1. Overview
This vignette is the second chapter in the “Pathway Significance
Testing with pathwayPCA” workflow, providing a detailed
perspective to the Import
Data section of the Quickstart Guide. This vignette will discuss
using the the read_gmt function to import Gene Matrix
Transposed (.gmt) pathway
collection files as a list object with class
pathwayCollection. Also, we will discuss importing assay
and response data, and how to make your assay data tidy. For our
pathway analysis to be meaningful, we need gene expression data (from a
microarray or something similar), corresponding phenotype information
(such as weight, type of cancer, or survival time and censoring
indicator), and a pathway collection.
Before we move on, we will outline our steps. After reading this
vignette, you should be able to
- Import a
.gmt file and save the pathways stored therein
as a pathwayCollection object using the
read_gmt function.
- Import an assay
.csv file with the
read_csv function from the readr package, and
transpose this data frame into “tidy” form with the
TransposeAssay function.
- Import phenotype information stored in a
.csv file, and
join (merge) it to the assay data frame with the inner_join
function from the dplyr package.
First, load the pathwayPCA package and the tidyverse package
suite.
library(tidyverse)
# Set tibble data frame print options
options(tibble.max_extra_cols = 10)
library(pathwayPCA)
2. GMT Files
The .gmt format is a commonly used file format for
storing pathway
collections. Lists of pathways in the Molecular Signatures Database
(MSigDB) can be downloaded from the MSigDB
Collections page.
2.2 Import GMT files with read_gmt
Based on the clearly-organized .gmt file format, we were
able to write a very fast function to read .gmt files into
R. The read_gmt function takes in a path specifying where
your .gmt file is stored, and outputs a pathways list.
gmt_path <- system.file("extdata", "c2.cp.v6.0.symbols.gmt",
package = "pathwayPCA", mustWork = TRUE)
cp_pathwayCollection <- read_gmt(gmt_path, description = TRUE)
We now carefully discuss the form of this information. This
cp_pathwayCollection object has class
pathwayCollection and contains the following
components:
pathways: A list of character vectors. Each character
vector should contain a subset of the names of the -Omes measured in
your assay data frame. These pathways should not be too short, otherwise
we devolve the problem into simply testing individual genes. The
pathwayPCA package requires each pathway to have a minimum
of three genes recorded in the assay data frame.
Important: some protein set lists have proteins
markers recorded as character numerics (e.g. “3”), so make sure the
feature names of your assay have an overlap with the gene or protein
names in the pathwayCollection list. Ensure that there is a
non-empty overlap between the gene names in the pathways list and the
feature names of the assay. Not every gene in your assay data frame will
be in the pathways list, and not every gene in each pathway will have a
corresponding measurement in the assay data frame. However, for
meaningful results, there should be a significant overlap between the
genes measured in the assay data frame and the gene names stored in the
pathways list. If your pathways list has very few matching genes in
your assay, then your pathway-based analysis results will be
significantly degraded. Make sure your pathways list and assay
data are compatible.
TERMS: A character vector comprised of the proper name
of each pathway in the pathway collection.description: (OPTIONAL) A character vector the same
length as the pathways list with descriptive information. For instance,
the .gmt file included with this package has hyperlinks to
the MSigDB description card for that pathway in this field. This field
will be imported by the read_gmt function when
description = TRUE (it defaults to
FALSE).setsize: the number of genes originally recorded in
each pathway, stored as an integer vector. NOTE: this information is
calculated and added to the pathways list at Omics-class
object creation (later in the workflow). This information is useful
to measure the ratio of the number of genes from each pathway recorded
in your assay to the number of genes defined to be in that pathway. For
each pathway, this ratio should be at least 0.5 for best pathway
analysis results.
The object itself has the following structure:
cp_pathwayCollection
#> Object with Class(es) 'pathwayCollection', 'list' [package 'pathwayPCA'] with 3 elements:
#> $ pathways :List of 1329
#> $ TERMS : chr [1:1329] "KEGG_GLYCOLYSIS_GLUCONEOGENESIS" ...
#> $ description: chr [1:1329] "http://www.broadinstitute.org/gsea/msigdb/cards/KEGG_GLYCOLYSIS_GLUCONEOGENESIS" ...
This object will be the list supplied to the
pathwayCollection_ls argument in the
CreateOmics function.
2.3 Creating Your Own pathwayCollection List
Additionally, you can create a pathwayCollection object
from scratch with the CreatePathwayCollection function.
This may be useful to users who have their pathway information stored in
some form other than a .gmt file. You must supply a list of
vectors of gene names to the pathways argument, and a
vector of the proper names of each pathway to the TERMS
argument. You could also store any other pertinant pathway information
by passing a <name> = <value> pair to this
function.
myPathways_ls <- list(
pathway1 = c("Gene1", "Gene2"),
pathway2 = c("Gene3", "Gene4", "Gene5"),
pathway3 = "Gene6"
)
myPathway_names <- c(
"KEGG_IMPORTANT_PATHWAY_1",
"KEGG_IMPORTANT_PATHWAY_2",
"SOME_OTHER_PATHWAY"
)
CreatePathwayCollection(
sets_ls = myPathways_ls,
TERMS = myPathway_names,
website = "URL_TO_PATHWAY_CITATION"
)
#> Object with Class(es) 'pathwayCollection', 'list' [package 'pathwayPCA'] with 3 elements:
#> $ pathways:List of 3
#> $ TERMS : chr [1:3] "KEGG_IMPORTANT_PATHWAY_1" ...
#> $ website : chr "URL_TO_PATHWAY_CITATION"
2.4 Importing a Pathway Collection from Wikipathways
To download a .gmt file from Wikipathways, we recommend
the R package rWikiPathways.
From their vignette:
WikiPathways also provides a monthly data release archived at http://data.wikipathways.org. The archive includes GPML,
GMT and SVG collections by organism and timestamped. There’s an R
function for grabbing files from the archive…
downloadPathwayArchive()
This will simply open the archive in your default browser so you can
look around (in case you don’t know what you are looking for). By
default, it opens to the latest collection of GPML files. However, if
you provide an organism, then it will download that file to your current
working directory or specified destpath. For example, here’s how you’d
get the latest GMT file for mouse:
downloadPathwayArchive(organism = "Mus musculus", format = "gmt")
And if you might want to specify an archive date so that you can
easily share and reproduce your script at any time in the future and get
the same result. Remember, new pathways are being added to WikiPathways
every month and existing pathways are improved continuously!
downloadPathwayArchive(date = "20171010", organism = "Mus musculus", format = "gmt")
2.4 Writing a pathwayCollection Object to a
.gmt File
Finally, we can save the pathwayCollection object we
just created via the write_gmt() function:
write_gmt(
pathwayCollection = cp_pathwayCollection,
file = "../test.gmt"
)
3. Import and Tidy an Assay Matrix
We assume that the assay data (e.g. transcriptomic data) is either in
an Excel file or flat text file. For example, your data may look like
this:
In this data set, the columns are individual samples. The values in
each row are the -Omic expression measurements for the gene in that
row.
3.1 Import with readr
To import data files in .csv (comma-separated),
.fwf (fixed-width), or .txt (tab-delimited)
format, we recommend the readr package. You
can .csv files with the read_csv function,
fixed-width files with read_fwf, and general delimited
files with read_delim. These functions are all from the
readr package. Additionally, for data in .xls
or .xlsx format, we recommend the readxl package. We
would read a .csv data file via
assay_path <- system.file("extdata", "ex_assay_subset.csv",
package = "pathwayPCA", mustWork = TRUE)
assay_df <- read_csv(assay_path)
#> New names:
#> Rows: 17 Columns: 37
#> ── Column specification
#> ──────────────────────────────────────────────────────── Delimiter: "," chr
#> (1): ...1 dbl (36): T21101311, T21101312, T21101313, T21101314, T21101315,
#> T21101316, ...
#> ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
#> Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> • `` -> `...1`
The read_csv function warns us that the name of the
first column is missing, but then automatically fills it in as
X1. Further, this function prints messages to the screen
informing you of the assumptions it makes when importing your data.
Specifically, this message tells us that all the imported data is
numeric (.default = col_double()) except for the gene name
column (X1 = col_character()).
Let’s inspect our assay data frame. Note that the gene names were
imported as a character column, as shown by the <chr>
tag at the top of the first column. This data import step stored the row
names (the gene names) as the first column, and preserved the column
names (sample labels) of the data.
assay_df
#> # A tibble: 17 × 37
#> ...1 T21101311 T21101312 T21101313 T21101314 T21101315 T21101316 T21101317
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 SOAT1 5.37 5.52 5.89 5.62 5.49 5.58 5.32
#> 2 LSS 9.77 9.78 8.11 8.67 9.83 9.85 10.0
#> 3 SQLE 7.74 8.06 7.00 8.59 8.13 8.55 6.99
#> 4 EBP 4.68 5.12 5.78 5.64 5.73 5.13 5.86
#> 5 CYP51A1 8.27 8.21 8.20 8.07 9.38 9.40 8.08
#> 6 DHCR7 8.32 8.33 8.39 8.64 8.15 8.71 9.25
#> 7 CYP27B1 6.78 6.47 6.57 6.47 6.43 6.56 6.86
#> 8 DHCR24 4.70 5.06 4.89 4.98 5.03 4.87 4.72
#> 9 HSD17B7 7.63 7.63 8.15 8.13 7.70 7.75 7.88
#> 10 MSMO1 7.60 7.33 7.61 5.56 6.30 7.77 6.97
#> 11 FDFT1 10.5 10.6 10.2 10.4 9.99 9.78 10.7
#> 12 SC5DL 9.53 9.35 9.82 9.70 9.50 9.34 9.30
#> 13 LIPA 8.17 6.75 7.36 7.17 6.82 7.82 6.88
#> 14 CEL 10.5 9.85 10.0 9.80 10.6 9.66 10.2
#> 15 TM7SF2 9.83 10.5 9.83 10.0 10.3 9.46 9.76
#> 16 NSDHL 6.88 7.47 7.75 6.16 5.94 6.74 6.32
#> 17 SOAT2 7.79 7.68 8.05 7.79 7.74 7.52 8.04
#> # ℹ 29 more variables: T21101318 <dbl>, T21101319 <dbl>, T21101320 <dbl>,
#> # T21101321 <dbl>, T21101322 <dbl>, T21101323 <dbl>, T21101324 <dbl>,
#> # T21101325 <dbl>, T21101326 <dbl>, T21101327 <dbl>, …
3.2 Tidy the Assay Data Frame
The assay input to the pathwayPCA package must be in tidy
data format. The “Tidy Data” format requires that each
observation be its own row, and each measurement its own column. This
means that we must transpose our assay data frame, while preserving the
row and column names.
To do this, we can use the TransposeAssay function. This
function takes in a data frame as imported by the three
readr functions based on data in a format similar to that
shown above: genes are the rows, gene names are the first column,
samples are stored in the subsequent columns, and all values in the
assay (other than the gene names in the first column) are numeric.
(assayT_df <- TransposeAssay(assay_df))
#> # A tibble: 36 × 18
#> Sample SOAT1 LSS SQLE EBP CYP51A1 DHCR7 CYP27B1 DHCR24 HSD17B7 MSMO1
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 T21101311 5.37 9.77 7.74 4.68 8.27 8.32 6.78 4.70 7.63 7.60
#> 2 T21101312 5.52 9.78 8.06 5.12 8.21 8.33 6.47 5.06 7.63 7.33
#> 3 T21101313 5.89 8.11 7.00 5.78 8.20 8.39 6.57 4.89 8.15 7.61
#> 4 T21101314 5.62 8.67 8.59 5.64 8.07 8.64 6.47 4.98 8.13 5.56
#> 5 T21101315 5.49 9.83 8.13 5.73 9.38 8.15 6.43 5.03 7.70 6.30
#> 6 T21101316 5.58 9.85 8.55 5.13 9.40 8.71 6.56 4.87 7.75 7.77
#> 7 T21101317 5.32 10.0 6.99 5.86 8.08 9.25 6.86 4.72 7.88 6.97
#> 8 T21101318 5.49 9.72 7.47 5.16 6.67 7.37 6.70 4.92 7.50 5.48
#> 9 T21101319 5.57 9.88 7.97 5.40 7.91 8.06 6.58 5.06 8.16 7.06
#> 10 T21101320 5.16 9.87 7.42 5.50 7.43 8.68 6.55 4.85 8.20 6.15
#> # ℹ 26 more rows
#> # ℹ 7 more variables: FDFT1 <dbl>, SC5DL <dbl>, LIPA <dbl>, CEL <dbl>,
#> # TM7SF2 <dbl>, NSDHL <dbl>, SOAT2 <dbl>
This transposed data frame has the gene names as the column names and
the sample names as a column of character (chr) values.
Notice that the data itself is 17 genes measured on 36 samples. Before
transposition, we had 37 columns because the feature names were stored
in the first column. After transposition, we have 36 rows but 18
columns: the first column stores the sample names. This transposed data
frame (after filtering to match the response data) will be supplied to
the assayData_df argument in the CreateOmics
function. (See the Creating
Omics Data Objects vignette for more information on
creating Omics-class objects.)
3.3 Subsetting a Tidy Data Frame
If ever we need to extract individual components of a tidy data
frame, we can use the assay[row, col] syntax. If we need
entire measurements (columns), then we can call the column by name with
the assay$ColName syntax. For example,
- If we need the second row of
assayT_df—corresponding to
Sample “T21101312”—then we type
assayT_df[2, ]
#> # A tibble: 1 × 18
#> Sample SOAT1 LSS SQLE EBP CYP51A1 DHCR7 CYP27B1 DHCR24 HSD17B7 MSMO1
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 T21101312 5.52 9.78 8.06 5.12 8.21 8.33 6.47 5.06 7.63 7.33
#> # ℹ 7 more variables: FDFT1 <dbl>, SC5DL <dbl>, LIPA <dbl>, CEL <dbl>,
#> # TM7SF2 <dbl>, NSDHL <dbl>, SOAT2 <dbl>
Notice that the tibble object has 1 row and 18 columns.
- If we need the third column of assayT_df—corresponding to
Gene “LSS”—then we type
assayT_df[, 3]
#> # A tibble: 36 × 1
#> LSS
#> <dbl>
#> 1 9.77
#> 2 9.78
#> 3 8.11
#> 4 8.67
#> 5 9.83
#> 6 9.85
#> 7 10.0
#> 8 9.72
#> 9 9.88
#> 10 9.87
#> # ℹ 26 more rows
This tibble object has 36 rows and 1 column. - If we
need the intersection of these two (the expression level of Gene “LSS”
in Sample “T21101312”), then we type
assayT_df[2, 3, drop = TRUE]
#> [1] 9.780137
This output would normally be a 1 by 1 tibble (which
isn’t terribly helpful), so we add the drop = TRUE argument
to “drop” the dimensions of the table. This gives us a single basic
number (scalar). - If we need the third column of
assayT_df, but we want the result back as a vector instead
of a tibble, we call the column by name:
assayT_df$LSS
#> [1] 9.772752 9.780137 8.109067 8.667270 9.828498 9.852128 10.004496
#> [8] 9.719076 9.883177 9.871709 7.892335 8.263840 8.215201 9.133894
#> [15] 10.116890 9.107010 9.820948 9.057552 9.268507 9.382549 7.852329
#> [22] 8.585521 8.949471 8.931112 9.597493 9.657331 7.800871 9.858547
#> [29] 9.758955 8.383229 10.562910 9.815668 10.013560 11.094211 9.217539
#> [36] 10.667547
3.4 Data from a SummarizedExperiment Object
Oftentimes, genomic experiment data is stored in a
SummarizedExperiment-class object. If your assay and
response data are stored in such an object, use the
SE2Tidy() function to extract the necessary information and
return it as a tidy data frame. Because
SummarizedExperiment objects can have more than one assay,
you must specify the index for the assay of your choice with the
whichAssay argument. Here is an example using the
airway data:
library(SummarizedExperiment)
data(airway, package = "airway")
airway_df <- SE2Tidy(airway)
Now we can look at a nice summary of the tidied assay and response
data. This will drop all of the gene-specific metadata, as well as any
experiment metadata. However, pathwayPCA can’t make use of
this data anyway, so we haven’t lost much.
airway_df[, 1:20]
#> Row.names SampleName cell dex albut Run avgLength Experiment
#> 1 SRR1039508 GSM1275862 N61311 untrt untrt SRR1039508 126 SRX384345
#> 2 SRR1039509 GSM1275863 N61311 trt untrt SRR1039509 126 SRX384346
#> 3 SRR1039512 GSM1275866 N052611 untrt untrt SRR1039512 126 SRX384349
#> 4 SRR1039513 GSM1275867 N052611 trt untrt SRR1039513 87 SRX384350
#> 5 SRR1039516 GSM1275870 N080611 untrt untrt SRR1039516 120 SRX384353
#> 6 SRR1039517 GSM1275871 N080611 trt untrt SRR1039517 126 SRX384354
#> 7 SRR1039520 GSM1275874 N061011 untrt untrt SRR1039520 101 SRX384357
#> 8 SRR1039521 GSM1275875 N061011 trt untrt SRR1039521 98 SRX384358
#> Sample BioSample ENSG00000000003 ENSG00000000005 ENSG00000000419
#> 1 SRS508568 SAMN02422669 679 0 467
#> 2 SRS508567 SAMN02422675 448 0 515
#> 3 SRS508571 SAMN02422678 873 0 621
#> 4 SRS508572 SAMN02422670 408 0 365
#> 5 SRS508575 SAMN02422682 1138 0 587
#> 6 SRS508576 SAMN02422673 1047 0 799
#> 7 SRS508579 SAMN02422683 770 0 417
#> 8 SRS508580 SAMN02422677 572 0 508
#> ENSG00000000457 ENSG00000000460 ENSG00000000938 ENSG00000000971
#> 1 260 60 0 3251
#> 2 211 55 0 3679
#> 3 263 40 2 6177
#> 4 164 35 0 4252
#> 5 245 78 1 6721
#> 6 331 63 0 11027
#> 7 233 76 0 5176
#> 8 229 60 0 7995
#> ENSG00000001036 ENSG00000001084 ENSG00000001167
#> 1 1433 519 394
#> 2 1062 380 236
#> 3 1733 595 464
#> 4 881 493 175
#> 5 1424 820 658
#> 6 1439 714 584
#> 7 1359 696 360
#> 8 1109 704 269
4. Import and Join Response Data
We now have an appropriate pathways list and a tidy -Omics assay data
frame. All we need now is some response data. Let’s imagine that your
phenotype data looks something like this:
We next import this response information. We can use the
read_csv function once again:
pInfo_path <- system.file("extdata", "ex_pInfo_subset.csv",
package = "pathwayPCA", mustWork = TRUE)
pInfo_df <- read_csv(pInfo_path)
#> Rows: 36 Columns: 3
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (1): Sample
#> dbl (1): eventTime
#> lgl (1): eventObserved
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
This phenotype data frame has a column for the sample labels
(Sample) and the response information. In this case, our
response is a survival response with an event time and observation
indicator.
pInfo_df
#> # A tibble: 36 × 3
#> Sample eventTime eventObserved
#> <chr> <dbl> <lgl>
#> 1 T21101311 14.2 TRUE
#> 2 T21101312 1 TRUE
#> 3 T21101313 6.75 FALSE
#> 4 T21101314 8.5 TRUE
#> 5 T21101315 7.25 FALSE
#> 6 T21101316 5 TRUE
#> 7 T21101317 20 TRUE
#> 8 T21101318 13.2 FALSE
#> 9 T21101319 7.75 FALSE
#> 10 T21101320 9 FALSE
#> # ℹ 26 more rows
This pInfo data frame has the sample names as a column
of character values, just like the transposed assay data frame. This is
crucially important for the “joining” step. We can use the
inner_join function from the dplyr library to
retain only the rows of the assayT_df data frame which have
responses in the pInfo data frame and vice versa. This way,
every response in the phenotype data has matching genes in the assay,
and every recorded gene in the assay matches a response in the phenotype
data.
joinedExperiment_df <- inner_join(pInfo_df, assayT_df, by = "Sample")
joinedExperiment_df
#> # A tibble: 36 × 20
#> Sample eventTime eventObserved SOAT1 LSS SQLE EBP CYP51A1 DHCR7 CYP27B1
#> <chr> <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 T21101… 14.2 TRUE 5.37 9.77 7.74 4.68 8.27 8.32 6.78
#> 2 T21101… 1 TRUE 5.52 9.78 8.06 5.12 8.21 8.33 6.47
#> 3 T21101… 6.75 FALSE 5.89 8.11 7.00 5.78 8.20 8.39 6.57
#> 4 T21101… 8.5 TRUE 5.62 8.67 8.59 5.64 8.07 8.64 6.47
#> 5 T21101… 7.25 FALSE 5.49 9.83 8.13 5.73 9.38 8.15 6.43
#> 6 T21101… 5 TRUE 5.58 9.85 8.55 5.13 9.40 8.71 6.56
#> 7 T21101… 20 TRUE 5.32 10.0 6.99 5.86 8.08 9.25 6.86
#> 8 T21101… 13.2 FALSE 5.49 9.72 7.47 5.16 6.67 7.37 6.70
#> 9 T21101… 7.75 FALSE 5.57 9.88 7.97 5.40 7.91 8.06 6.58
#> 10 T21101… 9 FALSE 5.16 9.87 7.42 5.50 7.43 8.68 6.55
#> # ℹ 26 more rows
#> # ℹ 10 more variables: DHCR24 <dbl>, HSD17B7 <dbl>, MSMO1 <dbl>, FDFT1 <dbl>,
#> # SC5DL <dbl>, LIPA <dbl>, CEL <dbl>, TM7SF2 <dbl>, NSDHL <dbl>, SOAT2 <dbl>
This requires you to have a key column in both data
frames with the same name. If the key column was called
“Sample” in the pInfo_df data set but “SampleID” in the
assay, then the by argument should be changed to
by = c("Sample" = "SampleID"). It’s much nicer to just keep
them with the same names, however. Moreover, it is vitally important
that you check your sample IDs. Obviously the recorded genetic data
should pair with the phenotype information, but it is your
responsibility as the user to confirm that the assay rows match the
correct responses. You are ultimately responsible to defend the
integrity of your data and to use this package properly.
5. Example Tidy Assay and Pathways List
Included in this package, we have a small tidy assay and
corresponding gene subset list. We will load and inspect this assay.
This data set has 656 gene expression measurements on 250 colon cancer
patients. Further notice that the assay and overall survival response
information have already been matched.
data("colonSurv_df")
colonSurv_df
#> # A tibble: 250 × 659
#> sampleID OS_time OS_event JUN SOS2 PAK3 RAF1 PRKCB BTC SHC1 PRKCA
#> <chr> <dbl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 subj1 64.9 0 9.29 5.48 8.21 8.03 5.49 6.65 8.26 8.94
#> 2 subj2 59.8 0 9.13 6.35 8.33 7.94 6.26 7.02 8.39 9.61
#> 3 subj3 62.4 0 9.37 5.67 7.82 7.74 6.05 7.52 8.69 8.40
#> 4 subj4 54.5 0 10.6 4.94 8.79 7.64 5.37 6.87 7.81 9.80
#> 5 subj5 46.3 1 8.70 5.60 8.75 8.05 6.07 6.49 8.45 8.21
#> 6 subj6 55.9 0 9.78 5.36 7.56 8.07 5.90 6.39 8.87 8.22
#> 7 subj7 58.0 0 9.22 5.05 8.20 7.80 5.55 6.86 8.28 8.97
#> 8 subj8 54.0 0 10.3 5.33 7.82 7.89 6.27 6.25 8.66 9.71
#> 9 subj9 0.427 1 10.8 5.07 7.63 7.69 5.48 7.57 8.36 9.69
#> 10 subj10 41.4 0 9.52 5.50 7.48 7.53 5.71 7.33 8.54 8.14
#> # ℹ 240 more rows
#> # ℹ 648 more variables: ELK1 <dbl>, NRG1 <dbl>, PAK2 <dbl>, MTOR <dbl>,
#> # PAK4 <dbl>, MAP2K4 <dbl>, EIF4EBP1 <dbl>, BAD <dbl>, PRKCG <dbl>,
#> # NRG3 <dbl>, …
We also have a small list of 15 pathways which correspond to our
example colon cancer assay. To create a toy example, we have curated
this artificial pathways list to include seven significant pathways and
eight non-significant pathways.
data("colon_pathwayCollection")
colon_pathwayCollection
#> Object with Class(es) 'pathwayCollection', 'list' [package 'pathwayPCA'] with 2 elements:
#> $ pathways:List of 15
#> $ TERMS : chr [1:15] "KEGG_PENTOSE_PHOSPHATE_PATHWAY" ...
The pathways list and tidy assay (with matched phenotype information)
are all the information we need to create an Omics-class
data object.
6. Review
We now summarize our steps so far. We have
- Imported a
.gmt file and saved the pathways stored
therein as a pathwayCollection object using the
read_gmt function.
- Imported an assay
.csv file with the
read_csv function from the readr package, and
transposed this data frame into “tidy” form with the
TransposeAssay function.
- Imported a phenotype information
.csv file, and joined
it to the assay data frame with the inner_join function
from the dplyr package.
Now we are prepared to create our first Omics-class
object for analysis with either AES-PCA or Supervised PCA. Please read
vignette chapter 3: Creating
Omics Data Objects.
Here is the R session information for this vignette:
sessionInfo()
#> R Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [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
#>
#> time zone: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 parallel stats graphics grDevices utils datasets
#> [8] methods base
#>
#> other attached packages:
#> [1] SummarizedExperiment_1.37.0 Biobase_2.67.0
#> [3] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
#> [5] IRanges_2.41.0 S4Vectors_0.45.0
#> [7] BiocGenerics_0.53.0 MatrixGenerics_1.19.0
#> [9] matrixStats_1.4.1 survminer_0.4.9
#> [11] ggpubr_0.6.0 survival_3.7-0
#> [13] pathwayPCA_1.23.0 lubridate_1.9.3
#> [15] forcats_1.0.0 stringr_1.5.1
#> [17] dplyr_1.1.4 purrr_1.0.2
#> [19] readr_2.1.5 tidyr_1.3.1
#> [21] tibble_3.2.1 ggplot2_3.5.1
#> [23] tidyverse_2.0.0
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#> loaded via a namespace (and not attached):
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#> [19] bit_4.5.0 DelayedArray_0.33.0 abind_1.4-8
#> [22] withr_3.0.2 grid_4.5.0 fansi_1.0.6
#> [25] lars_1.3 xtable_1.8-4 colorspace_2.1-1
#> [28] scales_1.3.0 cli_3.6.3 rmarkdown_2.28
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#> [58] htmltools_0.5.8.1 GenomeInfoDbData_1.2.13 R6_2.5.1
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#> [70] gridExtra_2.3 nlme_3.1-166 mgcv_1.9-1
#> [73] xfun_0.48 zoo_1.8-12 pkgconfig_2.0.3