TRGN 510 Final Project: Colon Cancer
Final Project
Section 1: Objective
For this project, I will be showing the log2(fold-change) in differential gene expression for RNA-seq data in two subsets of colon cancer: adenocarcinoma and cystic, mucinous and serous neoplasms. The final deliverable will be the the Glimma Vignette. The input files for the Glimma vignette will be HTSeq-count data obtained from The Cancer Genome Atlas Program (TCGA). I will be turning in an HTML containing my R Notebook.
Section 2: Datasets
Loading data
Data is obtained from TCGA. I filtered for RNA-Seq experimental strategy, TXT data format and HTSeq-counts workflow type. HTSeq-counts is a tool that quantifies the aligned reads overlapping a gene’s exons. HTSeq data does not have a header, is tab-delimited, the first column is the Ensembl gene ID and the second column is the number of mapped reads of the gene. The counts will be used in differential gene expression analysis using edgeR as the method. To look at the differential gene expression, the counts will be normalized using the calcNormFactors in edgeR and only reads that unambigously map to one gene are used.
RAW: URL for cystic, mucinous, and serous neoplasms, I will choose 30:
Unit test for data
When data is read in:
ENSG00000000003.13
<fctr>
X5290
<int>
ENSG00000000005.5 47
ENSG00000000419.11 1212
ENSG00000000457.12 1176
ENSG00000000460.15 121
ENSG00000000938.11 166
ENSG00000000971.14 1012
ENSG00000001036.12 4401
ENSG00000001084.9 1977
ENSG00000001167.13 976
ENSG00000001460.16 1638
1-10 of 60 rowsWhen dataset is made using the 60 text files:
[1] "DGEList"
attr(,"package")
[1] "edgeR"
[1] 60487 60Unit test for normalized data
Make a box plots of unnormalized and normalized data
Proposed Analysis
This project will compute and analyze the logarithmic ratio of differential gene expression of two subtypes of colon cancer. EdgeR will be used to import, organize, and normalize the data, Mus.musculus will be used for gene annotions, limma will be used to examine the gene expression anaylsis and make exploratory plots, Glimma will be used to make these plots interactive. RColorBrewer and gplots will be used to make heatmaps.
Flowchart
library(DiagrammeR)
grViz("digraph flowchart {
# node definitions with substituted label text
node [fontname = Helvetica, shape = rectangle]
tab1 [label = '@@1']
tab2 [label = '@@2']
tab3 [label = '@@3']
tab4 [label = '@@4']
tab5 [label = '@@5']
tab6 [label = '@@6']
tab7 [label = '@@7']
tab8 [label = '@@8']
tab9 [label = '@@9']
tab10 [label = '@@10']
# edge definitions with the node IDs
tab1 -> tab2;
tab2 -> tab3;
tab3 -> tab4;
tab4 -> tab8 -> tab5;
tab4 -> tab5 -> tab6 -> tab7 -> tab9 -> tab10
}
[1]: 'Download necessary libraries'
[2]: 'Load and read datasets'
[3]: 'Join datasets'
[4]: 'Unit test: Data was properly loaded?'
[5]: 'Normalize data'
[6]: 'Find Mean Varience Trend'
[7]: 'Analyze DE genes'
[8]: 'Troubleshoot and fix errors'
[9]: 'Make Interactive MDS plot'
[10]: 'Make HeatMap of log-CPM data'
")Loading libraries
Proposed Timeline and Milestones
Week 1: Run the Glimma vignette. I will install the necessary packages in R and understand each step in the vignette.
Week 2: Load in the data (joins, creating datasets) and do a simple, 1 line unit test to look at the data. I will download 60 datasets (30 from each subtype) and join multiple datasets. Emailed Dr. Craig on 11/19 and agreed on turning this milestone in on Sat Nov 23, 2019.
Week 3: Confirm that the data was loaded in correctly and analyze data using the Glimma vignette.Emailed Dr. Craig on 11/26 and agreed on turning this milestone in on 12/1.
Week 4: Troubleshoot for more errors and enhance the user interface.
User Interface
I anticipate having boxplots, heatmaps, and interactive multi-dimensional scaling (MDS) plots done in an R Notebook. I will submit an HTML page of my completed R Notebook.
MDS plot
Make MDS plot interactive
HeatMap of log-CPM data
Ashley E Noriega,
Nov 13, 2019
TRGN 510 Final Project: Milestone 1
A script for setting up the Glimma Vignette
{ if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”) BiocManager::install(“limma”) library(limma)
if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”) BiocManager::install(“Glimma”) library(Glimma)
if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”)
BiocManager::install(“edgeR”) library(edgeR)
if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”)
BiocManager::install(“Mus.musculus”) library(Mus.musculus)
library(R.utils)
if (!requireNamespace(“BiocManager”, quietly = TRUE)) install.packages(“BiocManager”)
BiocManager::install(“CAMERA”) library(CAMERA) }
Data Packaging
Read in count data
Read the 9 text files into R and combining into a matrix of counts
Annotate the samples
Organize sample information
Organize gene annotations
Resolve duplicate gene IDs
Package in a DGEList-object containing raw count data with associated sample information and gene annotations
Data Pre-processing
Transformations from the raw-scale: convert raw counts to counts per million (CPM) and log2-counts per million (log-CPM)
Remove lowly expressed genes
Filter genes while keeping as many genes as possible with worthwile counts
Plot the density of log-CPM values for raw and filtered data
Normalize gene expression distributions
Improve visualization by duplicating data then adjusting the counts
Boxplot expression distribution of samples for normalised and unnormalised data
Unsupervised clustering of cells: make multi-dimensional scaling plot (MDS) to show simmilarities and dissimilarities between samples in an unsupervised manner
Make interactive using Glimma
HTML page will be generarted and opened in a browser if launch=TRUE
Differential Expression Analysis
Create a design matrix
Contrasts for pairwise comparisons between cell populations
Remove heteroscedascity from count data
Apply voom precision weights to data
Examine the number of DE genes
Set a minimum log-fold change(log-FC) of 1
Extract genes that are DE in multiple comparisons
Extract and write results for all 3 comparisons (basalvsLP, basalvsML, and LPvsML) to a single output file
Examine individual DE genes from top to bottom
Summarize results for genes using mean-difference plots that highlight differentially expressed genes
Make interactive mean-difference plot.
To open HTML page in a browser, make launch=TRUE
Make heatmap
Gene set testing by applying the camera method on c2 gene signatures from the Broad Institute’s MSigDB c2 collection
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/')
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/')
library(edgeR)## Loading required package: limmax <- readDGE(COAD_files, columns=c(1,2)) #joins my 60 files and creates a dataset## Meta 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 lists## Formal 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"
## [2] "bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq"
## [3] "5697212f-b3fd-479f-84b0-ec0aae54534a.htseq"
## [4] "7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq"
## [5] "15864159-be88-41c8-bdef-c2c5927cb1a1.htseq"
## [6] "649b19e1-96e2-4b55-951d-3b6ee9f4b91f.htseq"
## [7] "86679663-dfc5-46ad-8cf9-c7954c4b339b.htseq"
## [8] "28004569-048d-4f8c-99aa-7a8c69a98dcc.htseq"
## [9] "911f6378-8a25-4570-9d3b-80f5b5bfc085.htseq"
## [10] "d5dca54e-d7e9-4328-b2ca-1d191a2b8b4c.htseq"
## [11] "f3895ae4-1228-49b3-9342-3c3b86cb5243.htseq"
## [12] "f590941d-19dc-427a-95b6-942c97ea8333.htseq"
## [13] "55aa6d16-3598-42ca-8844-0fe84739ef66.htseq"
## [14] "0e7094cf-4c79-43f4-8b72-9de259e5e18f.htseq"
## [15] "9a62fe1f-36ec-4e8e-b3d9-bdfc62f71905.htseq"
## [16] "d2587070-cb7d-440d-ae49-52f5077248e6.htseq"
## [17] "7800bdb2-aa8b-43e0-8e45-1b968872b34e.htseq"
## [18] "2bcd2efd-4fd6-40ee-86a4-867ae82711b0.htseq"
## [19] "424d8e5f-9fc6-470b-ad2c-b4447b0eb07e.htseq"
## [20] "934f9dc6-1260-4268-b022-870f1e37dd6f.htseq"
## [21] "0fa55c0e-6f8f-44a6-82fe-9a42495d3484.htseq"
## [22] "c8544a8a-4352-438d-94d4-3495af2e9a78.htseq"
## [23] "dade0b16-ecc3-43b3-b328-3819a8fc18c6.htseq"
## [24] "e875ae4e-4645-4e84-b0ff-9c9a694717a9.htseq"
## [25] "debd6982-7c27-42e8-b778-20afcc78a5f3.htseq"
## [26] "17c88994-9e8e-4f16-8c41-34e98a0d8c52.htseq"
## [27] "7f5a924a-ddf3-45ff-be1f-5b5909305f46.htseq"
## [28] "fa73bdce-67fb-42aa-883f-635f0e7bcdc6.htseq"
## [29] "abe20df7-6b97-4397-8864-881bac27e92c.htseq"
## [30] "62f84581-4c7d-4c8e-835c-9304bcec3106.htseq"
## [31] "3abbd2b5-04db-4fe0-8dd1-ea2b48caa4c1.htseq"
## [32] "087666cd-47ae-4f56-b947-d6aa1c25e8a7.htseq"
## [33] "c14f98e2-8e9b-49f4-a244-3d06c6cb7126.htseq"
## [34] "13abc91e-fbfc-4c55-bf54-fbd134979ccc.htseq"
## [35] "6ae2dd6c-2a39-411f-a1fc-11e0e6e82165.htseq"
## [36] "8f77f4f4-b184-40c7-8ab8-2f95b13620b5.htseq"
## [37] "168e5cb2-7390-45ad-ad04-c9aa4416e950.htseq"
## [38] "0ed65bdf-cb92-47c1-8aeb-42518ce639b8.htseq"
## [39] "4e7c6811-88e4-4bb7-a88f-7491dfa6d072.htseq"
## [40] "7fb73a84-867a-4c28-aa02-93068efffb7b.htseq"
## [41] "b53f9a9d-b24d-410d-b3e9-f2a8bf22ca27.htseq"
## [42] "f7ce175f-763e-4a55-97e3-0381d889b0eb.htseq"
## [43] "f346f2d2-285c-455c-ba34-ea8eec3fa881.htseq"
## [44] "e53e1a83-1979-4e12-bbb7-79b37d0cfe03.htseq"
## [45] "a26d49db-2309-46a0-a3ed-275378d484e7.htseq"
## [46] "a3f88a5d-7169-465b-bb80-e5999590681c.htseq"
## [47] "c264fe3b-482b-44ec-83a4-73df565663ff.htseq"
## [48] "bd2dfab3-88a8-4673-ba36-3daf252d0b4d.htseq"
## [49] "7261b656-c79c-4581-a503-15b653e2b5d2.htseq"
## [50] "ee4dcccc-514b-4cc6-ae63-6ed3e7519a40.htseq"
## [51] "f596eabc-e39a-4e35-9fc6-edade04eb785.htseq"
## [52] "bf9c448b-bdc9-4f74-b13a-374e6add7939.htseq"
## [53] "564daa81-cfef-45b6-94a0-3249b2724d9b.htseq"
## [54] "82e00e45-734c-471f-ba97-79ec3b7e0baa.htseq"
## [55] "9c52ed00-325f-4664-8873-327bcaa5ea74.htseq"
## [56] "fabefb10-5546-4017-8ea1-29982a10fb3c.htseq"
## [57] "32a115cf-570f-4ad9-a123-8e1970062f51.htseq"
## [58] "05eef9f8-a246-403a-b0be-07d274b6f93a.htseq"
## [59] "5c18c6a8-9ad2-43a8-a3a0-83d8fc0cc257.htseq"
## [60] "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)## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: BiocGenerics## Loading required package: parallel##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:parallel':
##
## clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
## clusterExport, clusterMap, parApply, parCapply, parLapply,
## parLapplyLB, parRapply, parSapply, parSapplyLB## The following object is masked from 'package:limma':
##
## plotMA## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which, which.max, which.min## Loading required package: Biobase## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.## Loading required package: IRanges## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:base':
##
## expand.grid## Loading required package: OrganismDbi## Loading required package: GenomicFeatures## Loading required package: GenomeInfoDb## Loading required package: GenomicRanges## Loading required package: GO.db## ## Loading required package: org.Hs.eg.db## ## Loading required package: TxDb.Hsapiens.UCSC.hg19.knownGene#library(stringr)
library(gsubfn)## Loading required package: proto## Warning in doTryCatch(return(expr), name, parentenv, handler): unable to load shared object '/Library/Frameworks/R.framework/Resources/modules//R_X11.so':
## dlopen(/Library/Frameworks/R.framework/Resources/modules//R_X11.so, 6): Library not loaded: /opt/X11/lib/libSM.6.dylib
## Referenced from: /Library/Frameworks/R.framework/Resources/modules//R_X11.so
## Reason: image not found## Could not load tcltk. Will use slower R code instead.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
x## An object of class "DGEList"
## $samples
## files
## 9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq 9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq.txt
## bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq.txt
## 5697212f-b3fd-479f-84b0-ec0aae54534a.htseq 5697212f-b3fd-479f-84b0-ec0aae54534a.htseq.txt
## 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq.txt
## 15864159-be88-41c8-bdef-c2c5927cb1a1.htseq 15864159-be88-41c8-bdef-c2c5927cb1a1.htseq.txt
## group lib.size norm.factors
## 9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq CMS 90179803 1
## bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq CMS 36807306 1
## 5697212f-b3fd-479f-84b0-ec0aae54534a.htseq CMS 42963355 1
## 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq CMS 111649651 1
## 15864159-be88-41c8-bdef-c2c5927cb1a1.htseq CMS 36349055 1
## 55 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
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## Tags 62f84581-4c7d-4c8e-835c-9304bcec3106.htseq
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## Samples
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## Samples
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## Samples
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## Samples
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## ENSG00000000005.5 78
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## Samples
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## ENSG00000000005.5 12
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## Samples
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## ENSG00000000005.5 4
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## Samples
## Tags 564daa81-cfef-45b6-94a0-3249b2724d9b.htseq
## ENSG00000000005.5 19
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## Samples
## Tags 82e00e45-734c-471f-ba97-79ec3b7e0baa.htseq
## ENSG00000000005.5 26
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## ENSG00000000457.12 728
## ENSG00000000460.15 626
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## Samples
## Tags 9c52ed00-325f-4664-8873-327bcaa5ea74.htseq
## ENSG00000000005.5 24
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## Samples
## Tags fabefb10-5546-4017-8ea1-29982a10fb3c.htseq
## ENSG00000000005.5 15
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## ENSG00000000457.12 679
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## Samples
## Tags 32a115cf-570f-4ad9-a123-8e1970062f51.htseq
## ENSG00000000005.5 37
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## 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 ...
##
## $genes
## ENSEMBL SYMBOL TXCHROM
## 1 ENSG00000000005 TNMD chrX
## 2 ENSG00000000419 DPM1 chr20
## 3 ENSG00000000457 SCYL3 chr1
## 4 ENSG00000000460 C1orf112 chr1
## 5 ENSG00000000938 FGR chr1
## 60482 more rows ...Ashley E Noriega
Nov 30, 2019
TRGN 510 Final Project: Milestone 3
Running the Glimma Vignette
Data Pre-processing
Transformations from the raw-scale: convert raw counts to counts per million (CPM) and log2-counts per million (log-CPM)
cpm <- cpm(x)
lcpm <- cpm(x, log=TRUE)
L <- mean(x$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
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## 5697212f-b3fd-479f-84b0-ec0aae54534a.htseq
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## 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq
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## 15864159-be88-41c8-bdef-c2c5927cb1a1.htseq
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## 28004569-048d-4f8c-99aa-7a8c69a98dcc.htseq
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## 911f6378-8a25-4570-9d3b-80f5b5bfc085.htseq
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## d5dca54e-d7e9-4328-b2ca-1d191a2b8b4c.htseq
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## f590941d-19dc-427a-95b6-942c97ea8333.htseq
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## 55aa6d16-3598-42ca-8844-0fe84739ef66.htseq
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## 0e7094cf-4c79-43f4-8b72-9de259e5e18f.htseq
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## 9a62fe1f-36ec-4e8e-b3d9-bdfc62f71905.htseq
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## d2587070-cb7d-440d-ae49-52f5077248e6.htseq
## Min. :-5.00536
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## Max. :17.69040
## 7800bdb2-aa8b-43e0-8e45-1b968872b34e.htseq
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## 2bcd2efd-4fd6-40ee-86a4-867ae82711b0.htseq
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## 424d8e5f-9fc6-470b-ad2c-b4447b0eb07e.htseq
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## 934f9dc6-1260-4268-b022-870f1e37dd6f.htseq
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## 0fa55c0e-6f8f-44a6-82fe-9a42495d3484.htseq
## Min. :-5.005
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## Max. :17.739
## c8544a8a-4352-438d-94d4-3495af2e9a78.htseq
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## dade0b16-ecc3-43b3-b328-3819a8fc18c6.htseq
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## e875ae4e-4645-4e84-b0ff-9c9a694717a9.htseq
## Min. :-5.0054
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## debd6982-7c27-42e8-b778-20afcc78a5f3.htseq
## Min. :-5.005
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## 17c88994-9e8e-4f16-8c41-34e98a0d8c52.htseq
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## 7f5a924a-ddf3-45ff-be1f-5b5909305f46.htseq
## Min. :-5.005
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## 3rd Qu.:-0.347
## Max. :18.253
## fa73bdce-67fb-42aa-883f-635f0e7bcdc6.htseq
## Min. :-5.0054
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## Median :-5.0054
## Mean :-2.4626
## 3rd Qu.:-0.7878
## Max. :18.5223
## abe20df7-6b97-4397-8864-881bac27e92c.htseq
## Min. :-5.0054
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## Mean :-2.4819
## 3rd Qu.:-0.6892
## Max. :18.2755
## 62f84581-4c7d-4c8e-835c-9304bcec3106.htseq
## Min. :-5.0054
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## Median :-5.0054
## Mean :-2.4094
## 3rd Qu.:-0.6332
## Max. :18.1225
## 3abbd2b5-04db-4fe0-8dd1-ea2b48caa4c1.htseq
## Min. :-5.0054
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## Mean :-2.3126
## 3rd Qu.:-0.3237
## Max. :17.6880
## 087666cd-47ae-4f56-b947-d6aa1c25e8a7.htseq
## Min. :-5.0054
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## Median :-4.5625
## Mean :-2.4787
## 3rd Qu.:-0.9039
## Max. :18.0167
## c14f98e2-8e9b-49f4-a244-3d06c6cb7126.htseq
## Min. :-5.0054
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## Median :-5.0054
## Mean :-2.5007
## 3rd Qu.:-0.6715
## Max. :18.2731
## 13abc91e-fbfc-4c55-bf54-fbd134979ccc.htseq
## Min. :-5.0054
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## 3rd Qu.:-0.3063
## Max. :17.8027
## 6ae2dd6c-2a39-411f-a1fc-11e0e6e82165.htseq
## Min. :-5.0054
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## Mean :-2.3342
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## Max. :17.8651
## 8f77f4f4-b184-40c7-8ab8-2f95b13620b5.htseq
## Min. :-5.0054
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## Mean :-2.4084
## 3rd Qu.:-0.7874
## Max. :17.8969
## 168e5cb2-7390-45ad-ad04-c9aa4416e950.htseq
## Min. :-5.0054
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## Mean :-2.4576
## 3rd Qu.:-0.6493
## Max. :18.3474
## 0ed65bdf-cb92-47c1-8aeb-42518ce639b8.htseq
## Min. :-5.0054
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## Mean :-2.3667
## 3rd Qu.:-0.5842
## Max. :17.8840
## 4e7c6811-88e4-4bb7-a88f-7491dfa6d072.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-5.0054
## Mean :-2.3788
## 3rd Qu.:-0.5019
## Max. :18.2679
## 7fb73a84-867a-4c28-aa02-93068efffb7b.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-5.0054
## Mean :-2.5250
## 3rd Qu.:-0.8667
## Max. :18.4535
## b53f9a9d-b24d-410d-b3e9-f2a8bf22ca27.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-4.4286
## Mean :-2.3446
## 3rd Qu.:-0.5695
## Max. :17.7747
## f7ce175f-763e-4a55-97e3-0381d889b0eb.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-4.5018
## Mean :-2.3837
## 3rd Qu.:-0.5533
## Max. :18.0439
## f346f2d2-285c-455c-ba34-ea8eec3fa881.htseq
## Min. :-5.005
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## Mean :-2.374
## 3rd Qu.:-0.463
## Max. :18.269
## e53e1a83-1979-4e12-bbb7-79b37d0cfe03.htseq
## Min. :-5.005
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## Median :-5.005
## Mean :-2.494
## 3rd Qu.:-0.882
## Max. :18.606
## a26d49db-2309-46a0-a3ed-275378d484e7.htseq
## Min. :-5.005
## 1st Qu.:-5.005
## Median :-5.005
## Mean :-2.505
## 3rd Qu.:-1.076
## Max. :17.950
## a3f88a5d-7169-465b-bb80-e5999590681c.htseq
## Min. :-5.0054
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## Median :-4.0762
## Mean :-1.9487
## 3rd Qu.: 0.8739
## Max. :18.1564
## c264fe3b-482b-44ec-83a4-73df565663ff.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
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## 3rd Qu.:-0.3071
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## bd2dfab3-88a8-4673-ba36-3daf252d0b4d.htseq
## Min. :-5.0054
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## 7261b656-c79c-4581-a503-15b653e2b5d2.htseq
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## Max. :17.7787
## ee4dcccc-514b-4cc6-ae63-6ed3e7519a40.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-4.6205
## Mean :-2.3583
## 3rd Qu.:-0.4248
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## f596eabc-e39a-4e35-9fc6-edade04eb785.htseq
## Min. :-5.005
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## Mean :-2.635
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## Max. :18.298
## bf9c448b-bdc9-4f74-b13a-374e6add7939.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-5.0054
## Mean :-2.5172
## 3rd Qu.:-0.9961
## Max. :18.2876
## 564daa81-cfef-45b6-94a0-3249b2724d9b.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-5.0054
## Mean :-2.5218
## 3rd Qu.:-0.6848
## Max. :18.2312
## 82e00e45-734c-471f-ba97-79ec3b7e0baa.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-4.5039
## Mean :-2.4147
## 3rd Qu.:-0.7359
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## 9c52ed00-325f-4664-8873-327bcaa5ea74.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-5.0054
## Mean :-2.3284
## 3rd Qu.:-0.2542
## Max. :18.0456
## fabefb10-5546-4017-8ea1-29982a10fb3c.htseq
## Min. :-5.005
## 1st Qu.:-5.005
## Median :-4.399
## Mean :-2.282
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## Max. :17.898
## 32a115cf-570f-4ad9-a123-8e1970062f51.htseq
## Min. :-5.00536
## 1st Qu.:-5.00536
## Median :-4.51407
## Mean :-2.18955
## 3rd Qu.:-0.05016
## Max. :17.82851
## 05eef9f8-a246-403a-b0be-07d274b6f93a.htseq
## Min. :-5.005
## 1st Qu.:-5.005
## Median :-5.005
## Mean :-2.610
## 3rd Qu.:-1.036
## Max. :18.150
## 5c18c6a8-9ad2-43a8-a3a0-83d8fc0cc257.htseq
## Min. :-5.0054
## 1st Qu.:-5.0054
## Median :-5.0054
## Mean :-2.4734
## 3rd Qu.:-0.6384
## Max. :18.7800
## 43b292be-5d63-4523-a43f-666d20039208.htseq
## Min. :-5.0054
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## Median :-5.0054
## Mean :-2.4267
## 3rd Qu.:-0.6233
## Max. :18.3746Remove 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 413Filter 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 colors## Warning in brewer.pal(nsamples, "Paired"): 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
x <- calcNormFactors(x, method = "TMM")
x$samples$norm.factors## [1] 0.8247877 0.8701067 0.9744718 0.3338411 1.0672340 0.9853437 0.8355374
## [8] 1.0685271 1.1041480 1.1621714 1.3190864 1.1616288 0.5656024 1.0079779
## [15] 1.1311621 1.3455788 0.2548806 1.1728192 0.5164198 0.4158854 1.1552465
## [22] 1.0977806 1.1538364 1.0380711 0.5054850 1.2962443 1.1705252 0.9900169
## [29] 0.9259593 1.1222135 1.2985756 1.0598233 0.9471138 1.3391984 1.3043419
## [36] 1.1144424 1.0697018 1.1921660 1.1054413 0.9399911 1.2384865 1.2243347
## [43] 1.0760378 0.9933192 1.1164186 1.4176459 1.3689476 0.8989757 1.3130426
## [50] 1.0789261 0.7851273 1.0110826 0.9751891 1.1994225 1.2667583 1.3476310
## [57] 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
## [7] 0.91165862 1.07196030 1.10500025 1.21054683 1.28772624 1.16063702
## [13] 0.60882746 1.04998828 1.13203522 1.31787908 0.28023587 1.16142488
## [19] 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
## [31] 1.28315406 1.05679584 0.99263908 1.36510564 1.33873108 1.14293994
## [37] 1.10193770 1.21035319 1.10148876 0.97629109 1.25905436 1.25325781
## [43] 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
## [55] 1.27684217 1.35509884 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 ## Warning in brewer.pal(nlevels(col.group), "Set1"): 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
library(Glimma)
glMDSPlot(lcpm, labels=paste(group, sep="_"),
groups=x$samples[,c(1,2)], launch=TRUE)Differential expression analysis
Creating a design matrix
design <- model.matrix(~0+group) #removes intercept from the factor group
#design <- model.matrix(~group) leaves intercept from factor group, but model contrasts are more straight forward without intercept
colnames(design) <- gsub("group", "", colnames(design))
design## ADENOCARCINOMA CMS
## 1 0 1
## 2 0 1
## 3 0 1
## 4 0 1
## 5 0 1
## 6 0 1
## 7 0 1
## 8 0 1
## 9 0 1
## 10 0 1
## 11 0 1
## 12 0 1
## 13 0 1
## 14 0 1
## 15 0 1
## 16 0 1
## 17 0 1
## 18 0 1
## 19 0 1
## 20 0 1
## 21 0 1
## 22 0 1
## 23 0 1
## 24 0 1
## 25 0 1
## 26 0 1
## 27 0 1
## 28 0 1
## 29 0 1
## 30 0 1
## 31 1 0
## 32 1 0
## 33 1 0
## 34 1 0
## 35 1 0
## 36 1 0
## 37 1 0
## 38 1 0
## 39 1 0
## 40 1 0
## 41 1 0
## 42 1 0
## 43 1 0
## 44 1 0
## 45 1 0
## 46 1 0
## 47 1 0
## 48 1 0
## 49 1 0
## 50 1 0
## 51 1 0
## 52 1 0
## 53 1 0
## 54 1 0
## 55 1 0
## 56 1 0
## 57 1 0
## 58 1 0
## 59 1 0
## 60 1 0
## attr(,"assign")
## [1] 1 1
## attr(,"contrasts")
## attr(,"contrasts")$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 -1Remove 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
v## An object of class "EList"
## $genes
## ENSEMBL SYMBOL TXCHROM
## 1 ENSG00000000005 TNMD chrX
## 2 ENSG00000000419 DPM1 chr20
## 3 ENSG00000000457 SCYL3 chr1
## 4 ENSG00000000460 C1orf112 chr1
## 5 ENSG00000000938 FGR chr1
## 19100 more rows ...
##
## $targets
## files
## 9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq 9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq.txt
## bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq.txt
## 5697212f-b3fd-479f-84b0-ec0aae54534a.htseq 5697212f-b3fd-479f-84b0-ec0aae54534a.htseq.txt
## 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq.txt
## 15864159-be88-41c8-bdef-c2c5927cb1a1.htseq 15864159-be88-41c8-bdef-c2c5927cb1a1.htseq.txt
## group lib.size norm.factors
## 9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq CMS 74292479 0.8247877
## bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq CMS 31987425 0.8701067
## 5697212f-b3fd-479f-84b0-ec0aae54534a.htseq CMS 41835745 0.9744718
## 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq CMS 37079196 0.3338411
## 15864159-be88-41c8-bdef-c2c5927cb1a1.htseq CMS 38748737 1.0672340
## 55 more rows ...
##
## $E
## Samples
## Tags 9e8b528b-1172-4c07-a09b-ebb23cf2310c.htseq
## ENSG00000000005.5 -0.6452887
## ENSG00000000419.11 4.0286248
## ENSG00000000457.12 3.9851413
## ENSG00000000460.15 0.7096682
## ENSG00000000938.11 1.1642341
## Samples
## Tags bda1a9a4-a14f-4463-81d2-a4fcca65d6f1.htseq
## ENSG00000000005.5 -2.829508
## ENSG00000000419.11 4.473258
## ENSG00000000457.12 2.886263
## ENSG00000000460.15 2.725081
## ENSG00000000938.11 2.134993
## Samples
## Tags 5697212f-b3fd-479f-84b0-ec0aae54534a.htseq
## ENSG00000000005.5 -4.064736
## ENSG00000000419.11 4.069690
## ENSG00000000457.12 3.723166
## ENSG00000000460.15 2.937516
## ENSG00000000938.11 1.160230
## Samples
## Tags 7f9a629b-12ed-48cc-8d5c-1c2f5db9cf1f.htseq
## ENSG00000000005.5 -1.820221
## ENSG00000000419.11 3.544018
## ENSG00000000457.12 4.437616
## ENSG00000000460.15 4.029445
## ENSG00000000938.11 3.674683
## Samples
## Tags 15864159-be88-41c8-bdef-c2c5927cb1a1.htseq
## ENSG00000000005.5 -3.468722
## ENSG00000000419.11 4.049228
## ENSG00000000457.12 3.007011
## ENSG00000000460.15 2.235675
## ENSG00000000938.11 1.751829
## Samples
## Tags 649b19e1-96e2-4b55-951d-3b6ee9f4b91f.htseq
## ENSG00000000005.5 -1.1743179
## ENSG00000000419.11 5.1369998
## ENSG00000000457.12 2.9131449
## ENSG00000000460.15 2.6294792
## ENSG00000000938.11 0.6819466
## Samples
## Tags 86679663-dfc5-46ad-8cf9-c7954c4b339b.htseq
## ENSG00000000005.5 -2.786013
## ENSG00000000419.11 5.199731
## ENSG00000000457.12 4.248928
## ENSG00000000460.15 3.844348
## ENSG00000000938.11 3.108387
## Samples
## Tags 28004569-048d-4f8c-99aa-7a8c69a98dcc.htseq
## ENSG00000000005.5 -1.553263
## ENSG00000000419.11 4.371787
## ENSG00000000457.12 2.924414
## ENSG00000000460.15 2.663967
## ENSG00000000938.11 2.643134
## Samples
## Tags 911f6378-8a25-4570-9d3b-80f5b5bfc085.htseq
## ENSG00000000005.5 -5.2805208
## ENSG00000000419.11 4.3152962
## ENSG00000000457.12 2.9396157
## ENSG00000000460.15 2.2570859
## ENSG00000000938.11 0.5112933
## Samples
## Tags d5dca54e-d7e9-4328-b2ca-1d191a2b8b4c.htseq
## ENSG00000000005.5 -2.408949
## ENSG00000000419.11 5.250282
## ENSG00000000457.12 4.301365
## ENSG00000000460.15 3.473694
## ENSG00000000938.11 2.114612
## Samples
## Tags f3895ae4-1228-49b3-9342-3c3b86cb5243.htseq
## ENSG00000000005.5 -2.674378
## ENSG00000000419.11 4.258048
## ENSG00000000457.12 3.219863
## ENSG00000000460.15 1.996700
## ENSG00000000938.11 3.869207
## Samples
## Tags f590941d-19dc-427a-95b6-942c97ea8333.htseq
## ENSG00000000005.5 -6.376198
## ENSG00000000419.11 4.130606
## ENSG00000000457.12 3.101561
## ENSG00000000460.15 2.533696
## ENSG00000000938.11 3.187952
## Samples
## Tags 55aa6d16-3598-42ca-8844-0fe84739ef66.htseq
## ENSG00000000005.5 -5.645779
## ENSG00000000419.11 5.295513
## ENSG00000000457.12 3.570967
## ENSG00000000460.15 3.454883
## ENSG00000000938.11 2.374738
## Samples
## Tags 0e7094cf-4c79-43f4-8b72-9de259e5e18f.htseq
## ENSG00000000005.5 -1.143810
## ENSG00000000419.11 3.241950
## ENSG00000000457.12 2.041301
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## ENSG00000000938.11 3.788053
## Samples
## Tags 9a62fe1f-36ec-4e8e-b3d9-bdfc62f71905.htseq
## ENSG00000000005.5 -2.844269
## ENSG00000000419.11 4.802950
## ENSG00000000457.12 3.394772
## ENSG00000000460.15 2.854320
## ENSG00000000938.11 2.128424
## Samples
## Tags d2587070-cb7d-440d-ae49-52f5077248e6.htseq
## ENSG00000000005.5 0.06657207
## ENSG00000000419.11 4.12233363
## ENSG00000000457.12 2.89854696
## ENSG00000000460.15 1.25377506
## ENSG00000000938.11 4.15471639
## Samples
## Tags 7800bdb2-aa8b-43e0-8e45-1b968872b34e.htseq
## ENSG00000000005.5 -3.518050
## ENSG00000000419.11 4.399620
## ENSG00000000457.12 2.911566
## ENSG00000000460.15 3.157201
## ENSG00000000938.11 2.996072
## Samples
## Tags 2bcd2efd-4fd6-40ee-86a4-867ae82711b0.htseq
## ENSG00000000005.5 -2.288229
## ENSG00000000419.11 4.029531
## ENSG00000000457.12 2.762875
## ENSG00000000460.15 1.912198
## ENSG00000000938.11 2.328744
## Samples
## Tags 424d8e5f-9fc6-470b-ad2c-b4447b0eb07e.htseq
## ENSG00000000005.5 -3.874298
## ENSG00000000419.11 4.834002
## ENSG00000000457.12 4.163623
## ENSG00000000460.15 3.365842
## ENSG00000000938.11 4.758697
## Samples
## Tags 934f9dc6-1260-4268-b022-870f1e37dd6f.htseq
## ENSG00000000005.5 -1.706603
## ENSG00000000419.11 4.063307
## ENSG00000000457.12 3.788035
## ENSG00000000460.15 3.419091
## ENSG00000000938.11 2.599558
## Samples
## Tags 0fa55c0e-6f8f-44a6-82fe-9a42495d3484.htseq
## ENSG00000000005.5 -4.763380
## ENSG00000000419.11 3.898399
## ENSG00000000457.12 2.786598
## ENSG00000000460.15 1.465439
## ENSG00000000938.11 2.976738
## Samples
## Tags c8544a8a-4352-438d-94d4-3495af2e9a78.htseq
## ENSG00000000005.5 2.2408036
## ENSG00000000419.11 5.2673893
## ENSG00000000457.12 3.0724378
## ENSG00000000460.15 2.2930928
## ENSG00000000938.11 0.1175401
## Samples
## Tags dade0b16-ecc3-43b3-b328-3819a8fc18c6.htseq
## ENSG00000000005.5 -4.544553
## ENSG00000000419.11 4.505505
## ENSG00000000457.12 2.470112
## ENSG00000000460.15 1.787053
## ENSG00000000938.11 2.555099
## Samples
## Tags e875ae4e-4645-4e84-b0ff-9c9a694717a9.htseq
## ENSG00000000005.5 -5.921106
## ENSG00000000419.11 4.643996
## ENSG00000000457.12 2.952338
## ENSG00000000460.15 2.939981
## ENSG00000000938.11 3.460437
## Samples
## Tags debd6982-7c27-42e8-b778-20afcc78a5f3.htseq
## ENSG00000000005.5 -7.372309
## ENSG00000000419.11 4.197072
## ENSG00000000457.12 3.391733
## ENSG00000000460.15 1.938303
## ENSG00000000938.11 2.667980
## Samples
## Tags 17c88994-9e8e-4f16-8c41-34e98a0d8c52.htseq
## ENSG00000000005.5 -3.003489
## ENSG00000000419.11 4.346009
## ENSG00000000457.12 2.818355
## ENSG00000000460.15 1.819463
## ENSG00000000938.11 3.009197
## Samples
## Tags 7f5a924a-ddf3-45ff-be1f-5b5909305f46.htseq
## ENSG00000000005.5 -1.751115
## ENSG00000000419.11 4.290426
## ENSG00000000457.12 3.102534
## ENSG00000000460.15 2.617107
## ENSG00000000938.11 1.849445
## Samples
## Tags fa73bdce-67fb-42aa-883f-635f0e7bcdc6.htseq
## ENSG00000000005.5 -2.408306
## ENSG00000000419.11 4.410370
## ENSG00000000457.12 2.768674
## ENSG00000000460.15 2.449675
## ENSG00000000938.11 2.846306
## Samples
## Tags abe20df7-6b97-4397-8864-881bac27e92c.htseq
## ENSG00000000005.5 -3.366592
## ENSG00000000419.11 3.442602
## ENSG00000000457.12 3.241795
## ENSG00000000460.15 3.216222
## ENSG00000000938.11 3.246014
## Samples
## Tags 62f84581-4c7d-4c8e-835c-9304bcec3106.htseq
## ENSG00000000005.5 -5.454801
## ENSG00000000419.11 4.120738
## ENSG00000000457.12 2.446066
## ENSG00000000460.15 2.284417
## ENSG00000000938.11 1.600482
## Samples
## Tags 3abbd2b5-04db-4fe0-8dd1-ea2b48caa4c1.htseq
## ENSG00000000005.5 -5.844178
## ENSG00000000419.11 5.073443
## ENSG00000000457.12 3.085574
## ENSG00000000460.15 2.520686
## ENSG00000000938.11 4.420656
## Samples
## Tags 087666cd-47ae-4f56-b947-d6aa1c25e8a7.htseq
## ENSG00000000005.5 -1.37511256
## ENSG00000000419.11 5.29828123
## ENSG00000000457.12 3.64998907
## ENSG00000000460.15 2.96936577
## ENSG00000000938.11 -0.08112134
## Samples
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## 19100 more rows ...
##
## $weights
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## 19100 more rows ...
##
## $design
## ADENOCARCINOMA CMS
## 1 0 1
## 2 0 1
## 3 0 1
## 4 0 1
## 5 0 1
## 55 more rows ...
Apply voom precision weights to data
Each black dot is a gene. The blue line is the average log2 residual standard deviation computed with the Bayes algorithm.
vfit <- lmFit(v, design)
vfit <- contrasts.fit(vfit, contrasts=contr.matrix)
efit <- eBayes(vfit)
plotSA(efit, main="Final model: Mean-variance trend") #plots log2 residual standard deviations against mean log-CPM values
Examine the number of DE genes
Quick view at how many genes are down-regulated, up-regulated, and not statistically significant. The adjusted p-value cutoff is 5% by default.
summary(decideTests(efit))## ADENOCARCINOMAvsCMS
## Down 1474
## NotSig 15810
## Up 1821Set a minimum log-fold change(log-FC) of 1
This is a stricter definition of significance and could be overcorrecting since now I don’t have any down-regulated or up-regulated genes.
tfit <- treat(vfit, lfc=1) #p-values calculated from empirical Bayes moderated t-statistics with a minimum log-FC requirement.
dt <- decideTests(tfit)
#dt <- decideTests(efit) #for testing purposes
summary(dt)## ADENOCARCINOMAvsCMS
## Down 0
## NotSig 19105
## Up 0Extract genes that are DE in multiple comparisons
I don’t have any DE genes if tfit is used. If efit is used, I have 3295 DE genes.
de.common <- which(dt[,1]!=0)
length(de.common) ## [1] 0The first 20 DE genes
If efit is used the genes are: “DPM1”, “CFH”, “LAS1L”, “CFTR”, “TMEM176A”, “DBNDD1”, “TFPI”, “SLC7A2”, “ARF5”, “POLDIP2”, “ARHGAP33”, “UPF1”, “MCUB”, “POLR2J”, “THSD7A”, “LIG3”, “SPPL2B”, “IBTK”, “PDK2”, “REX1BD”
head(tfit$genes$SYMBOL[de.common], n=20)## character(0)Make Venn Diagram
My diagram only has 1 circle because I only have 1 pairwise comparison.
vennDiagram(dt[,1], circle.col=c("turquoise", "salmon"))
Extract and write results for comparisons of ADENOCARCINOMAvsCMS to a single output file
write.fit(tfit, dt, file="results.txt")Examining individual DE genes from top to bottom
ADENOCARCINOMA.vs.CMS <- topTreat(tfit, coef=1, n=Inf)
head(ADENOCARCINOMA.vs.CMS)Summarize results for genes using mean-difference plots that highlight DE genes
If efit is used, will have read, black and blue genes. Since tfit is used, all genes are black.
plotMD(tfit, column=1, status=dt[,1], main=colnames(tfit)[1],
xlim=c(-8,13))
Make interactive mean-difference plot
To open HTML page in a browser make launch=TRUE
library(Glimma)
glMDPlot(tfit, coef=1, status=dt, main=colnames(tfit)[1],
side.main="ENSEMBL", counts=lcpm, groups=group, launch=TRUE)Make heatmap
Install heatmap.plus beacuse heatmap.2 did not work for my data.
library(gplots)##
## Attaching package: 'gplots'## The following object is masked from 'package:IRanges':
##
## space## The following object is masked from 'package:S4Vectors':
##
## space## The following object is masked from 'package:stats':
##
## lowesslibrary(heatmap.plus)
ADENOCARCINOMA.vs.CMS.topgenes <- ADENOCARCINOMA.vs.CMS$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
Gene set testing with Camera
I spoke to professor Craig on 12/4 and agreed that this step would not work for me becuase I did not have differentlially expressed genes.
Ashley E Noriega,
Dec 6, 2019
TRGN 510 Final Project: Milestone 4
HTML containing Knitted R Notebook
Known Issues: Interactive plots
I spoke to professor Craig on 12/6 and the interactive plots are not embedded in RPubs.