Milestone 4 test
Ashley E Noriega,
Nov 20, 2019
TRGN 510 Final Project: Milestone 2
Loading in RNA seq colon cancer data from TCGA
RNASeq files for cystic, mucinous, and serous neoplasms and adenocarcinoma
I created a new folder called COAD and stored all 60 files in it. I then changed them to TXT files and opened them.
Data Packaging
The urls for the 30 files I downloaded locally for cystic, mucinous and serous colon cancer (CMS)
https://portal.gdc.cancer.gov/files/536f5a77-0087-457d-ac95-6d1a9abad8cb, UUID 536f5a77-0087-457d-ac95-6d1a9abad8cb, case: TCGA-AA-3516
https://portal.gdc.cancer.gov/files/ed52de66-66fa-44ce-b679-cf641b0d92cd, UUID ed52de66-66fa-44ce-b679-cf641b0d92cd, case: TCGA-AA-3516
https://portal.gdc.cancer.gov/files/b28090c5-c42d-4836-9bb1-ce906d3ead95, UUID: b28090c5-c42d-4836-9bb1-ce906d3ead95, case TCGA-AA-3854
https://portal.gdc.cancer.gov/cases/57cdaa1c-4e94-4a28-ab3b-300c0457555f, UUID: 49e29c69-d9d7-4496-9f24-26f42c8b6d8e, case: TCGA-A6-2674
https://portal.gdc.cancer.gov/files/08ed32e4-fb94-4bc0-8715-83ee2143a13d, UUID: 08ed32e4-fb94-4bc0-8715-83ee2143a13d, case: TCGA-AA-A00J
https://portal.gdc.cancer.gov/files/6e571f71-d5fb-42f3-a35b-554c5ab76587, UUID: 6e571f71-d5fb-42f3-a35b-554c5ab76587, case: TCGA-AA-A01G
https://portal.gdc.cancer.gov/files/8b12a000-f588-4a78-a9eb-f06041a65789, UUID: 8b12a000-f588-4a78-a9eb-f06041a65789, case: TCGA-A6-6780
https://portal.gdc.cancer.gov/files/02734d4d-fc8f-4ef7-ac82-1b4d7184cc5e, UUID: 02734d4d-fc8f-4ef7-ac82-1b4d7184cc5e, case: TCGA-CK-4950
https://portal.gdc.cancer.gov/files/6466a8b1-d1e2-4195-a353-0800576c13c8, UUID: 6466a8b1-d1e2-4195-a353-0800576c13c8, case: TCGA-G4-6322
https://portal.gdc.cancer.gov/files/bc47f01c-1994-4ff8-a356-94d9679b66ee, UUID: bc47f01c-1994-4ff8-a356-94d9679b66ee, case: TCGA-AA-3947
https://portal.gdc.cancer.gov/files/b045ee79-82a6-4636-a875-1a58603d89ff, UUID: b045ee79-82a6-4636-a875-1a58603d89ff, case: TCGA-A6-A566
https://portal.gdc.cancer.gov/files/c383ba2c-b00a-4bd2-82cb-b3f04c2a8172, UUID: c383ba2c-b00a-4bd2-82cb-b3f04c2a8172, case: TCGA-AA-3877
https://portal.gdc.cancer.gov/files/b52775aa-273e-484e-82c7-c625f09415fa, UUID: b52775aa-273e-484e-82c7-c625f09415fa, case: TCGA-A6-3809
https://portal.gdc.cancer.gov/files/7b15a87a-805c-4b8a-84de-549cec9c44e3, UUID: 7b15a87a-805c-4b8a-84de-549cec9c44e3, case: TCGA-AA-3684
https://portal.gdc.cancer.gov/files/b4f3dbbb-2686-4896-9e60-5bef6c9150b4, UUID: b4f3dbbb-2686-4896-9e60-5bef6c9150b4, case: TCGA-AA-3692
https://portal.gdc.cancer.gov/files/0b16e2bd-3ec7-4901-9ff0-a389670e5019, UUID: 0b16e2bd-3ec7-4901-9ff0-a389670e5019, case: TCGA-D5-6534
https://portal.gdc.cancer.gov/files/a6690007-f347-49c3-a0ba-28e01d131971, UUID: a6690007-f347-49c3-a0ba-28e01d131971, case: TCGA-A6-3809
https://portal.gdc.cancer.gov/files/a1742cf6-c3c5-43e7-879c-489494460e78, UUID: a1742cf6-c3c5-43e7-879c-489494460e78, case: TCGA-AA-A00N
https://portal.gdc.cancer.gov/files/d5be795d-beb6-4def-bda8-f485ee45bfc1, UUID: d5be795d-beb6-4def-bda8-f485ee45bfc1, case: TCGA-A6-2674
https://portal.gdc.cancer.gov/files/46306072-c59c-4b4b-963c-9c4e778ff34b, UUID: 46306072-c59c-4b4b-963c-9c4e778ff34b, case: TCGA-A6-6780
https://portal.gdc.cancer.gov/files/a938cb2c-c8e8-4395-915b-37e1e279a4da, UUID: a938cb2c-c8e8-4395-915b-37e1e279a4da, case: TCGA-G4-6302
https://portal.gdc.cancer.gov/files/7fec7c90-fd2e-4ee2-ba1a-77f85920771f, UUID: 7fec7c90-fd2e-4ee2-ba1a-77f85920771f, case: TCGA-DM-A282
https://portal.gdc.cancer.gov/files/2c3fd34c-70d1-4331-9628-260b77329b53, UUID: 2c3fd34c-70d1-4331-9628-260b77329b53, case: TCGA-F4-6704
https://portal.gdc.cancer.gov/files/4168a720-521e-47ff-afb5-4abe3e815490, UUID: 4168a720-521e-47ff-afb5-4abe3e815490, case: TCGA-AA-3950
https://portal.gdc.cancer.gov/files/ecc90bd1-f594-41ea-ba4b-d42f4c64880b, UUID: ecc90bd1-f594-41ea-ba4b-d42f4c64880b, case: TCGA-A6-6781
https://portal.gdc.cancer.gov/files/8736ed27-2141-48d9-b677-b1a0e14d4b50, UUID: 8736ed27-2141-48d9-b677-b1a0e14d4b50, case: TCGA-CA-6717
https://portal.gdc.cancer.gov/files/3b8d04cd-d658-46ba-adca-079fee531e17, UUID: 3b8d04cd-d658-46ba-adca-079fee531e17, case: TCGA-AA-3821
https://portal.gdc.cancer.gov/files/b27da518-d023-4f9c-a9ab-5cd68ee37870, UUID: b27da518-d023-4f9c-a9ab-5cd68ee37870, case: TCGA-CK-4951
https://portal.gdc.cancer.gov/files/e7005df6-f78b-4e47-abe7-61ae6a2ee026, UUID: e7005df6-f78b-4e47-abe7-61ae6a2ee026, case: TCGA-AA-A01R
https://portal.gdc.cancer.gov/files/e3598d14-292c-41cc-9b59-4497fa078272, UUID: e3598d14-292c-41cc-9b59-4497fa078272, case: TCGA-D5-6930
The urls for the 30 files adenocarcinoma I downloaded locally for adenocarcinoma
https://portal.gdc.cancer.gov/files/f1185347-ad15-43ae-9ef3-d5343b31a0fc, UUID: f1185347-ad15-43ae-9ef3-d5343b31a0fc, case: TCGA-A6-6654
https://portal.gdc.cancer.gov/files/0d53cb1c-97c4-4088-9e43-029de88fd66d, UUID: 0d53cb1c-97c4-4088-9e43-029de88fd66d, case: TCGA-DM-A1D4
https://portal.gdc.cancer.gov/files/a74bbce0-7f3d-434e-b294-7fa45e5b3a60, UUID: a74bbce0-7f3d-434e-b294-7fa45e5b3a60, case: TCGA-A6-2684
https://portal.gdc.cancer.gov/files/47554e4e-cd13-4b92-80be-e1940f9a950f, UUID: 47554e4e-cd13-4b92-80be-e1940f9a950f, case: TCGA-A6-5657
https://portal.gdc.cancer.gov/files/de60dbd7-8a93-47a5-b1ea-a3f95beade8a, UUID: de60dbd7-8a93-47a5-b1ea-a3f95beade8a, case: TCGA-F4-6854
https://portal.gdc.cancer.gov/files/70883b31-d130-4efd-a7c6-169c8d4a253d, UUID: 70883b31-d130-4efd-a7c6-169c8d4a253d, case: TCGA-AD-A5EJ
https://portal.gdc.cancer.gov/files/042bda3d-77aa-4522-8a97-c121711a760e, UUID: 042bda3d-77aa-4522-8a97-c121711a760e, case: TCGA-AG-3582
https://portal.gdc.cancer.gov/files/b6388e09-7ed5-4041-97bb-4427ba5571ba, UUID: b6388e09-7ed5-4041-97bb-4427ba5571ba, case: TCGA-AY-6197
https://portal.gdc.cancer.gov/files/54394c0b-6ae3-4b48-8e89-350ad5349611, UUID: 54394c0b-6ae3-4b48-8e89-350ad5349611, case: TCGA-AA-3554
https://portal.gdc.cancer.gov/files/f7e21d61-19b6-4e99-887f-463d4419628c, UUID: f7e21d61-19b6-4e99-887f-463d4419628c, case: TCGA-AG-4015
https://portal.gdc.cancer.gov/files/b4114885-38cd-4e8a-874b-b78da8d95e2c, UUID: b4114885-38cd-4e8a-874b-b78da8d95e2c, case: TCGA-CM-6171
https://portal.gdc.cancer.gov/files/f9fda40d-67e4-4cb9-859c-ddc2ea84b7e4, UUID: f9fda40d-67e4-4cb9-859c-ddc2ea84b7e4, case: TCGA-CM-6170
https://portal.gdc.cancer.gov/files/b4aebb2a-d0b8-43d8-bd1f-78af2065d8f9, UUID: b4aebb2a-d0b8-43d8-bd1f-78af2065d8f9, case: TCGA-AA-3846
https://portal.gdc.cancer.gov/files/6a750710-5ed9-4d24-b2bf-3a4e3211878f, UUID: 6a750710-5ed9-4d24-b2bf-3a4e3211878f, case: TCGA-CM-6677
https://portal.gdc.cancer.gov/files/93d1a78f-423e-4560-b4d3-ee4a89ac922b, UUID: 93d1a78f-423e-4560-b4d3-ee4a89ac922b, case: TCGA-RU-A8FL
https://portal.gdc.cancer.gov/files/2e632fd9-fa17-4290-9601-a5d462cf152c, UUID: 2e632fd9-fa17-4290-9601-a5d462cf152c, case: TCGA-AZ-4323
https://portal.gdc.cancer.gov/files/7239b026-2587-489d-81fe-7bc657b7523c, UUID: 7239b026-2587-489d-81fe-7bc657b7523c, case: TCGA-CM-6164
https://portal.gdc.cancer.gov/files/90e86a26-fffa-4c38-b2e0-bf0704ee3615, UUID: 90e86a26-fffa-4c38-b2e0-bf0704ee3615, case: TCGA-AZ-4315
https://portal.gdc.cancer.gov/files/9ff11fe0-037c-405e-95c3-dc4a15413db8, UUID: 9ff11fe0-037c-405e-95c3-dc4a15413db8, case: TCGA-G4-6311
https://portal.gdc.cancer.gov/files/b8eed826-6051-4358-9b3d-44d1553dd9ad, UUID: b8eed826-6051-4358-9b3d-44d1553dd9ad, case: TCGA-AA-3522
https://portal.gdc.cancer.gov/files/c172bc07-d4f0-41be-a558-49abc81065c2, UUID: c172bc07-d4f0-41be-a558-49abc81065c2, case: TCGA-AA-3667
https://portal.gdc.cancer.gov/files/260edc5e-1ca6-4b07-b96d-59594d03ac54, UUID: 260edc5e-1ca6-4b07-b96d-59594d03ac54, case: TCGA-AA-A00U
https://portal.gdc.cancer.gov/files/0c5c1a38-7e9c-4b43-810d-0761c3af49b1, UUID: 0c5c1a38-7e9c-4b43-810d-0761c3af49b1, case: TCGA-AA-3506
https://portal.gdc.cancer.gov/files/7024ba0c-be56-4907-9254-cdb2579e536e, UUID: 7024ba0c-be56-4907-9254-cdb2579e536e, case: TCGA-NH-A8F7
https://portal.gdc.cancer.gov/files/031cf2a5-74e0-4b5f-98bd-da60628c0854, UUID: 031cf2a5-74e0-4b5f-98bd-da60628c0854, case: TCGA-AA-3680
https://portal.gdc.cancer.gov/files/91991ecf-cc54-4110-8a4e-9236bf8aa072, UUID: 91991ecf-cc54-4110-8a4e-9236bf8aa072, case: TCGA-A6-4105
https://portal.gdc.cancer.gov/files/47aceec1-a01d-419f-9689-c46284c79bcb, UUID: 47aceec1-a01d-419f-9689-c46284c79bcb, case: TCGA-D5-6922
https://portal.gdc.cancer.gov/files/ce84c955-63db-473a-a6d7-0e3daad6efd4, UUID: ce84c955-63db-473a-a6d7-0e3daad6efd4, case: TCGA-AA-3524
https://portal.gdc.cancer.gov/files/a071fc45-61ea-4815-93bf-be34980e59ee, UUID: a071fc45-61ea-4815-93bf-be34980e59ee, case: TCGA-AA-3855
https://portal.gdc.cancer.gov/files/8b275144-b885-4fb0-af39-fea1e48a970a, UUID: 8b275144-b885-4fb0-af39-fea1e48a970a, case: TCGA-AA-A00Q
Load in count data
First rename the “.count” files to “.txt” and unzip each one by opening each file.
setwd('~/Desktop/COAD_Data/')The working directory was changed to /Users/ashleynoriega/Desktop/COAD_Data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.COAD_files <- c("9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq.txt", "bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq.txt",
"5697212f-b3fd-479f-84b0-ec0aae54534a.htseq.txt", "7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq.txt",
"15864159-be88-41c8-bdef-c2c5927cb1a1.htseq.txt", "649b19e1-96e2-4b55-951d-3b6ee9f4b91f.htseq.txt",
"86679663-dfc5-46ad-8cf9-c7954c4b339b.htseq.txt", "28004569-048d-4f8c-99aa-7a8c69a98dcc.htseq.txt", "911f6378-8a25-4570-9d3b-80f5b5bfc085.htseq.txt", "d5dca54e-d7e9-4328-b2ca-1d191a2b8b4c.htseq.txt", "f3895ae4-1228-49b3-9342-3c3b86cb5243.htseq.txt", "f590941d-19dc-427a-95b6-942c97ea8333.htseq.txt", "55aa6d16-3598-42ca-8844-0fe84739ef66.htseq.txt", "0e7094cf-4c79-43f4-8b72-9de259e5e18f.htseq.txt", "9a62fe1f-36ec-4e8e-b3d9-bdfc62f71905.htseq.txt", "d2587070-cb7d-440d-ae49-52f5077248e6.htseq.txt", "7800bdb2-aa8b-43e0-8e45-1b968872b34e.htseq.txt", "2bcd2efd-4fd6-40ee-86a4-867ae82711b0.htseq.txt", "424d8e5f-9fc6-470b-ad2c-b4447b0eb07e.htseq.txt", "934f9dc6-1260-4268-b022-870f1e37dd6f.htseq.txt", "0fa55c0e-6f8f-44a6-82fe-9a42495d3484.htseq.txt", "c8544a8a-4352-438d-94d4-3495af2e9a78.htseq.txt", "dade0b16-ecc3-43b3-b328-3819a8fc18c6.htseq.txt", "e875ae4e-4645-4e84-b0ff-9c9a694717a9.htseq.txt", "debd6982-7c27-42e8-b778-20afcc78a5f3.htseq.txt", "17c88994-9e8e-4f16-8c41-34e98a0d8c52.htseq.txt", "7f5a924a-ddf3-45ff-be1f-5b5909305f46.htseq.txt", "fa73bdce-67fb-42aa-883f-635f0e7bcdc6.htseq.txt", "abe20df7-6b97-4397-8864-881bac27e92c.htseq.txt", "62f84581-4c7d-4c8e-835c-9304bcec3106.htseq.txt", "3abbd2b5-04db-4fe0-8dd1-ea2b48caa4c1.htseq.txt", "087666cd-47ae-4f56-b947-d6aa1c25e8a7.htseq.txt",
"c14f98e2-8e9b-49f4-a244-3d06c6cb7126.htseq.txt", "13abc91e-fbfc-4c55-bf54-fbd134979ccc.htseq.txt",
"6ae2dd6c-2a39-411f-a1fc-11e0e6e82165.htseq.txt", "8f77f4f4-b184-40c7-8ab8-2f95b13620b5.htseq.txt",
"168e5cb2-7390-45ad-ad04-c9aa4416e950.htseq.txt", "0ed65bdf-cb92-47c1-8aeb-42518ce639b8.htseq.txt", "4e7c6811-88e4-4bb7-a88f-7491dfa6d072.htseq.txt", "7fb73a84-867a-4c28-aa02-93068efffb7b.htseq.txt", "b53f9a9d-b24d-410d-b3e9-f2a8bf22ca27.htseq.txt", "f7ce175f-763e-4a55-97e3-0381d889b0eb.htseq.txt", "f346f2d2-285c-455c-ba34-ea8eec3fa881.htseq.txt", "e53e1a83-1979-4e12-bbb7-79b37d0cfe03.htseq.txt", "a26d49db-2309-46a0-a3ed-275378d484e7.htseq.txt", "a3f88a5d-7169-465b-bb80-e5999590681c.htseq.txt", "c264fe3b-482b-44ec-83a4-73df565663ff.htseq.txt", "bd2dfab3-88a8-4673-ba36-3daf252d0b4d.htseq.txt", "7261b656-c79c-4581-a503-15b653e2b5d2.htseq.txt", "ee4dcccc-514b-4cc6-ae63-6ed3e7519a40.htseq.txt", "f596eabc-e39a-4e35-9fc6-edade04eb785.htseq.txt", "bf9c448b-bdc9-4f74-b13a-374e6add7939.htseq.txt", "564daa81-cfef-45b6-94a0-3249b2724d9b.htseq.txt", "82e00e45-734c-471f-ba97-79ec3b7e0baa.htseq.txt", "9c52ed00-325f-4664-8873-327bcaa5ea74.htseq.txt", "fabefb10-5546-4017-8ea1-29982a10fb3c.htseq.txt", "32a115cf-570f-4ad9-a123-8e1970062f51.htseq.txt", "05eef9f8-a246-403a-b0be-07d274b6f93a.htseq.txt", "5c18c6a8-9ad2-43a8-a3a0-83d8fc0cc257.htseq.txt", "43b292be-5d63-4523-a43f-666d20039208.htseq.txt")
read.delim(COAD_files[1], nrows = 60)Create dataset, join the 60 loaded txt files
Use edgeR to create a matrix of 60 text files.
Known issue: Working directory
Spoke to professor Craig on 12/4 and it is ok to not change the root, just setwd to desktop as my desktop since files were downloaded locally.
setwd('~/Desktop/COAD_Data/')The working directory was changed to /Users/ashleynoriega/Desktop/COAD_Data inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.library(edgeR)
x <- readDGE(COAD_files, columns=c(1,2)) #joins my 60 files and creates a datasetMeta tags detected: __no_feature, __ambiguous, __too_low_aQual, __not_aligned, __alignment_not_uniqueclass(x)[1] "DGEList"
attr(,"package")
[1] "edgeR"dim(x)[1] 60487 60names(x) #accessor function [1] "samples" "counts" str(x) #displays the structure of x in compact way, alternative to summary and best for displaying contents of listsFormal class 'DGEList' [package "edgeR"] with 1 slot
..@ .Data:List of 2
.. ..$ :'data.frame': 60 obs. of 4 variables:
.. .. ..$ files : chr [1:60] "9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq.txt" "bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq.txt" "5697212f-b3fd-479f-84b0-ec0aae54534a.htseq.txt" "7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq.txt" ...
.. .. ..$ group : Factor w/ 1 level "1": 1 1 1 1 1 1 1 1 1 1 ...
.. .. ..$ lib.size : num [1:60] 9.02e+07 3.68e+07 4.30e+07 1.12e+08 3.63e+07 ...
.. .. ..$ norm.factors: num [1:60] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ : num [1:60487, 1:60] 47 1212 1176 121 166 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ Tags : chr [1:60487] "ENSG00000000005.5" "ENSG00000000419.11" "ENSG00000000457.12" "ENSG00000000460.15" ...
.. .. .. ..$ Samples: chr [1:60] "9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq" "bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq" "5697212f-b3fd-479f-84b0-ec0aae54534a.htseq" "7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq" ...Annotate the samples
x$samplesOrganize sample information
Associate sample-level information with the columns of the counts matrix
samplenames <- substring(colnames(x), 1, nchar(colnames(x)))
samplenames [1] "9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq" "bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq"
[3] "5697212f-b3fd-479f-84b0-ec0aae54534a.htseq" "7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq"
[5] "15864159-be88-41c8-bdef-c2c5927cb1a1.htseq" "649b19e1-96e2-4b55-951d-3b6ee9f4b91f.htseq"
[7] "86679663-dfc5-46ad-8cf9-c7954c4b339b.htseq" "28004569-048d-4f8c-99aa-7a8c69a98dcc.htseq"
[9] "911f6378-8a25-4570-9d3b-80f5b5bfc085.htseq" "d5dca54e-d7e9-4328-b2ca-1d191a2b8b4c.htseq"
[11] "f3895ae4-1228-49b3-9342-3c3b86cb5243.htseq" "f590941d-19dc-427a-95b6-942c97ea8333.htseq"
[13] "55aa6d16-3598-42ca-8844-0fe84739ef66.htseq" "0e7094cf-4c79-43f4-8b72-9de259e5e18f.htseq"
[15] "9a62fe1f-36ec-4e8e-b3d9-bdfc62f71905.htseq" "d2587070-cb7d-440d-ae49-52f5077248e6.htseq"
[17] "7800bdb2-aa8b-43e0-8e45-1b968872b34e.htseq" "2bcd2efd-4fd6-40ee-86a4-867ae82711b0.htseq"
[19] "424d8e5f-9fc6-470b-ad2c-b4447b0eb07e.htseq" "934f9dc6-1260-4268-b022-870f1e37dd6f.htseq"
[21] "0fa55c0e-6f8f-44a6-82fe-9a42495d3484.htseq" "c8544a8a-4352-438d-94d4-3495af2e9a78.htseq"
[23] "dade0b16-ecc3-43b3-b328-3819a8fc18c6.htseq" "e875ae4e-4645-4e84-b0ff-9c9a694717a9.htseq"
[25] "debd6982-7c27-42e8-b778-20afcc78a5f3.htseq" "17c88994-9e8e-4f16-8c41-34e98a0d8c52.htseq"
[27] "7f5a924a-ddf3-45ff-be1f-5b5909305f46.htseq" "fa73bdce-67fb-42aa-883f-635f0e7bcdc6.htseq"
[29] "abe20df7-6b97-4397-8864-881bac27e92c.htseq" "62f84581-4c7d-4c8e-835c-9304bcec3106.htseq"
[31] "3abbd2b5-04db-4fe0-8dd1-ea2b48caa4c1.htseq" "087666cd-47ae-4f56-b947-d6aa1c25e8a7.htseq"
[33] "c14f98e2-8e9b-49f4-a244-3d06c6cb7126.htseq" "13abc91e-fbfc-4c55-bf54-fbd134979ccc.htseq"
[35] "6ae2dd6c-2a39-411f-a1fc-11e0e6e82165.htseq" "8f77f4f4-b184-40c7-8ab8-2f95b13620b5.htseq"
[37] "168e5cb2-7390-45ad-ad04-c9aa4416e950.htseq" "0ed65bdf-cb92-47c1-8aeb-42518ce639b8.htseq"
[39] "4e7c6811-88e4-4bb7-a88f-7491dfa6d072.htseq" "7fb73a84-867a-4c28-aa02-93068efffb7b.htseq"
[41] "b53f9a9d-b24d-410d-b3e9-f2a8bf22ca27.htseq" "f7ce175f-763e-4a55-97e3-0381d889b0eb.htseq"
[43] "f346f2d2-285c-455c-ba34-ea8eec3fa881.htseq" "e53e1a83-1979-4e12-bbb7-79b37d0cfe03.htseq"
[45] "a26d49db-2309-46a0-a3ed-275378d484e7.htseq" "a3f88a5d-7169-465b-bb80-e5999590681c.htseq"
[47] "c264fe3b-482b-44ec-83a4-73df565663ff.htseq" "bd2dfab3-88a8-4673-ba36-3daf252d0b4d.htseq"
[49] "7261b656-c79c-4581-a503-15b653e2b5d2.htseq" "ee4dcccc-514b-4cc6-ae63-6ed3e7519a40.htseq"
[51] "f596eabc-e39a-4e35-9fc6-edade04eb785.htseq" "bf9c448b-bdc9-4f74-b13a-374e6add7939.htseq"
[53] "564daa81-cfef-45b6-94a0-3249b2724d9b.htseq" "82e00e45-734c-471f-ba97-79ec3b7e0baa.htseq"
[55] "9c52ed00-325f-4664-8873-327bcaa5ea74.htseq" "fabefb10-5546-4017-8ea1-29982a10fb3c.htseq"
[57] "32a115cf-570f-4ad9-a123-8e1970062f51.htseq" "05eef9f8-a246-403a-b0be-07d274b6f93a.htseq"
[59] "5c18c6a8-9ad2-43a8-a3a0-83d8fc0cc257.htseq" "43b292be-5d63-4523-a43f-666d20039208.htseq"Specify which files are Cystic, Mucinous, and Serous (CMS) and which files are Adenocarcinoma
colnames(x) <- samplenames
group <- as.factor(c("CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
"CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
"CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
"CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
"CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
"ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
"ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
"ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
"ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
"ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
"ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
"ADENOCARCINOMA", "ADENOCARCINOMA"))
x$samples$group <- group
x$samplesDF<-x$samples #for my own visualization purposesScript to organize gene annotations
{ if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”)
BiocManager::install(“Homo.sapiens”) library(Homo.sapiens) install.packages(gsubfn) library(gsubfn) }
Script to annotate Genes
First install Homo.sapiens, then use a script remove the decimals and numbers after the decimal points in all 60487 ENSEMBL geneid elements.
library(Homo.sapiens)
#library(stringr)
library(gsubfn)
geneid <- rownames(x)
#geneid_test <- c("ENSG00000000005",
# "ENSG00000000419",
# "ENSG00000000457",
# "ENSG00000000938")
#geneid <- str_remove(geneid, "[.]") removes decimals only
geneid <- gsub("\\.[0-9]*$", "", geneid) #remove decimals and numbers after decimals
genes <- select(Homo.sapiens, keys=geneid, columns=c("SYMBOL", "TXCHROM"),
keytype="ENSEMBL")'select()' returned 1:many mapping between keys and columnshead(genes)Remove duplicate genes
genes <- genes[!duplicated(genes$ENSEMBL),]Package in a DGEList-object containing raw count data with associated sample information and gene annotations
x$genes <- genes
xAn object of class "DGEList"
$samples55 more rows ...
$counts
Samples
Tags 9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq
ENSG00000000005.5 47
ENSG00000000419.11 1212
ENSG00000000457.12 1176
ENSG00000000460.15 121
ENSG00000000938.11 166
Samples
Tags bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq
ENSG00000000005.5 4
ENSG00000000419.11 710
ENSG00000000457.12 236
ENSG00000000460.15 211
ENSG00000000938.11 140
Samples
Tags 5697212f-b3fd-479f-84b0-ec0aae54534a.htseq
ENSG00000000005.5 2
ENSG00000000419.11 702
ENSG00000000457.12 552
ENSG00000000460.15 320
ENSG00000000938.11 93
Samples
Tags 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq
ENSG00000000005.5 10
ENSG00000000419.11 432
ENSG00000000457.12 803
ENSG00000000460.15 605
ENSG00000000938.11 473
Samples
Tags 15864159-be88-41c8-bdef-c2c5927cb1a1.htseq
ENSG00000000005.5 3
ENSG00000000419.11 641
ENSG00000000457.12 311
ENSG00000000460.15 182
ENSG00000000938.11 130
Samples
Tags 649b19e1-96e2-4b55-951d-3b6ee9f4b91f.htseq
ENSG00000000005.5 14
ENSG00000000419.11 1151
ENSG00000000457.12 246
ENSG00000000460.15 202
ENSG00000000938.11 52
Samples
Tags 86679663-dfc5-46ad-8cf9-c7954c4b339b.htseq
ENSG00000000005.5 14
ENSG00000000419.11 3675
ENSG00000000457.12 1901
ENSG00000000460.15 1436
ENSG00000000938.11 862
Samples
Tags 28004569-048d-4f8c-99aa-7a8c69a98dcc.htseq
ENSG00000000005.5 37
ENSG00000000419.11 2278
ENSG00000000457.12 835
ENSG00000000460.15 697
ENSG00000000938.11 687
Samples
Tags 911f6378-8a25-4570-9d3b-80f5b5bfc085.htseq
ENSG00000000005.5 2
ENSG00000000419.11 1934
ENSG00000000457.12 745
ENSG00000000460.15 464
ENSG00000000938.11 138
Samples
Tags d5dca54e-d7e9-4328-b2ca-1d191a2b8b4c.htseq
ENSG00000000005.5 3
ENSG00000000419.11 707
ENSG00000000457.12 366
ENSG00000000460.15 206
ENSG00000000938.11 80
Samples
Tags f3895ae4-1228-49b3-9342-3c3b86cb5243.htseq
ENSG00000000005.5 10
ENSG00000000419.11 1282
ENSG00000000457.12 624
ENSG00000000460.15 267
ENSG00000000938.11 979
Samples
Tags f590941d-19dc-427a-95b6-942c97ea8333.htseq
ENSG00000000005.5 0
ENSG00000000419.11 727
ENSG00000000457.12 356
ENSG00000000460.15 240
ENSG00000000938.11 378
Samples
Tags 55aa6d16-3598-42ca-8844-0fe84739ef66.htseq
ENSG00000000005.5 1
ENSG00000000419.11 2949
ENSG00000000457.12 892
ENSG00000000460.15 823
ENSG00000000938.11 389
Samples
Tags 0e7094cf-4c79-43f4-8b72-9de259e5e18f.htseq
ENSG00000000005.5 10
ENSG00000000419.11 219
ENSG00000000457.12 95
ENSG00000000460.15 106
ENSG00000000938.11 320
Samples
Tags 9a62fe1f-36ec-4e8e-b3d9-bdfc62f71905.htseq
ENSG00000000005.5 7
ENSG00000000419.11 1503
ENSG00000000457.12 566
ENSG00000000460.15 389
ENSG00000000938.11 235
Samples
Tags d2587070-cb7d-440d-ae49-52f5077248e6.htseq
ENSG00000000005.5 124
ENSG00000000419.11 2070
ENSG00000000457.12 886
ENSG00000000460.15 283
ENSG00000000938.11 2117
Samples
Tags 7800bdb2-aa8b-43e0-8e45-1b968872b34e.htseq
ENSG00000000005.5 2
ENSG00000000419.11 604
ENSG00000000457.12 215
ENSG00000000460.15 255
ENSG00000000938.11 228
Samples
Tags 2bcd2efd-4fd6-40ee-86a4-867ae82711b0.htseq
ENSG00000000005.5 6
ENSG00000000419.11 518
ENSG00000000457.12 215
ENSG00000000460.15 119
ENSG00000000938.11 159
Samples
Tags 424d8e5f-9fc6-470b-ad2c-b4447b0eb07e.htseq
ENSG00000000005.5 5
ENSG00000000419.11 2300
ENSG00000000457.12 1445
ENSG00000000460.15 831
ENSG00000000938.11 2183
Samples
Tags 934f9dc6-1260-4268-b022-870f1e37dd6f.htseq
ENSG00000000005.5 11
ENSG00000000419.11 627
ENSG00000000457.12 518
ENSG00000000460.15 401
ENSG00000000938.11 227
Samples
Tags 0fa55c0e-6f8f-44a6-82fe-9a42495d3484.htseq
ENSG00000000005.5 2
ENSG00000000419.11 1012
ENSG00000000457.12 468
ENSG00000000460.15 187
ENSG00000000938.11 534
Samples
Tags c8544a8a-4352-438d-94d4-3495af2e9a78.htseq
ENSG00000000005.5 433
ENSG00000000419.11 3532
ENSG00000000457.12 771
ENSG00000000460.15 449
ENSG00000000938.11 99
Samples
Tags dade0b16-ecc3-43b3-b328-3819a8fc18c6.htseq
ENSG00000000005.5 6
ENSG00000000419.11 3445
ENSG00000000457.12 840
ENSG00000000460.15 523
ENSG00000000938.11 891
Samples
Tags e875ae4e-4645-4e84-b0ff-9c9a694717a9.htseq
ENSG00000000005.5 0
ENSG00000000419.11 757
ENSG00000000457.12 234
ENSG00000000460.15 232
ENSG00000000938.11 333
Samples
Tags debd6982-7c27-42e8-b778-20afcc78a5f3.htseq
ENSG00000000005.5 0
ENSG00000000419.11 1519
ENSG00000000457.12 869
ENSG00000000460.15 317
ENSG00000000938.11 526
Samples
Tags 17c88994-9e8e-4f16-8c41-34e98a0d8c52.htseq
ENSG00000000005.5 11
ENSG00000000419.11 1875
ENSG00000000457.12 650
ENSG00000000460.15 325
ENSG00000000938.11 742
Samples
Tags 7f5a924a-ddf3-45ff-be1f-5b5909305f46.htseq
ENSG00000000005.5 11
ENSG00000000419.11 757
ENSG00000000457.12 332
ENSG00000000460.15 237
ENSG00000000938.11 139
Samples
Tags fa73bdce-67fb-42aa-883f-635f0e7bcdc6.htseq
ENSG00000000005.5 8
ENSG00000000419.11 959
ENSG00000000457.12 307
ENSG00000000460.15 246
ENSG00000000938.11 324
Samples
Tags abe20df7-6b97-4397-8864-881bac27e92c.htseq
ENSG00000000005.5 3
ENSG00000000419.11 392
ENSG00000000457.12 341
ENSG00000000460.15 335
ENSG00000000938.11 342
Samples
Tags 62f84581-4c7d-4c8e-835c-9304bcec3106.htseq
ENSG00000000005.5 1
ENSG00000000419.11 1144
ENSG00000000457.12 358
ENSG00000000460.15 320
ENSG00000000938.11 199
Samples
Tags 3abbd2b5-04db-4fe0-8dd1-ea2b48caa4c1.htseq
ENSG00000000005.5 1
ENSG00000000419.11 2901
ENSG00000000457.12 731
ENSG00000000460.15 494
ENSG00000000938.11 1845
Samples
Tags 087666cd-47ae-4f56-b947-d6aa1c25e8a7.htseq
ENSG00000000005.5 36
ENSG00000000419.11 3725
ENSG00000000457.12 1188
ENSG00000000460.15 741
ENSG00000000938.11 89
Samples
Tags c14f98e2-8e9b-49f4-a244-3d06c6cb7126.htseq
ENSG00000000005.5 2
ENSG00000000419.11 378
ENSG00000000457.12 171
ENSG00000000460.15 230
ENSG00000000938.11 440
Samples
Tags 13abc91e-fbfc-4c55-bf54-fbd134979ccc.htseq
ENSG00000000005.5 100
ENSG00000000419.11 2292
ENSG00000000457.12 831
ENSG00000000460.15 874
ENSG00000000938.11 489
Samples
Tags 6ae2dd6c-2a39-411f-a1fc-11e0e6e82165.htseq
ENSG00000000005.5 31
ENSG00000000419.11 4884
ENSG00000000457.12 765
ENSG00000000460.15 628
ENSG00000000938.11 284
Samples
Tags 8f77f4f4-b184-40c7-8ab8-2f95b13620b5.htseq
ENSG00000000005.5 4
ENSG00000000419.11 1593
ENSG00000000457.12 575
ENSG00000000460.15 368
ENSG00000000938.11 376
Samples
Tags 168e5cb2-7390-45ad-ad04-c9aa4416e950.htseq
ENSG00000000005.5 76
ENSG00000000419.11 1247
ENSG00000000457.12 274
ENSG00000000460.15 239
ENSG00000000938.11 158
Samples
Tags 0ed65bdf-cb92-47c1-8aeb-42518ce639b8.htseq
ENSG00000000005.5 3
ENSG00000000419.11 1853
ENSG00000000457.12 673
ENSG00000000460.15 437
ENSG00000000938.11 271
Samples
Tags 4e7c6811-88e4-4bb7-a88f-7491dfa6d072.htseq
ENSG00000000005.5 1
ENSG00000000419.11 506
ENSG00000000457.12 270
ENSG00000000460.15 184
ENSG00000000938.11 918
Samples
Tags 7fb73a84-867a-4c28-aa02-93068efffb7b.htseq
ENSG00000000005.5 19
ENSG00000000419.11 1464
ENSG00000000457.12 271
ENSG00000000460.15 303
ENSG00000000938.11 93
Samples
Tags b53f9a9d-b24d-410d-b3e9-f2a8bf22ca27.htseq
ENSG00000000005.5 1
ENSG00000000419.11 1331
ENSG00000000457.12 743
ENSG00000000460.15 422
ENSG00000000938.11 437
Samples
Tags f7ce175f-763e-4a55-97e3-0381d889b0eb.htseq
ENSG00000000005.5 72
ENSG00000000419.11 4749
ENSG00000000457.12 877
ENSG00000000460.15 536
ENSG00000000938.11 446
Samples
Tags f346f2d2-285c-455c-ba34-ea8eec3fa881.htseq
ENSG00000000005.5 7
ENSG00000000419.11 1954
ENSG00000000457.12 422
ENSG00000000460.15 283
ENSG00000000938.11 97
Samples
Tags e53e1a83-1979-4e12-bbb7-79b37d0cfe03.htseq
ENSG00000000005.5 252
ENSG00000000419.11 2538
ENSG00000000457.12 581
ENSG00000000460.15 521
ENSG00000000938.11 208
Samples
Tags a26d49db-2309-46a0-a3ed-275378d484e7.htseq
ENSG00000000005.5 26
ENSG00000000419.11 3001
ENSG00000000457.12 875
ENSG00000000460.15 462
ENSG00000000938.11 39
Samples
Tags a3f88a5d-7169-465b-bb80-e5999590681c.htseq
ENSG00000000005.5 4
ENSG00000000419.11 2746
ENSG00000000457.12 732
ENSG00000000460.15 542
ENSG00000000938.11 888
Samples
Tags c264fe3b-482b-44ec-83a4-73df565663ff.htseq
ENSG00000000005.5 104
ENSG00000000419.11 5777
ENSG00000000457.12 684
ENSG00000000460.15 634
ENSG00000000938.11 304
Samples
Tags bd2dfab3-88a8-4673-ba36-3daf252d0b4d.htseq
ENSG00000000005.5 10
ENSG00000000419.11 980
ENSG00000000457.12 193
ENSG00000000460.15 309
ENSG00000000938.11 76
Samples
Tags 7261b656-c79c-4581-a503-15b653e2b5d2.htseq
ENSG00000000005.5 2
ENSG00000000419.11 2981
ENSG00000000457.12 658
ENSG00000000460.15 845
ENSG00000000938.11 459
Samples
Tags ee4dcccc-514b-4cc6-ae63-6ed3e7519a40.htseq
ENSG00000000005.5 78
ENSG00000000419.11 1846
ENSG00000000457.12 1368
ENSG00000000460.15 415
ENSG00000000938.11 283
Samples
Tags f596eabc-e39a-4e35-9fc6-edade04eb785.htseq
ENSG00000000005.5 12
ENSG00000000419.11 478
ENSG00000000457.12 98
ENSG00000000460.15 95
ENSG00000000938.11 112
Samples
Tags bf9c448b-bdc9-4f74-b13a-374e6add7939.htseq
ENSG00000000005.5 4
ENSG00000000419.11 850
ENSG00000000457.12 277
ENSG00000000460.15 315
ENSG00000000938.11 67
Samples
Tags 564daa81-cfef-45b6-94a0-3249b2724d9b.htseq
ENSG00000000005.5 19
ENSG00000000419.11 202
ENSG00000000457.12 117
ENSG00000000460.15 61
ENSG00000000938.11 91
Samples
Tags 82e00e45-734c-471f-ba97-79ec3b7e0baa.htseq
ENSG00000000005.5 26
ENSG00000000419.11 5155
ENSG00000000457.12 728
ENSG00000000460.15 626
ENSG00000000938.11 46
Samples
Tags 9c52ed00-325f-4664-8873-327bcaa5ea74.htseq
ENSG00000000005.5 24
ENSG00000000419.11 557
ENSG00000000457.12 454
ENSG00000000460.15 175
ENSG00000000938.11 70
Samples
Tags fabefb10-5546-4017-8ea1-29982a10fb3c.htseq
ENSG00000000005.5 15
ENSG00000000419.11 4147
ENSG00000000457.12 679
ENSG00000000460.15 764
ENSG00000000938.11 477
Samples
Tags 32a115cf-570f-4ad9-a123-8e1970062f51.htseq
ENSG00000000005.5 37
ENSG00000000419.11 2843
ENSG00000000457.12 1259
ENSG00000000460.15 869
ENSG00000000938.11 438
Samples
Tags 05eef9f8-a246-403a-b0be-07d274b6f93a.htseq
ENSG00000000005.5 42
ENSG00000000419.11 844
ENSG00000000457.12 137
ENSG00000000460.15 133
ENSG00000000938.11 24
Samples
Tags 5c18c6a8-9ad2-43a8-a3a0-83d8fc0cc257.htseq
ENSG00000000005.5 179
ENSG00000000419.11 1307
ENSG00000000457.12 571
ENSG00000000460.15 307
ENSG00000000938.11 136
Samples
Tags 43b292be-5d63-4523-a43f-666d20039208.htseq
ENSG00000000005.5 140
ENSG00000000419.11 1101
ENSG00000000457.12 407
ENSG00000000460.15 191
ENSG00000000938.11 85
60482 more rows ...
$genes60482 more rows ...Ashley E Noriega
Nov 30, 2019
TRGN 510 Final Project: Milestone 3
Running the Glimma Vignette
Data Pre-processing
Transformations from the raw-scale: convert raw counts to counts per million (CPM) and log2-counts per million (log-CPM)
cpm <- cpm(x)
lcpm <- cpm(x, log=TRUE)
L <- mean(x$samples$lib.size) * 1e-6
M <- median(x$samples$lib.size) * 1e-6
c(L, M)[1] 64.23804 58.76902summary(lcpm) 9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq
Min. :-5.0054 Min. :-5.005
1st Qu.:-5.0054 1st Qu.:-5.005
Median :-5.0054 Median :-5.005
Mean :-2.5302 Mean :-2.573
3rd Qu.:-0.7721 3rd Qu.:-1.020
Max. :17.9542 Max. :18.478
5697212f-b3fd-479f-84b0-ec0aae54534a.htseq 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq
Min. :-5.005 Min. :-5.0054
1st Qu.:-5.005 1st Qu.:-5.0054
Median :-5.005 Median :-3.4138
Mean :-2.548 Mean :-2.3687
3rd Qu.:-1.079 3rd Qu.:-0.6434
Max. :18.160 Max. :19.0973
15864159-be88-41c8-bdef-c2c5927cb1a1.htseq 649b19e1-96e2-4b55-951d-3b6ee9f4b91f.htseq
Min. :-5.005 Min. :-5.0054
1st Qu.:-5.005 1st Qu.:-5.0054
Median :-5.005 Median :-5.0054
Mean :-2.406 Mean :-2.4792
3rd Qu.:-0.591 3rd Qu.:-0.8821
Max. :18.390 Max. :18.3537
86679663-dfc5-46ad-8cf9-c7954c4b339b.htseq 28004569-048d-4f8c-99aa-7a8c69a98dcc.htseq
Min. :-5.0054 Min. :-5.0054
1st Qu.:-5.0054 1st Qu.:-5.0054
Median :-4.6634 Median :-4.6140
Mean :-2.4026 Mean :-2.4599
3rd Qu.:-0.7838 3rd Qu.:-0.7757
Max. :18.3240 Max. :18.0427
911f6378-8a25-4570-9d3b-80f5b5bfc085.htseq d5dca54e-d7e9-4328-b2ca-1d191a2b8b4c.htseq
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Max. :18.7800 Max. :18.3746 Remove lowly expressed genes
True signifies how many genes have counts equal to zero, meaning genes are unexpressed throughout all samples.
table(rowSums(x$counts==0)==9)
FALSE TRUE
60074 413 Filter genes while keeping as many genes as possible with worthwile counts
keep.exprs <- filterByExpr(x, group=group)
x <- x[keep.exprs,, keep.lib.sizes=FALSE]
dim(x)[1] 19105 60Plot the density of log-CPM values for raw and filtered data
There is a sample that is a potential outlier (green colored line), could remove the sample for future analysis but spoke to porfessor Craig on 12/4 and agreed to leave the sample in since the vignette has a normalisation step.
Known issue: color palette
Spoke to professor Craig on 12/4 and agreed to stop working on this issue. I understand that the “Paired” palatte only offers 12 colors so every 13th sample repeats color scheme. I tried increasing the number of colors available with colorRampPalatte but was unsuccesful.
lcpm.cutoff <- log2(10/M + 2/L)
library(RColorBrewer)
#library(colorRamps)
nsamples <- ncol(x)
col <- brewer.pal(nsamples, "Paired") #results in the error message: n too large, allowed maximum for palette Paired is 12. Returning the palette you asked for with that many colorsn too large, allowed maximum for palette Paired is 12
Returning the palette you asked for with that many colors#nb.cols = 60
#col <- colorRampPalette(brewer.pal(nsamples, "Paired"))(nb.cols) #colorRampPalette is a constructor function that builds palettes with arbitrary number of colors by interpolating existing palette
par(mfrow=c(1,2)) #1 row, 2 columns
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")
title(main="A. Raw data", xlab="Log-cpm")abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}legend("topright", samplenames, text.col=col, bty="n")
lcpm <- cpm(x, log=TRUE)
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")title(main="B. Filtered data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", samplenames, text.col=col, bty="n")
Normalising gene expression distributions
x <- calcNormFactors(x, method = "TMM")
x$samples$norm.factors [1] 0.8247877 0.8701067 0.9744718 0.3338411 1.0672340 0.9853437 0.8355374 1.0685271 1.1041480
[10] 1.1621714 1.3190864 1.1616288 0.5656024 1.0079779 1.1311621 1.3455788 0.2548806 1.1728192
[19] 0.5164198 0.4158854 1.1552465 1.0977806 1.1538364 1.0380711 0.5054850 1.2962443 1.1705252
[28] 0.9900169 0.9259593 1.1222135 1.2985756 1.0598233 0.9471138 1.3391984 1.3043419 1.1144424
[37] 1.0697018 1.1921660 1.1054413 0.9399911 1.2384865 1.2243347 1.0760378 0.9933192 1.1164186
[46] 1.4176459 1.3689476 0.8989757 1.3130426 1.0789261 0.7851273 1.0110826 0.9751891 1.1994225
[55] 1.2667583 1.3476310 1.4869359 1.0917179 0.8579364 1.0468322Improve visualization by duplicating data, then adjusting the counts
x2 <- x
x2$samples$norm.factors <- 1
x2$counts[,1] <- ceiling(x2$counts[,1]*0.05)
x2$counts[,2] <- x2$counts[,2]*5Boxplot expression distribution of samples for unnormalised data
par(mfrow=c(1,1)) #makes boxplot look less cramped
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="A. Example: Unnormalised data",ylab="Log-cpm")
x2 <- calcNormFactors(x2)
x2$samples$norm.factors [1] 0.04889808 4.36314201 0.99857375 0.36344409 1.08814631 1.00048470 0.91165862 1.07196030
[9] 1.10500025 1.21054683 1.28772624 1.16063702 0.60882746 1.04998828 1.13203522 1.31787908
[17] 0.28023587 1.16142488 0.53329090 0.46494919 1.14274272 1.09741972 1.16406362 1.04925131
[25] 0.51082602 1.30832124 1.17758030 1.00128465 0.97668711 1.11752935 1.28315406 1.05679584
[33] 0.99263908 1.36510564 1.33873108 1.14293994 1.10193770 1.21035319 1.10148876 0.97629109
[41] 1.25905436 1.25325781 1.12605092 1.02503014 1.11956858 1.41609997 1.38704078 0.91182127
[49] 1.31684398 1.09702295 0.85028060 1.03939580 1.01768934 1.20164571 1.27684217 1.35509884
[57] 1.51897054 1.10244902 0.85428854 1.05218206Boxplot expression distribution of samples for normalised data
This step forces the samples to even out, may not be a good thing since there is a potential outlier.
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="B. Example: Normalised data",ylab="Log-cpm")
Unsupervised clustering of cells: make multi-dimensional scaling plot (MDS) to show simmilarities and dissimilarities between samples in an unsupervised manner
Known issue: color palette
I spoke to professor Craig on 12/4, ok to ignore error since I am only comparing 2 different subsets of colon cancer. To get rid of this error I would need to add an additional factor: lane.
lcpm <- cpm(x, log=TRUE)
par(mfrow=c(1,1)) #1 row, 1 column
col.group <- group
levels(col.group) <- brewer.pal(nlevels(col.group), "Set1") #n= number of different colors in a palette with the min being 3 minimal value for n is 3, returning requested palette with 3 different levelscol.group <- as.character(col.group)
#col.lane <- lane did not have lanes for my data
#levels(col.lane) <- brewer.pal(nlevels(col.lane), "Set2")
#col.lane <- as.character(col.lane)
plotMDS(lcpm, labels=group, col=col.group)
title(main="A. Sample groups")
#plotMDS(lcpm, labels=lane, col=col.lane, dim=c(3,4))
#title(main="B. Sequencing lanes")Make interactive using Glimma
HTML page will be generarted and opened in a browser if launch=TRUE
suppressPackageStartupMessages(library(Glimma))
glMDSPlot(lcpm, labels=paste(group, sep="_"),
groups=x$samples[,c(1,2)], launch=FALSE)Differential expression analysis
Creating a design matrix
design <- model.matrix(~0+group) #removes intercept from the factor group
#design <- model.matrix(~group) leaves intercept from factor group, but model contrasts are more straight forward without intercept
colnames(design) <- gsub("group", "", colnames(design))
design ADENOCARCINOMA CMS
1 0 1
2 0 1
3 0 1
4 0 1
5 0 1
6 0 1
7 0 1
8 0 1
9 0 1
10 0 1
11 0 1
12 0 1
13 0 1
14 0 1
15 0 1
16 0 1
17 0 1
18 0 1
19 0 1
20 0 1
21 0 1
22 0 1
23 0 1
24 0 1
25 0 1
26 0 1
27 0 1
28 0 1
29 0 1
30 0 1
31 1 0
32 1 0
33 1 0
34 1 0
35 1 0
36 1 0
37 1 0
38 1 0
39 1 0
40 1 0
41 1 0
42 1 0
43 1 0
44 1 0
45 1 0
46 1 0
47 1 0
48 1 0
49 1 0
50 1 0
51 1 0
52 1 0
53 1 0
54 1 0
55 1 0
56 1 0
57 1 0
58 1 0
59 1 0
60 1 0
attr(,"assign")
[1] 1 1
attr(,"contrasts")
attr(,"contrasts")$group
[1] "contr.treatment"Contrasts for pairwise comparisons between cell populations
Since I am only comparing CMS and Adenocarcinoma, I will only have 1 pairwise comparison.
library(limma)
contr.matrix <- makeContrasts(
ADENOCARCINOMAvsCMS = ADENOCARCINOMA-CMS,
levels = colnames(design))
contr.matrix Contrasts
Levels ADENOCARCINOMAvsCMS
ADENOCARCINOMA 1
CMS -1## Remove heteroscedascity from count data
Voom plot
Each black dot represents a gene. The red curve is the estimated mean-varience trend used to compute the voom weights.
par(mfrow=c(1,2))
v <- voom(x, design, plot=TRUE) #voom converts raw counts to log-CPM values by extracting library sizes and normalisation factors from x
vAn object of class "EList"
$genes19100 more rows ...
$targets55 more rows ...
$E
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$design
ADENOCARCINOMA CMS
1 0 1
2 0 1
3 0 1
4 0 1
5 0 1
55 more rows ...Apply voom precision weights to data
Each black dot is a gene. The blue line is the average log2 residual standard deviation computed with the Bayes algorithm.
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
plotSA(efit, main="Final model: Mean-variance trend") #plots log2 residual standard deviations against mean log-CPM values
Examine the number of DE genes
Quick view at how many genes are down-regulated, up-regulated, and not statistically significant. The adjusted p-value cutoff is 5% by default.
summary(decideTests(efit)) ADENOCARCINOMAvsCMS
Down 1474
NotSig 15810
Up 1821Set a minimum log-fold change(log-FC) of 1
This is a stricter definition of significance and could be overcorrecting since now I don’t have any down-regulated or up-regulated genes.
tfit <- treat(vfit, lfc=1) #p-values calculated from empirical Bayes moderated t-statistics with a minimum log-FC requirement.
dt <- decideTests(tfit)
#dt <- decideTests(efit) #for testing purposes
summary(dt) ADENOCARCINOMAvsCMS
Down 0
NotSig 19105
Up 0Extract genes that are DE in multiple comparisons
I don’t have any DE genes if tfit is used. If efit is used, I have 3295 DE genes.
de.common <- which(dt[,1]!=0)
length(de.common) [1] 0The first 20 DE genes
If efit is used the genes are: “DPM1”, “CFH”, “LAS1L”, “CFTR”, “TMEM176A”, “DBNDD1”, “TFPI”, “SLC7A2”, “ARF5”, “POLDIP2”, “ARHGAP33”, “UPF1”, “MCUB”, “POLR2J”, “THSD7A”, “LIG3”, “SPPL2B”, “IBTK”, “PDK2”, “REX1BD”
head(tfit$genes$SYMBOL[de.common], n=20)character(0)Make Venn Diagram
My diagram only has 1 circle because I only have 1 pairwise comparison.
vennDiagram(dt[,1], circle.col=c("turquoise", "salmon"))
Extract and write results for comparisons of ADENOCARCINOMAvsCMS to a single output file
write.fit(tfit, dt, file="results.txt")Examining individual DE genes from top to bottom
ADENOCARCINOMA.vs.CMS <- topTreat(tfit, coef=1, n=Inf)
head(ADENOCARCINOMA.vs.CMS)Summarize results for genes using mean-difference plots that highlight DE genes
If efit is used, will have read, black and blue genes. Since tfit is used, all genes are black.
plotMD(tfit, column=1, status=dt[,1], main=colnames(tfit)[1],
xlim=c(-8,13))
Make interactive mean-difference plot
To open HTML page in a browser make launch=TRUE
library(Glimma)
glMDPlot(tfit, coef=1, status=dt, main=colnames(tfit)[1],
side.main="ENSEMBL", counts=lcpm, groups=group, launch=FALSE)Make heatmap
Install heatmap.plus beacuse heatmap.2 did not work for my data.
library(gplots)
library(heatmap.plus)
ADENOCARCINOMA.vs.CMS.topgenes <- ADENOCARCINOMA.vs.CMS$ENSEMBL[1:100]
i <- which(v$genes$ENSEMBL %in% ADENOCARCINOMA.vs.CMS.topgenes)
mycol <- colorpanel(1000,"blue","white","red")
#par("mar") OUTPUT SHOULD BE [1] 5.1 4.1 4.1 2.1
par(cex.main=0.8,mar=c(1,1,1,1)) #mar=c(1,1,1,1) ensures margins are large enough
heatmap.plus(lcpm[i,], col=bluered(20),cexRow=1,cexCol=0.2, margins = c(10,10), main = "HeatMap") #changed the margins to have a more legible heatmap
---
title: "Milestone 4 test"
output: html_notebook
---

# Ashley E Noriega,
# Nov 20, 2019
# TRGN 510 Final Project: Milestone 2
# Loading in RNA seq colon cancer data from TCGA 
## RNASeq files for cystic, mucinous, and serous neoplasms and adenocarcinoma
I created a new folder called COAD and stored all 60 files in it. I then changed them to TXT files and opened them.

# Data Packaging

## The urls for the 30 files I downloaded locally for cystic, mucinous and serous colon cancer (CMS)

1. https://portal.gdc.cancer.gov/files/536f5a77-0087-457d-ac95-6d1a9abad8cb, UUID 536f5a77-0087-457d-ac95-6d1a9abad8cb, case: TCGA-AA-3516 

2. https://portal.gdc.cancer.gov/files/ed52de66-66fa-44ce-b679-cf641b0d92cd, UUID ed52de66-66fa-44ce-b679-cf641b0d92cd, case: TCGA-AA-3516 

3. https://portal.gdc.cancer.gov/files/b28090c5-c42d-4836-9bb1-ce906d3ead95, UUID: b28090c5-c42d-4836-9bb1-ce906d3ead95, case TCGA-AA-3854 

4. https://portal.gdc.cancer.gov/cases/57cdaa1c-4e94-4a28-ab3b-300c0457555f, UUID: 49e29c69-d9d7-4496-9f24-26f42c8b6d8e, case: TCGA-A6-2674 

5. https://portal.gdc.cancer.gov/files/08ed32e4-fb94-4bc0-8715-83ee2143a13d, UUID: 08ed32e4-fb94-4bc0-8715-83ee2143a13d, case: TCGA-AA-A00J 

6. https://portal.gdc.cancer.gov/files/6e571f71-d5fb-42f3-a35b-554c5ab76587, UUID: 6e571f71-d5fb-42f3-a35b-554c5ab76587, case: TCGA-AA-A01G 

7. https://portal.gdc.cancer.gov/files/8b12a000-f588-4a78-a9eb-f06041a65789, UUID: 8b12a000-f588-4a78-a9eb-f06041a65789, case: TCGA-A6-6780 

8. https://portal.gdc.cancer.gov/files/02734d4d-fc8f-4ef7-ac82-1b4d7184cc5e, UUID: 02734d4d-fc8f-4ef7-ac82-1b4d7184cc5e, case: TCGA-CK-4950 

9. https://portal.gdc.cancer.gov/files/6466a8b1-d1e2-4195-a353-0800576c13c8, UUID: 6466a8b1-d1e2-4195-a353-0800576c13c8, case: TCGA-G4-6322 

10. https://portal.gdc.cancer.gov/files/bc47f01c-1994-4ff8-a356-94d9679b66ee, UUID: bc47f01c-1994-4ff8-a356-94d9679b66ee, case: TCGA-AA-3947 

11. https://portal.gdc.cancer.gov/files/b045ee79-82a6-4636-a875-1a58603d89ff, UUID: b045ee79-82a6-4636-a875-1a58603d89ff, case: TCGA-A6-A566 

12. https://portal.gdc.cancer.gov/files/c383ba2c-b00a-4bd2-82cb-b3f04c2a8172, UUID: c383ba2c-b00a-4bd2-82cb-b3f04c2a8172, case: TCGA-AA-3877 

13. https://portal.gdc.cancer.gov/files/b52775aa-273e-484e-82c7-c625f09415fa, UUID: b52775aa-273e-484e-82c7-c625f09415fa, case: TCGA-A6-3809 

14. https://portal.gdc.cancer.gov/files/7b15a87a-805c-4b8a-84de-549cec9c44e3, UUID: 7b15a87a-805c-4b8a-84de-549cec9c44e3, case: TCGA-AA-3684 

15. https://portal.gdc.cancer.gov/files/b4f3dbbb-2686-4896-9e60-5bef6c9150b4, UUID: b4f3dbbb-2686-4896-9e60-5bef6c9150b4, case: TCGA-AA-3692 

16. https://portal.gdc.cancer.gov/files/0b16e2bd-3ec7-4901-9ff0-a389670e5019, UUID: 0b16e2bd-3ec7-4901-9ff0-a389670e5019, case: TCGA-D5-6534 

17. https://portal.gdc.cancer.gov/files/a6690007-f347-49c3-a0ba-28e01d131971, UUID: a6690007-f347-49c3-a0ba-28e01d131971, case: TCGA-A6-3809 

18. https://portal.gdc.cancer.gov/files/a1742cf6-c3c5-43e7-879c-489494460e78, UUID: a1742cf6-c3c5-43e7-879c-489494460e78, case: TCGA-AA-A00N 

19. https://portal.gdc.cancer.gov/files/d5be795d-beb6-4def-bda8-f485ee45bfc1, UUID: d5be795d-beb6-4def-bda8-f485ee45bfc1, case: TCGA-A6-2674 

20. https://portal.gdc.cancer.gov/files/46306072-c59c-4b4b-963c-9c4e778ff34b, UUID: 46306072-c59c-4b4b-963c-9c4e778ff34b, case: TCGA-A6-6780 

21. https://portal.gdc.cancer.gov/files/a938cb2c-c8e8-4395-915b-37e1e279a4da, UUID: a938cb2c-c8e8-4395-915b-37e1e279a4da, case: TCGA-G4-6302 

22. https://portal.gdc.cancer.gov/files/7fec7c90-fd2e-4ee2-ba1a-77f85920771f, UUID: 7fec7c90-fd2e-4ee2-ba1a-77f85920771f, case: TCGA-DM-A282 

23. https://portal.gdc.cancer.gov/files/2c3fd34c-70d1-4331-9628-260b77329b53, UUID: 2c3fd34c-70d1-4331-9628-260b77329b53, case: TCGA-F4-6704 

24. https://portal.gdc.cancer.gov/files/4168a720-521e-47ff-afb5-4abe3e815490, UUID: 4168a720-521e-47ff-afb5-4abe3e815490, case: TCGA-AA-3950 

25. https://portal.gdc.cancer.gov/files/ecc90bd1-f594-41ea-ba4b-d42f4c64880b, UUID: ecc90bd1-f594-41ea-ba4b-d42f4c64880b, case: TCGA-A6-6781 

26. https://portal.gdc.cancer.gov/files/8736ed27-2141-48d9-b677-b1a0e14d4b50, UUID: 8736ed27-2141-48d9-b677-b1a0e14d4b50, case: TCGA-CA-6717 

27. https://portal.gdc.cancer.gov/files/3b8d04cd-d658-46ba-adca-079fee531e17, UUID: 3b8d04cd-d658-46ba-adca-079fee531e17, case: TCGA-AA-3821 

28. https://portal.gdc.cancer.gov/files/b27da518-d023-4f9c-a9ab-5cd68ee37870, UUID: b27da518-d023-4f9c-a9ab-5cd68ee37870, case: TCGA-CK-4951 

29. https://portal.gdc.cancer.gov/files/e7005df6-f78b-4e47-abe7-61ae6a2ee026, UUID: e7005df6-f78b-4e47-abe7-61ae6a2ee026, case: TCGA-AA-A01R 

30. https://portal.gdc.cancer.gov/files/e3598d14-292c-41cc-9b59-4497fa078272, UUID: e3598d14-292c-41cc-9b59-4497fa078272, case: TCGA-D5-6930 


## The urls for the 30 files adenocarcinoma I downloaded locally for adenocarcinoma

1. https://portal.gdc.cancer.gov/files/f1185347-ad15-43ae-9ef3-d5343b31a0fc, UUID: f1185347-ad15-43ae-9ef3-d5343b31a0fc, case: TCGA-A6-6654

2. https://portal.gdc.cancer.gov/files/0d53cb1c-97c4-4088-9e43-029de88fd66d, UUID: 0d53cb1c-97c4-4088-9e43-029de88fd66d, case: TCGA-DM-A1D4

3. https://portal.gdc.cancer.gov/files/a74bbce0-7f3d-434e-b294-7fa45e5b3a60, UUID: a74bbce0-7f3d-434e-b294-7fa45e5b3a60, case: TCGA-A6-2684

4. https://portal.gdc.cancer.gov/files/47554e4e-cd13-4b92-80be-e1940f9a950f, UUID: 47554e4e-cd13-4b92-80be-e1940f9a950f, case: TCGA-A6-5657

5. https://portal.gdc.cancer.gov/files/de60dbd7-8a93-47a5-b1ea-a3f95beade8a, UUID: de60dbd7-8a93-47a5-b1ea-a3f95beade8a, case: TCGA-F4-6854

6. https://portal.gdc.cancer.gov/files/70883b31-d130-4efd-a7c6-169c8d4a253d, UUID: 70883b31-d130-4efd-a7c6-169c8d4a253d, case: TCGA-AD-A5EJ

7. https://portal.gdc.cancer.gov/files/042bda3d-77aa-4522-8a97-c121711a760e, UUID: 042bda3d-77aa-4522-8a97-c121711a760e, case: TCGA-AG-3582

8. https://portal.gdc.cancer.gov/files/b6388e09-7ed5-4041-97bb-4427ba5571ba, UUID: b6388e09-7ed5-4041-97bb-4427ba5571ba, case: TCGA-AY-6197

9. https://portal.gdc.cancer.gov/files/54394c0b-6ae3-4b48-8e89-350ad5349611, UUID: 54394c0b-6ae3-4b48-8e89-350ad5349611, case: TCGA-AA-3554

10. https://portal.gdc.cancer.gov/files/f7e21d61-19b6-4e99-887f-463d4419628c, UUID: f7e21d61-19b6-4e99-887f-463d4419628c, case: TCGA-AG-4015

11. https://portal.gdc.cancer.gov/files/b4114885-38cd-4e8a-874b-b78da8d95e2c, UUID: b4114885-38cd-4e8a-874b-b78da8d95e2c, case: TCGA-CM-6171

12. https://portal.gdc.cancer.gov/files/f9fda40d-67e4-4cb9-859c-ddc2ea84b7e4, UUID: f9fda40d-67e4-4cb9-859c-ddc2ea84b7e4, case: TCGA-CM-6170

13. https://portal.gdc.cancer.gov/files/b4aebb2a-d0b8-43d8-bd1f-78af2065d8f9, UUID: b4aebb2a-d0b8-43d8-bd1f-78af2065d8f9, case: TCGA-AA-3846

14. https://portal.gdc.cancer.gov/files/6a750710-5ed9-4d24-b2bf-3a4e3211878f, UUID: 6a750710-5ed9-4d24-b2bf-3a4e3211878f, case: TCGA-CM-6677

15. https://portal.gdc.cancer.gov/files/93d1a78f-423e-4560-b4d3-ee4a89ac922b, UUID: 93d1a78f-423e-4560-b4d3-ee4a89ac922b, case: TCGA-RU-A8FL

16. https://portal.gdc.cancer.gov/files/2e632fd9-fa17-4290-9601-a5d462cf152c, UUID: 2e632fd9-fa17-4290-9601-a5d462cf152c, case: TCGA-AZ-4323

17. https://portal.gdc.cancer.gov/files/7239b026-2587-489d-81fe-7bc657b7523c, UUID: 7239b026-2587-489d-81fe-7bc657b7523c, case: TCGA-CM-6164

18. https://portal.gdc.cancer.gov/files/90e86a26-fffa-4c38-b2e0-bf0704ee3615, UUID: 90e86a26-fffa-4c38-b2e0-bf0704ee3615, case: TCGA-AZ-4315

19. https://portal.gdc.cancer.gov/files/9ff11fe0-037c-405e-95c3-dc4a15413db8, UUID: 9ff11fe0-037c-405e-95c3-dc4a15413db8, case: TCGA-G4-6311

20. https://portal.gdc.cancer.gov/files/b8eed826-6051-4358-9b3d-44d1553dd9ad, UUID: b8eed826-6051-4358-9b3d-44d1553dd9ad, case: TCGA-AA-3522

21. https://portal.gdc.cancer.gov/files/c172bc07-d4f0-41be-a558-49abc81065c2, UUID: c172bc07-d4f0-41be-a558-49abc81065c2, case: TCGA-AA-3667

22. https://portal.gdc.cancer.gov/files/260edc5e-1ca6-4b07-b96d-59594d03ac54, UUID: 260edc5e-1ca6-4b07-b96d-59594d03ac54, case: TCGA-AA-A00U

23. https://portal.gdc.cancer.gov/files/0c5c1a38-7e9c-4b43-810d-0761c3af49b1, UUID: 0c5c1a38-7e9c-4b43-810d-0761c3af49b1, case: TCGA-AA-3506

24. https://portal.gdc.cancer.gov/files/7024ba0c-be56-4907-9254-cdb2579e536e, UUID: 7024ba0c-be56-4907-9254-cdb2579e536e, case: TCGA-NH-A8F7

25. https://portal.gdc.cancer.gov/files/031cf2a5-74e0-4b5f-98bd-da60628c0854, UUID: 031cf2a5-74e0-4b5f-98bd-da60628c0854, case: TCGA-AA-3680

26. https://portal.gdc.cancer.gov/files/91991ecf-cc54-4110-8a4e-9236bf8aa072, UUID: 91991ecf-cc54-4110-8a4e-9236bf8aa072, case: TCGA-A6-4105

27. https://portal.gdc.cancer.gov/files/47aceec1-a01d-419f-9689-c46284c79bcb, UUID: 47aceec1-a01d-419f-9689-c46284c79bcb, case: TCGA-D5-6922

28. https://portal.gdc.cancer.gov/files/ce84c955-63db-473a-a6d7-0e3daad6efd4, UUID: ce84c955-63db-473a-a6d7-0e3daad6efd4, case: TCGA-AA-3524

29. https://portal.gdc.cancer.gov/files/a071fc45-61ea-4815-93bf-be34980e59ee, UUID: a071fc45-61ea-4815-93bf-be34980e59ee, case: TCGA-AA-3855

30. https://portal.gdc.cancer.gov/files/8b275144-b885-4fb0-af39-fea1e48a970a, UUID: 8b275144-b885-4fb0-af39-fea1e48a970a, case: TCGA-AA-A00Q

## Load in count data
First rename the ".count" files to ".txt" and unzip each one by opening each file.
```{r}
setwd('~/Desktop/COAD_Data/')
COAD_files <- c("9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq.txt", "bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq.txt", 
   "5697212f-b3fd-479f-84b0-ec0aae54534a.htseq.txt", "7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq.txt",
   "15864159-be88-41c8-bdef-c2c5927cb1a1.htseq.txt", "649b19e1-96e2-4b55-951d-3b6ee9f4b91f.htseq.txt",
   "86679663-dfc5-46ad-8cf9-c7954c4b339b.htseq.txt", "28004569-048d-4f8c-99aa-7a8c69a98dcc.htseq.txt",                 "911f6378-8a25-4570-9d3b-80f5b5bfc085.htseq.txt", "d5dca54e-d7e9-4328-b2ca-1d191a2b8b4c.htseq.txt",                 "f3895ae4-1228-49b3-9342-3c3b86cb5243.htseq.txt", "f590941d-19dc-427a-95b6-942c97ea8333.htseq.txt",                 "55aa6d16-3598-42ca-8844-0fe84739ef66.htseq.txt", "0e7094cf-4c79-43f4-8b72-9de259e5e18f.htseq.txt",                 "9a62fe1f-36ec-4e8e-b3d9-bdfc62f71905.htseq.txt", "d2587070-cb7d-440d-ae49-52f5077248e6.htseq.txt",                 "7800bdb2-aa8b-43e0-8e45-1b968872b34e.htseq.txt", "2bcd2efd-4fd6-40ee-86a4-867ae82711b0.htseq.txt",                 "424d8e5f-9fc6-470b-ad2c-b4447b0eb07e.htseq.txt", "934f9dc6-1260-4268-b022-870f1e37dd6f.htseq.txt",                 "0fa55c0e-6f8f-44a6-82fe-9a42495d3484.htseq.txt", "c8544a8a-4352-438d-94d4-3495af2e9a78.htseq.txt",                 "dade0b16-ecc3-43b3-b328-3819a8fc18c6.htseq.txt", "e875ae4e-4645-4e84-b0ff-9c9a694717a9.htseq.txt",                 "debd6982-7c27-42e8-b778-20afcc78a5f3.htseq.txt", "17c88994-9e8e-4f16-8c41-34e98a0d8c52.htseq.txt",                 "7f5a924a-ddf3-45ff-be1f-5b5909305f46.htseq.txt", "fa73bdce-67fb-42aa-883f-635f0e7bcdc6.htseq.txt",                 "abe20df7-6b97-4397-8864-881bac27e92c.htseq.txt", "62f84581-4c7d-4c8e-835c-9304bcec3106.htseq.txt", "3abbd2b5-04db-4fe0-8dd1-ea2b48caa4c1.htseq.txt", "087666cd-47ae-4f56-b947-d6aa1c25e8a7.htseq.txt", 
   "c14f98e2-8e9b-49f4-a244-3d06c6cb7126.htseq.txt", "13abc91e-fbfc-4c55-bf54-fbd134979ccc.htseq.txt",
   "6ae2dd6c-2a39-411f-a1fc-11e0e6e82165.htseq.txt", "8f77f4f4-b184-40c7-8ab8-2f95b13620b5.htseq.txt",
   "168e5cb2-7390-45ad-ad04-c9aa4416e950.htseq.txt", "0ed65bdf-cb92-47c1-8aeb-42518ce639b8.htseq.txt",                 "4e7c6811-88e4-4bb7-a88f-7491dfa6d072.htseq.txt", "7fb73a84-867a-4c28-aa02-93068efffb7b.htseq.txt",                 "b53f9a9d-b24d-410d-b3e9-f2a8bf22ca27.htseq.txt", "f7ce175f-763e-4a55-97e3-0381d889b0eb.htseq.txt",                 "f346f2d2-285c-455c-ba34-ea8eec3fa881.htseq.txt", "e53e1a83-1979-4e12-bbb7-79b37d0cfe03.htseq.txt",                 "a26d49db-2309-46a0-a3ed-275378d484e7.htseq.txt", "a3f88a5d-7169-465b-bb80-e5999590681c.htseq.txt",                 "c264fe3b-482b-44ec-83a4-73df565663ff.htseq.txt", "bd2dfab3-88a8-4673-ba36-3daf252d0b4d.htseq.txt",                 "7261b656-c79c-4581-a503-15b653e2b5d2.htseq.txt", "ee4dcccc-514b-4cc6-ae63-6ed3e7519a40.htseq.txt",                 "f596eabc-e39a-4e35-9fc6-edade04eb785.htseq.txt", "bf9c448b-bdc9-4f74-b13a-374e6add7939.htseq.txt",                 "564daa81-cfef-45b6-94a0-3249b2724d9b.htseq.txt", "82e00e45-734c-471f-ba97-79ec3b7e0baa.htseq.txt",                 "9c52ed00-325f-4664-8873-327bcaa5ea74.htseq.txt", "fabefb10-5546-4017-8ea1-29982a10fb3c.htseq.txt",                 "32a115cf-570f-4ad9-a123-8e1970062f51.htseq.txt", "05eef9f8-a246-403a-b0be-07d274b6f93a.htseq.txt",                 "5c18c6a8-9ad2-43a8-a3a0-83d8fc0cc257.htseq.txt", "43b292be-5d63-4523-a43f-666d20039208.htseq.txt")
read.delim(COAD_files[1], nrows = 60)
```

## Create dataset, join the 60 loaded txt files
Use edgeR to create a matrix of 60 text files.

#### Known issue: Working directory
Spoke to professor Craig on 12/4 and it is ok to not change the root, just setwd to desktop as my desktop since files were downloaded locally.
```{r}
setwd('~/Desktop/COAD_Data/')
library(edgeR)
x <- readDGE(COAD_files, columns=c(1,2)) #joins my 60 files and creates a dataset
class(x)
dim(x)
```

```{r}
names(x) #accessor function  
str(x) #displays the structure of x in compact way, alternative to summary and best for displaying contents of lists
```

## Annotate the samples
```{r}
x$samples
```

# Organize sample information

## Associate sample-level information with the columns of the counts matrix
```{r}
samplenames <- substring(colnames(x), 1, nchar(colnames(x)))
samplenames
```

## Specify which files are Cystic, Mucinous, and Serous (CMS) and which files are Adenocarcinoma
```{r}
colnames(x) <- samplenames
group <- as.factor(c("CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
                     "CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
                     "CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
                     "CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
                     "CMS", "CMS", "CMS", "CMS", "CMS", "CMS",
                     "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",                                 "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
                     "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
                     "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
                     "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
                     "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
                     "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA", "ADENOCARCINOMA",
                     "ADENOCARCINOMA", "ADENOCARCINOMA"))

x$samples$group <- group
x$samples
DF<-x$samples #for my own visualization purposes
```

# Script to organize gene annotations
{
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("Homo.sapiens")
library(Homo.sapiens)
install.packages(gsubfn)
library(gsubfn)
}

## Script to annotate Genes
First install Homo.sapiens, then use a script remove the decimals and numbers after the decimal points in all 60487 ENSEMBL geneid elements.
```{r}
library(Homo.sapiens)
#library(stringr)
library(gsubfn)
geneid <- rownames(x)
#geneid_test <- c("ENSG00000000005", 
#	"ENSG00000000419",
#	"ENSG00000000457",
#	"ENSG00000000938") 
#geneid <- str_remove(geneid, "[.]") removes decimals only
geneid <- gsub("\\.[0-9]*$", "", geneid) #remove decimals and numbers after decimals
genes <- select(Homo.sapiens, keys=geneid, columns=c("SYMBOL", "TXCHROM"), 
                keytype="ENSEMBL")
head(genes)
```

## Remove duplicate genes
```{r}
genes <- genes[!duplicated(genes$ENSEMBL),]
```

## Package in a DGEList-object containing raw count data with associated sample information and gene annotations
```{r}
x$genes <- genes
x
```

# Ashley E Noriega
# Nov 30, 2019
# TRGN 510 Final Project: Milestone 3
# Running the Glimma Vignette

# Data Pre-processing

## Transformations from the raw-scale: convert raw counts to counts per million (CPM) and log2-counts per million (log-CPM)
```{r}
cpm <- cpm(x)
lcpm <- cpm(x, log=TRUE)
L <- mean(x$samples$lib.size) * 1e-6
M <- median(x$samples$lib.size) * 1e-6
c(L, M)
summary(lcpm)
```

## Remove lowly expressed genes
True signifies how many genes have counts equal to zero, meaning genes are unexpressed throughout all samples. 
```{r}
table(rowSums(x$counts==0)==9)
```

## Filter genes while keeping as many genes as possible with worthwile counts
```{r}
keep.exprs <- filterByExpr(x, group=group)
x <- x[keep.exprs,, keep.lib.sizes=FALSE]
dim(x)
```

## Plot the density of log-CPM values for raw and filtered data
There is a sample that is a potential outlier (green colored line), could remove the sample for future analysis but spoke to porfessor Craig on 12/4 and agreed to leave the sample in since the vignette has a normalisation step.

#### Known issue: color palette
Spoke to professor Craig on 12/4 and agreed to stop working on this issue. I understand that the "Paired" palatte only offers 12 colors so every 13th sample repeats color scheme. I tried increasing the number of colors available with colorRampPalatte but was unsuccesful.
```{r}
lcpm.cutoff <- log2(10/M + 2/L)
library(RColorBrewer)
#library(colorRamps)
nsamples <- ncol(x)
col <- brewer.pal(nsamples, "Paired") #results in the error message: n too large, allowed maximum for palette Paired is 12. Returning the palette you asked for with that many colors
#nb.cols = 60
#col <- colorRampPalette(brewer.pal(nsamples, "Paired"))(nb.cols) #colorRampPalette is a constructor function that builds palettes with arbitrary number of colors by interpolating existing palette 
par(mfrow=c(1,2)) #1 row, 2 columns
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")
title(main="A. Raw data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", samplenames, text.col=col, bty="n")
lcpm <- cpm(x, log=TRUE)
plot(density(lcpm[,1]), col=col[1], lwd=2, ylim=c(0,0.26), las=2, main="", xlab="")
title(main="B. Filtered data", xlab="Log-cpm")
abline(v=lcpm.cutoff, lty=3)
for (i in 2:nsamples){
den <- density(lcpm[,i])
lines(den$x, den$y, col=col[i], lwd=2)
}
legend("topright", samplenames, text.col=col, bty="n")
```

## Normalising gene expression distributions
```{r}
x <- calcNormFactors(x, method = "TMM")
x$samples$norm.factors
```

## Improve visualization by duplicating data, then adjusting the counts
```{r}
x2 <- x
x2$samples$norm.factors <- 1
x2$counts[,1] <- ceiling(x2$counts[,1]*0.05)
x2$counts[,2] <- x2$counts[,2]*5
```

## Boxplot expression distribution of samples for unnormalised data
```{r}
par(mfrow=c(1,1)) #makes boxplot look less cramped 
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="A. Example: Unnormalised data",ylab="Log-cpm")
x2 <- calcNormFactors(x2)  
x2$samples$norm.factors
```

## Boxplot expression distribution of samples for normalised data
This step forces the samples to even out, may not be a good thing since there is a potential outlier.
```{r}
lcpm <- cpm(x2, log=TRUE)
boxplot(lcpm, las=2, col=col, main="")
title(main="B. Example: Normalised data",ylab="Log-cpm")
```

## Unsupervised clustering of cells: make multi-dimensional scaling plot (MDS) to show simmilarities and dissimilarities between samples in an unsupervised manner

#### Known issue: color palette 
I spoke to professor Craig on 12/4, ok to ignore error since I am only comparing 2 different subsets of colon cancer. To get rid of this error I would need to add an additional factor: lane.
```{r}
lcpm <- cpm(x, log=TRUE)
par(mfrow=c(1,1)) #1 row, 1 column 
col.group <- group
levels(col.group) <-  brewer.pal(nlevels(col.group), "Set1") #n= number of different colors in a palette with the min being 3 
col.group <- as.character(col.group)
#col.lane <- lane did not have lanes for my data
#levels(col.lane) <-  brewer.pal(nlevels(col.lane), "Set2")
#col.lane <- as.character(col.lane)
plotMDS(lcpm, labels=group, col=col.group)
title(main="A. Sample groups")
#plotMDS(lcpm, labels=lane, col=col.lane, dim=c(3,4))
#title(main="B. Sequencing lanes")
```

## Make interactive using Glimma
HTML page will be generarted and opened in a browser if launch=TRUE
```{r}
suppressPackageStartupMessages(library(Glimma))
glMDSPlot(lcpm, labels=paste(group, sep="_"), 
          groups=x$samples[,c(1,2)], launch=FALSE)
```

# Differential expression analysis

## Creating a design matrix
```{r}
design <- model.matrix(~0+group) #removes intercept from the factor group
#design <- model.matrix(~group) leaves intercept from factor group, but model contrasts are more straight forward without intercept
colnames(design) <- gsub("group", "", colnames(design))
design
```

## Contrasts for pairwise comparisons between cell populations
Since I am only comparing CMS and Adenocarcinoma, I will only have 1 pairwise comparison.
```{r}
library(limma)
contr.matrix <- makeContrasts(
   ADENOCARCINOMAvsCMS = ADENOCARCINOMA-CMS, 
   levels = colnames(design))
contr.matrix
```

 ## Remove heteroscedascity from count data

### Voom plot
Each black dot represents a gene. The red curve is the estimated mean-varience trend used to compute the voom weights.
```{r}
 par(mfrow=c(1,2))
v <- voom(x, design, plot=TRUE) #voom converts raw counts to log-CPM values by extracting library sizes and normalisation factors from x
v
```

### Apply voom precision weights to data
Each black dot is a gene. The blue line is the average log2 residual standard deviation computed with the Bayes algorithm.
```{r}
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
plotSA(efit, main="Final model: Mean-variance trend") #plots log2 residual standard deviations against mean log-CPM values
```

## Examine the number of DE genes
Quick view at how many genes are down-regulated, up-regulated, and not statistically significant. The adjusted p-value cutoff is 5% by default.
```{r}
summary(decideTests(efit))
```

## Set a minimum log-fold change(log-FC) of 1
This is a stricter definition of significance and could be overcorrecting since now I don't have any down-regulated or up-regulated genes.
```{r}
tfit <- treat(vfit, lfc=1) #p-values calculated from empirical Bayes moderated t-statistics with a minimum log-FC requirement.
dt <- decideTests(tfit)
#dt <- decideTests(efit) #for testing purposes
summary(dt)
```

## Extract genes that are DE in multiple comparisons
I don't have any DE genes if tfit is used. If efit is used, I have 3295 DE genes.
```{r}
de.common <- which(dt[,1]!=0)
length(de.common) 
```

## The first 20 DE genes
If efit is used the genes are: "DPM1", "CFH", "LAS1L", "CFTR", "TMEM176A", "DBNDD1", "TFPI", "SLC7A2", "ARF5", "POLDIP2", "ARHGAP33", "UPF1", "MCUB", "POLR2J", "THSD7A", "LIG3", "SPPL2B", "IBTK", "PDK2", "REX1BD"  
```{r}
head(tfit$genes$SYMBOL[de.common], n=20)
```

## Make Venn Diagram
My diagram only has 1 circle because I only have 1 pairwise comparison.
```{r}
vennDiagram(dt[,1], circle.col=c("turquoise", "salmon"))
```

## Extract and write results for comparisons of ADENOCARCINOMAvsCMS to a single output file
```{r}
write.fit(tfit, dt, file="results.txt")
```

## Examining individual DE genes from top to bottom
```{r}
ADENOCARCINOMA.vs.CMS <- topTreat(tfit, coef=1, n=Inf)
head(ADENOCARCINOMA.vs.CMS)
```

## Summarize results for genes using mean-difference plots that highlight DE genes
If efit is used, will have read, black and blue genes. Since tfit is used, all genes are black.
```{r}
plotMD(tfit, column=1, status=dt[,1], main=colnames(tfit)[1], 
       xlim=c(-8,13))
```

## Make interactive mean-difference plot
To open HTML page in a browser make launch=TRUE
```{r}
library(Glimma)
glMDPlot(tfit, coef=1, status=dt, main=colnames(tfit)[1],
         side.main="ENSEMBL", counts=lcpm, groups=group, launch=FALSE)
```

## Make heatmap
Install heatmap.plus beacuse heatmap.2 did not work for my data.
```{r}
library(gplots)
library(heatmap.plus)
ADENOCARCINOMA.vs.CMS.topgenes <- ADENOCARCINOMA.vs.CMS$ENSEMBL[1:100]
i <- which(v$genes$ENSEMBL %in% ADENOCARCINOMA.vs.CMS.topgenes)
mycol <- colorpanel(1000,"blue","white","red")
#par("mar") OUTPUT SHOULD BE [1] 5.1 4.1 4.1 2.1
par(cex.main=0.8,mar=c(1,1,1,1)) #mar=c(1,1,1,1) ensures margins are large enough
heatmap.plus(lcpm[i,], col=bluered(20),cexRow=1,cexCol=0.2, margins = c(10,10), main = "HeatMap") #changed the margins to have a more legible heatmap
```