatR_1.2                  mime_0.3                     digest_0.6.8                
## [31] stringi_0.5-5                shiny_0.12.1                 grid_3.2.1                  
## [34] tools_3.2.1                  bitops_1.0-6                 magrittr_1.5                
## [37] RCurl_1.95-4.7               futile.options_1.0.0         MASS_7.3-43                 
## [40] rmarkdown_0.7                httr_1.0.0                   R6_2.1.0

Basic Lab for R & Bioconductor

Sonali Arora

Contents

0.1 Basic lab for Bioconductor

0.2 Solutions

0.3 `sessionInfo()`

Basic Lab for R & Bioconductor

Sonali Arora

Contents

0.1 Basic lab for Bioconductor

0.2 Solutions

0.3 sessionInfo()

0.3 `sessionInfo()`