Final_Project
Landyn Jones
2025-11-13
knitr::opts_chunk$set(error = TRUE)Data wrangling with R
Tibbles
Load in data:
library(tibble)Tibbles are modified data frames; tweak some of the older features
as_tibble(iris)## # A tibble: 150 × 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fct>
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## # ℹ 140 more rowsAs we can see, we have the same data frame, but we have different features.
We can also create from scratch:
tibble(
x = 1:5,
y = 1,
z = x ^ 2 + y
)## # A tibble: 5 × 3
## x y z
## <int> <dbl> <dbl>
## 1 1 1 2
## 2 2 1 5
## 3 3 1 10
## 4 4 1 17
## 5 5 1 26You can also use tribble for basic data table creation
tribble(
~ genea, ~ geneb, ~ genec,
#########################
110, 112, 114,
6, 5, 4
)## # A tibble: 2 × 3
## genea geneb genec
## <dbl> <dbl> <dbl>
## 1 110 112 114
## 2 6 5 4Tibbles are built to not overwhelm you console when printing data. only showing the first lines.
TidyR
Load in data:
library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ purrr 1.0.2
## ✔ forcats 1.0.0 ✔ readr 2.1.4
## ✔ ggplot2 3.4.4 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsTidy verse follows, each variable must have own column, each obs. has own row, each value has own cell.
Picking one consistent method of data sotrage makes for easier understanding
bmi <- tibble(women)
bmi %>%
mutate(bmi = (703 * weight)/(height)^2)## # A tibble: 15 × 3
## height weight bmi
## <dbl> <dbl> <dbl>
## 1 58 115 24.0
## 2 59 117 23.6
## 3 60 120 23.4
## 4 61 123 23.2
## 5 62 126 23.0
## 6 63 129 22.8
## 7 64 132 22.7
## 8 65 135 22.5
## 9 66 139 22.4
## 10 67 142 22.2
## 11 68 146 22.2
## 12 69 150 22.1
## 13 70 154 22.1
## 14 71 159 22.2
## 15 72 164 22.2Separate and Gather
Sometimes you’ll find data sets that wont fit well into a tibble:
table4a## # A tibble: 3 × 3
## country `1999` `2000`
## <chr> <dbl> <dbl>
## 1 Afghanistan 745 2666
## 2 Brazil 37737 80488
## 3 China 212258 213766We have one variable in A (country), b and c are two of the same.
To fix, we can use the gather function.
table4a %>%
gather('1999', '2000', key = 'year', value = 'cases')## # A tibble: 6 × 3
## country year cases
## <chr> <chr> <dbl>
## 1 Afghanistan 1999 745
## 2 Brazil 1999 37737
## 3 China 1999 212258
## 4 Afghanistan 2000 2666
## 5 Brazil 2000 80488
## 6 China 2000 213766If we want to join two tables we can use dplyr.
table4a <- table4a %>%
gather('1999', '2000', key = 'year', value = 'cases')
table4b <- table4b %>%
gather("1999", "2000", key = "year", value = "population")
left_join(table4a, table4b)## Joining with `by = join_by(country, year)`## # A tibble: 6 × 4
## country year cases population
## <chr> <chr> <dbl> <dbl>
## 1 Afghanistan 1999 745 19987071
## 2 Brazil 1999 37737 172006362
## 3 China 1999 212258 1272915272
## 4 Afghanistan 2000 2666 20595360
## 5 Brazil 2000 80488 174504898
## 6 China 2000 213766 1280428583Spreading - opposite of gathring
table2 ## # A tibble: 12 × 4
## country year type count
## <chr> <dbl> <chr> <dbl>
## 1 Afghanistan 1999 cases 745
## 2 Afghanistan 1999 population 19987071
## 3 Afghanistan 2000 cases 2666
## 4 Afghanistan 2000 population 20595360
## 5 Brazil 1999 cases 37737
## 6 Brazil 1999 population 172006362
## 7 Brazil 2000 cases 80488
## 8 Brazil 2000 population 174504898
## 9 China 1999 cases 212258
## 10 China 1999 population 1272915272
## 11 China 2000 cases 213766
## 12 China 2000 population 1280428583spread(table2, key = type, value = count)## # A tibble: 6 × 4
## country year cases population
## <chr> <dbl> <dbl> <dbl>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583“Type” is the key of the column of what we are turning it into.
Separating and Pull
What happens if we have two obs stuck in one column
table3## # A tibble: 6 × 3
## country year rate
## <chr> <dbl> <chr>
## 1 Afghanistan 1999 745/19987071
## 2 Afghanistan 2000 2666/20595360
## 3 Brazil 1999 37737/172006362
## 4 Brazil 2000 80488/174504898
## 5 China 1999 212258/1272915272
## 6 China 2000 213766/1280428583The rate is just the population and cases combined; we can use separate to fix.
table3%>%
separate(rate, into = c("cases", "population"))## # A tibble: 6 × 4
## country year cases population
## <chr> <dbl> <chr> <chr>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583The column type is not correct.
table3%>%
separate(rate, into = c("cases", "populate"), convert = TRUE)## # A tibble: 6 × 4
## country year cases populate
## <chr> <dbl> <int> <int>
## 1 Afghanistan 1999 745 19987071
## 2 Afghanistan 2000 2666 20595360
## 3 Brazil 1999 37737 172006362
## 4 Brazil 2000 80488 174504898
## 5 China 1999 212258 1272915272
## 6 China 2000 213766 1280428583Unite
What happens if we want to do the inverse of separate?
table5## # A tibble: 6 × 4
## country century year rate
## <chr> <chr> <chr> <chr>
## 1 Afghanistan 19 99 745/19987071
## 2 Afghanistan 20 00 2666/20595360
## 3 Brazil 19 99 37737/172006362
## 4 Brazil 20 00 80488/174504898
## 5 China 19 99 212258/1272915272
## 6 China 20 00 213766/1280428583table5 %>%
unite(date, century, year)## # A tibble: 6 × 3
## country date rate
## <chr> <chr> <chr>
## 1 Afghanistan 19_99 745/19987071
## 2 Afghanistan 20_00 2666/20595360
## 3 Brazil 19_99 37737/172006362
## 4 Brazil 20_00 80488/174504898
## 5 China 19_99 212258/1272915272
## 6 China 20_00 213766/1280428583table5 %>%
unite(date, century, year, sep = "")## # A tibble: 6 × 3
## country date rate
## <chr> <chr> <chr>
## 1 Afghanistan 1999 745/19987071
## 2 Afghanistan 2000 2666/20595360
## 3 Brazil 1999 37737/172006362
## 4 Brazil 2000 80488/174504898
## 5 China 1999 212258/1272915272
## 6 China 2000 213766/1280428583Missing Values
If theres a value missing:
gene_data <- tibble(
gene = c( 'a', 'a', 'a', 'a', 'b', 'b', 'b'),
nuc = c(20, 22, 24, 25, NA, 42, 67),
run = c(1, 2, 3, 4, 2, 3, 4)
)DIPLYR
It is rare that you will be working with a single data table; the DIPLY package allows you to join two tbls based on common values. Mutate joins - add new variables t one data frame from the matching pberservations in another. Filtering joins - filters observation from one data frame based on presence in another. Set operations - treats observations as they are set elements.
Load data:
library(tidyverse)
library(nycflights13)Lets pull full carier names based on letter codes:
airlines## # A tibble: 16 × 2
## carrier name
## <chr> <chr>
## 1 9E Endeavor Air Inc.
## 2 AA American Airlines Inc.
## 3 AS Alaska Airlines Inc.
## 4 B6 JetBlue Airways
## 5 DL Delta Air Lines Inc.
## 6 EV ExpressJet Airlines Inc.
## 7 F9 Frontier Airlines Inc.
## 8 FL AirTran Airways Corporation
## 9 HA Hawaiian Airlines Inc.
## 10 MQ Envoy Air
## 11 OO SkyWest Airlines Inc.
## 12 UA United Air Lines Inc.
## 13 US US Airways Inc.
## 14 VX Virgin America
## 15 WN Southwest Airlines Co.
## 16 YV Mesa Airlines Inc.Stringr
Load in data:
library(tidyverse)
library(stringr) You can create strings using single or double quotes
string1 <- "this is a string"
string2 <- 'to put a "quote" in your string, use the opposite'
string1## [1] "this is a string"string2## [1] "to put a \"quote\" in your string, use the opposite"If you forget to close your string, you’ll get this:
string3 <- "where is this string going?"
string3## [1] "where is this string going?"Multiple strings are stored in character vectors
string4 <- c("one", "two", "three")
string4## [1] "one" "two" "three"Measuring string length
str_length(string3)## [1] 27str_length(string4)## [1] 3 3 5You can use “sep” to control how they are separated
str_c(string1, string2, sep = " ")## [1] "this is a string to put a \"quote\" in your string, use the opposite"str_c("x", "y", "z", sep = "_")## [1] "x_y_z"Subsetting Strings
You can subset a string using str_sub()
HSP <- c("HSP123", "HSP234", "HSP456")
str_sub(HSP, 4, 6)## [1] "123" "234" "456"This just drops the first four letters from the string, or you can use negative to cound back from the end
str_sub(HSP, -3, -1)## [1] "123" "234" "456"You can convert the cases of strings like follows:
HSP## [1] "HSP123" "HSP234" "HSP456"str_to_lower(HSP)## [1] "hsp123" "hsp234" "hsp456"Missing values
What should you do if a value is missing?
gene_data <- tibble(
gene = c( 'a', 'a', 'a', 'a', 'b', 'b', 'b'),
nuc = c(20, 22, 24, 25, NA, 42, 67),
run = c(1, 2, 3, 4, 2, 3, 4)
)You can also use “gene_data” The nucleotide count for Gene B run 2 is explicitly missing; the nucleotide count for Gene B run 1 is inplicitly missing. We can make implicit missing values explicit. If we want to remove the missing values, we can spread and and gather, and na.rm = TRUE:
gene_data %>%
spread(gene, nuc) %>%
gather (gener, nuc, 'a': 'b', na.rm = TRUE)## # A tibble: 6 × 3
## run gener nuc
## <dbl> <chr> <dbl>
## 1 1 a 20
## 2 2 a 22
## 3 3 a 24
## 4 4 a 25
## 5 3 b 42
## 6 4 b 67Another way we can make implicit values explicit, is complete ()
gene_data %>%
complete(gene, run)## # A tibble: 8 × 3
## gene run nuc
## <chr> <dbl> <dbl>
## 1 a 1 20
## 2 a 2 22
## 3 a 3 24
## 4 a 4 25
## 5 b 1 NA
## 6 b 2 NA
## 7 b 3 42
## 8 b 4 67Microarrays Analysis
Load in data:
library(ath1121501cdf)## Warning: replacing previous import 'AnnotationDbi::tail' by 'utils::tail' when
## loading 'ath1121501cdf'## Warning: replacing previous import 'AnnotationDbi::head' by 'utils::head' when
## loading 'ath1121501cdf'## library(ath1121501.db)## Loading required package: AnnotationDbi## Loading required package: stats4## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:lubridate':
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## 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.max, which.min## Loading required package: Biobase## Welcome to Bioconductor
##
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## 'browseVignettes()'. To cite Bioconductor, see
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## expand, smithslibrary(affy)Read cell files, set directory, and put into directory:
setwd("~/Desktop/classroom/myfiles")
targets <- readTargets("Bric16_Targets.csv", sep = ",", row.names = "filename")
ab <- ReadAffy()
hist(ab)
Normalizing the microarray experiment
eset <- affy::rma(ab)## Background correcting
## Normalizing
## Calculating ExpressionpData(eset)## sample
## Atha_Ler-0_sShoots_FLT_Rep1_F-F2_raw.CEL 1
## Atha_Ler-0_sShoots_FLT_Rep2_F-F3_raw.CEL 2
## Atha_Ler-0_sShoots_FLT_Rep3_F-F4_raw.CEL 3
## Atha_Ler-0_sShoots_GC_Rep1_H-F2_raw.CEL 4
## Atha_Ler-0_sShoots_GC_Rep2_H-F3_raw.CEL 5
## Atha_Ler-0_sShoots_GC_Rep3_H-F4_raw.CEL 6Lets vizualize the normalized data:
hist(eset)
Now, lets characterize the data:
ID <- featureNames(eset)
Symbol <- getSYMBOL(ID, "ath1121501.db")
affydata <- read.delim("AFFY_ATH1_array_elements.txt")## Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec,
## : EOF within quoted stringDifferential Gene Expression Analysis
Flight vs Ground
condition <- targets$gravity
design <- model.matrix(~ factor (condition))
colnames(design) <- c("flight", "ground")
fit <- lmFit(eset, design)
fit <- eBayes(fit)
options(digits = 2)
top <- topTable(fit, coef = 2, n = Inf, adjust = 'fdr')
top## logFC AveExpr t P.Value adj.P.Val B
## 264005_at -3.461 4.27 -20.16 1.4e-07 0.0022 7.6386
## 266790_at 3.533 3.89 19.10 2.1e-07 0.0022 7.3784
## 266865_at 3.271 5.13 17.06 4.6e-07 0.0022 6.8015
## 248337_at -6.969 6.13 -15.84 7.7e-07 0.0022 6.4000
## 252543_at 2.092 4.17 15.67 8.3e-07 0.0022 6.3401
## 248179_at 2.106 4.53 15.05 1.1e-06 0.0022 6.1122
## 259839_at 3.139 4.76 14.97 1.1e-06 0.0022 6.0836
## 260297_at 2.608 3.35 14.92 1.2e-06 0.0022 6.0629
## 246475_at 2.188 4.55 14.86 1.2e-06 0.0022 6.0408
## 263498_at 2.704 4.62 14.78 1.2e-06 0.0022 6.0107
## 264580_at -3.174 7.34 -14.46 1.4e-06 0.0022 5.8834
## 265400_at 3.789 8.07 14.37 1.5e-06 0.0022 5.8499
## 259426_at -3.280 5.99 -14.21 1.6e-06 0.0022 5.7823
## 263431_at 3.203 6.10 13.93 1.9e-06 0.0022 5.6667
## 245688_at 4.523 5.63 13.88 1.9e-06 0.0022 5.6473
## 267028_at -1.927 8.70 -13.86 1.9e-06 0.0022 5.6383
## 263998_at 3.313 4.40 13.82 2.0e-06 0.0022 5.6217
## 258552_at 2.157 3.68 13.75 2.1e-06 0.0022 5.5904
## 255822_at 2.968 3.72 13.74 2.1e-06 0.0022 5.5875
## 259705_at -3.218 6.47 -13.72 2.1e-06 0.0022 5.5782
## 257137_at 2.267 5.45 13.70 2.1e-06 0.0022 5.5699
## 259736_at 2.358 2.85 13.69 2.1e-06 0.0022 5.5666
## 264054_at 2.531 4.48 13.33 2.5e-06 0.0025 5.4067
## 267158_at 2.798 3.61 12.81 3.3e-06 0.0030 5.1681
## 260005_at -2.666 4.59 -12.64 3.7e-06 0.0030 5.0839
## 252612_at 4.626 4.22 12.61 3.7e-06 0.0030 5.0722
## 251032_at 1.960 4.18 12.61 3.7e-06 0.0030 5.0696
## 264672_at 3.099 4.62 12.57 3.8e-06 0.0030 5.0531
## 266462_at -4.503 6.74 -12.41 4.2e-06 0.0030 4.9730
## 266533_s_at 2.252 7.26 12.38 4.2e-06 0.0030 4.9572
## 261131_at 2.206 5.69 12.29 4.4e-06 0.0030 4.9151
## 265084_at -4.436 5.74 -12.24 4.6e-06 0.0030 4.8902
## 254239_at 1.996 6.95 12.21 4.7e-06 0.0030 4.8715
## 248352_at -5.500 5.50 -12.21 4.7e-06 0.0030 4.8710
## 251575_at 1.743 5.17 12.17 4.8e-06 0.0030 4.8553
## 245247_at -1.556 5.98 -12.17 4.8e-06 0.0030 4.8548
## 256825_at 3.618 7.14 12.13 4.9e-06 0.0030 4.8326
## 265250_at 3.045 4.41 12.09 5.0e-06 0.0030 4.8128
## 263320_at -1.776 7.52 -11.98 5.3e-06 0.0030 4.7561
## 264025_at 2.687 2.85 11.98 5.3e-06 0.0030 4.7542
## 245711_at -2.720 4.78 -11.82 5.8e-06 0.0032 4.6740
## 260922_at 1.693 2.95 11.76 6.0e-06 0.0032 4.6396
## 245319_at -2.436 4.70 -11.76 6.0e-06 0.0032 4.6389
## 257206_at 2.597 3.89 11.71 6.2e-06 0.0032 4.6128
## 248759_at -2.826 3.59 -11.62 6.6e-06 0.0033 4.5633
## 252863_at 2.651 3.26 11.58 6.7e-06 0.0033 4.5452
## 252965_at 2.331 2.67 11.46 7.2e-06 0.0035 4.4779
## 267093_at 2.157 4.90 11.42 7.4e-06 0.0035 4.4558
## 258159_at 2.245 3.28 11.32 7.9e-06 0.0035 4.3990
## 248912_at 3.569 3.68 11.31 7.9e-06 0.0035 4.3967
## 252255_at 2.514 6.75 11.30 7.9e-06 0.0035 4.3903
## 258156_at 1.935 3.70 11.18 8.5e-06 0.0037 4.3225
## 253255_at 3.245 3.82 11.14 8.7e-06 0.0038 4.3021
## 253073_at 1.862 3.94 11.09 9.0e-06 0.0038 4.2746
## 267339_at 2.205 3.58 11.03 9.3e-06 0.0039 4.2399
## 253347_at 2.975 4.73 10.91 1.0e-05 0.0040 4.1667
## 267053_s_at 3.666 3.99 10.90 1.0e-05 0.0040 4.1610
## 251183_at 1.690 2.44 10.88 1.0e-05 0.0040 4.1489
## 252915_at -1.895 6.77 -10.74 1.1e-05 0.0043 4.0685
## 262694_at 1.498 4.60 10.73 1.1e-05 0.0043 4.0612
## 245174_at 3.152 5.38 10.65 1.2e-05 0.0044 4.0156
## 251395_at 3.344 4.93 10.64 1.2e-05 0.0044 4.0058
## 249867_at 4.695 3.49 10.62 1.2e-05 0.0044 3.9938
## 266097_at -2.319 5.45 -10.52 1.3e-05 0.0046 3.9342
## 252102_at -4.666 4.86 -10.41 1.4e-05 0.0048 3.8659
## 264371_at 2.218 6.56 10.39 1.4e-05 0.0048 3.8568
## 254362_at 1.504 2.86 10.33 1.5e-05 0.0048 3.8181
## 258196_at 2.591 4.00 10.28 1.5e-05 0.0048 3.7914
## 249073_at 2.048 6.85 10.25 1.5e-05 0.0048 3.7679
## 259180_at 2.251 2.69 10.24 1.5e-05 0.0048 3.7660
## 252515_at -4.344 5.92 -10.24 1.5e-05 0.0048 3.7641
## 254474_at 1.651 2.61 10.22 1.6e-05 0.0048 3.7506
## 260840_at 1.934 5.41 10.22 1.6e-05 0.0048 3.7484
## 262434_at 2.056 2.59 10.21 1.6e-05 0.0048 3.7458
## 255782_at 2.013 3.64 10.17 1.6e-05 0.0048 3.7209
## 255104_at 2.467 4.31 10.15 1.6e-05 0.0048 3.7069
## 245176_at 1.493 3.96 10.15 1.6e-05 0.0048 3.7043
## 255601_at -2.469 4.59 -10.12 1.7e-05 0.0048 3.6890
## 258325_at -4.441 4.49 -10.11 1.7e-05 0.0048 3.6831
## 267316_at 2.247 4.42 10.10 1.7e-05 0.0048 3.6756
## 256237_at 3.207 4.72 9.97 1.8e-05 0.0050 3.5934
## 245196_at 1.902 2.44 9.97 1.9e-05 0.0050 3.5922
## 250992_at 2.604 3.10 9.87 2.0e-05 0.0050 3.5281
## 254233_at 2.124 4.98 9.87 2.0e-05 0.0050 3.5238
## 253829_at -1.341 6.31 -9.83 2.0e-05 0.0050 3.5000
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## 254757_at -0.104 1.08 -0.59 5.8e-01 0.7987 -6.6171
## 246345_at 0.072 1.93 0.59 5.8e-01 0.7987 -6.6171
## 261718_at -0.137 3.74 -0.59 5.8e-01 0.7987 -6.6171
## 251833_at -0.095 1.25 -0.59 5.8e-01 0.7987 -6.6171
## 246958_at -0.119 6.08 -0.59 5.8e-01 0.7987 -6.6171
## 261873_at 0.147 1.82 0.59 5.8e-01 0.7987 -6.6172
## 256586_at -0.083 0.99 -0.59 5.8e-01 0.7987 -6.6172
## 264991_s_at 0.082 1.64 0.59 5.8e-01 0.7987 -6.6172
## 258128_at 0.096 3.27 0.59 5.8e-01 0.7987 -6.6172
## 249543_at -0.119 1.65 -0.59 5.8e-01 0.7987 -6.6173
## 265653_at -0.095 1.10 -0.59 5.8e-01 0.7987 -6.6173
## 250154_at -0.093 1.40 -0.59 5.8e-01 0.7987 -6.6173
## 252544_at -0.083 1.21 -0.59 5.8e-01 0.7988 -6.6174
## 250780_at -0.233 1.41 -0.59 5.8e-01 0.7988 -6.6175
## 250944_at -0.187 7.03 -0.59 5.8e-01 0.7988 -6.6175
## 259338_at -0.088 1.35 -0.59 5.8e-01 0.7988 -6.6176
## 246482_at -0.219 2.49 -0.59 5.8e-01 0.7988 -6.6176
## 248532_at -0.169 6.92 -0.59 5.8e-01 0.7988 -6.6176
## 256081_at 0.108 3.74 0.59 5.8e-01 0.7988 -6.6176
## 266106_at 0.164 5.70 0.59 5.8e-01 0.7988 -6.6177
## 254112_at 0.116 1.93 0.59 5.8e-01 0.7988 -6.6177
## 265136_at 0.089 1.20 0.59 5.8e-01 0.7988 -6.6178
## 250829_at -0.079 6.23 -0.59 5.8e-01 0.7988 -6.6178
## 264839_at -0.086 1.37 -0.59 5.8e-01 0.7988 -6.6178
## 260338_at 0.124 4.39 0.58 5.8e-01 0.7989 -6.6179
## 265976_at -0.092 1.35 -0.58 5.8e-01 0.7990 -6.6181
## 257942_at 0.085 1.15 0.58 5.8e-01 0.7990 -6.6181
## 251233_at -0.180 2.70 -0.58 5.8e-01 0.7991 -6.6181
## 255624_at 0.072 3.84 0.58 5.8e-01 0.7992 -6.6183
## 253227_at 0.086 1.24 0.58 5.8e-01 0.7993 -6.6184
## 249998_at -0.211 5.88 -0.58 5.8e-01 0.7997 -6.6187
## 253492_at 0.179 4.40 0.58 5.8e-01 0.7997 -6.6188
## 256398_at 0.088 1.70 0.58 5.8e-01 0.7998 -6.6189
## 255340_at -0.079 1.20 -0.58 5.8e-01 0.7998 -6.6189
## 267342_at 0.086 1.64 0.58 5.8e-01 0.7998 -6.6189
## 259672_at -0.112 3.10 -0.58 5.8e-01 0.7998 -6.6190
## 250705_at 0.113 3.49 0.58 5.8e-01 0.7999 -6.6191
## 262864_at -0.077 1.42 -0.58 5.8e-01 0.8000 -6.6192
## 253497_at 0.167 6.19 0.58 5.8e-01 0.8001 -6.6194
## 245763_at -0.144 3.53 -0.58 5.8e-01 0.8001 -6.6194
## 249798_at -0.164 3.28 -0.58 5.8e-01 0.8001 -6.6194
## 249334_at 0.077 1.65 0.58 5.8e-01 0.8001 -6.6194
## 248539_at -0.103 1.11 -0.58 5.8e-01 0.8001 -6.6194
## 253507_at -0.115 1.59 -0.58 5.8e-01 0.8001 -6.6195
## 254786_at 0.072 1.13 0.58 5.8e-01 0.8001 -6.6195
## 248823_s_at -0.080 1.06 -0.58 5.8e-01 0.8001 -6.6196
## 248644_at -0.086 1.23 -0.58 5.8e-01 0.8001 -6.6196
## 264835_at 0.134 3.14 0.58 5.8e-01 0.8001 -6.6197
## 251536_at 0.128 6.03 0.58 5.8e-01 0.8001 -6.6197
## 246419_at 0.102 1.90 0.58 5.8e-01 0.8001 -6.6197
## 246893_at -0.281 5.22 -0.58 5.8e-01 0.8001 -6.6197
## 259650_at -0.084 1.15 -0.58 5.8e-01 0.8001 -6.6197
## 252307_at -0.109 1.40 -0.58 5.8e-01 0.8002 -6.6198
## 254441_at 0.087 1.32 0.58 5.8e-01 0.8002 -6.6199
## 260933_at 0.103 1.64 0.58 5.8e-01 0.8003 -6.6199
## 263021_at -0.097 1.11 -0.58 5.8e-01 0.8004 -6.6201
## 259071_at -0.148 3.27 -0.58 5.8e-01 0.8004 -6.6201
## 264379_at -0.110 1.64 -0.58 5.8e-01 0.8005 -6.6202
## 249560_at -0.085 1.51 -0.58 5.8e-01 0.8005 -6.6202
## 253017_at -0.106 1.36 -0.58 5.8e-01 0.8005 -6.6203
## 265145_at -0.092 1.53 -0.58 5.8e-01 0.8005 -6.6203
## 260372_at 0.092 3.46 0.58 5.8e-01 0.8006 -6.6204
## 246454_at -0.083 2.61 -0.58 5.8e-01 0.8006 -6.6205
## 261146_at 0.114 1.72 0.58 5.8e-01 0.8006 -6.6205
## 246094_at -0.114 3.98 -0.58 5.8e-01 0.8006 -6.6205
## 255507_at 0.273 3.66 0.58 5.8e-01 0.8007 -6.6206
## 264705_at 0.159 6.00 0.58 5.8e-01 0.8007 -6.6207
## 253489_at -0.101 4.01 -0.58 5.8e-01 0.8007 -6.6207
## 251766_at 0.083 1.54 0.58 5.8e-01 0.8007 -6.6208
## 258995_at 0.229 4.91 0.58 5.8e-01 0.8007 -6.6208
## 265748_at -0.091 1.19 -0.58 5.8e-01 0.8007 -6.6208
## 258287_at -0.117 2.02 -0.58 5.8e-01 0.8007 -6.6208
## 249857_at -0.077 1.22 -0.58 5.8e-01 0.8009 -6.6210
## 254828_at -0.109 1.33 -0.58 5.8e-01 0.8010 -6.6211
## 267427_at -0.086 1.42 -0.58 5.8e-01 0.8010 -6.6212
## 256596_at 0.119 2.14 0.58 5.8e-01 0.8011 -6.6212
## 255803_at 0.139 4.32 0.58 5.8e-01 0.8011 -6.6213
## 251986_at -0.092 1.49 -0.58 5.8e-01 0.8011 -6.6213
## 245931_at -0.092 1.92 -0.58 5.8e-01 0.8011 -6.6214
## 253198_at -0.080 2.59 -0.58 5.8e-01 0.8011 -6.6214
## 249555_at 0.128 3.29 0.58 5.8e-01 0.8013 -6.6216
## 246174_s_at -0.081 1.23 -0.58 5.8e-01 0.8013 -6.6216
## 261818_at -0.118 2.43 -0.58 5.8e-01 0.8013 -6.6217
## 260052_at -0.095 1.11 -0.58 5.8e-01 0.8014 -6.6217
## 245206_at -0.086 2.36 -0.58 5.8e-01 0.8016 -6.6219
## 247769_at 0.103 1.23 0.58 5.8e-01 0.8016 -6.6219
## 252748_at -0.078 1.06 -0.58 5.8e-01 0.8016 -6.6220
## 249936_at -0.145 5.08 -0.58 5.8e-01 0.8016 -6.6220
## 266102_at 0.180 1.55 0.58 5.8e-01 0.8017 -6.6221
## 249918_at 0.217 4.68 0.58 5.8e-01 0.8017 -6.6222
## 250459_at -0.182 3.28 -0.58 5.8e-01 0.8017 -6.6222
## 253665_at -0.084 1.29 -0.58 5.8e-01 0.8017 -6.6222
## 257691_at -0.097 1.25 -0.58 5.8e-01 0.8018 -6.6223
## 250281_at -0.204 2.88 -0.58 5.8e-01 0.8018 -6.6224
## 246490_at 0.121 2.43 0.58 5.8e-01 0.8020 -6.6225
## 259754_at 0.124 4.32 0.58 5.8e-01 0.8021 -6.6226
## 252003_at 0.087 1.34 0.58 5.8e-01 0.8021 -6.6227
## 265724_at -0.095 1.50 -0.58 5.8e-01 0.8021 -6.6227
## 263074_at 0.077 2.21 0.58 5.8e-01 0.8021 -6.6227
## 266657_at 0.111 5.12 0.58 5.8e-01 0.8021 -6.6228
## 248471_at 0.159 1.80 0.58 5.8e-01 0.8021 -6.6228
## 266957_at 0.153 4.95 0.58 5.8e-01 0.8021 -6.6228
## 253621_at 0.087 1.14 0.58 5.8e-01 0.8021 -6.6229
## 257642_at 0.152 3.31 0.58 5.8e-01 0.8021 -6.6229
## 259992_at -0.117 2.37 -0.58 5.8e-01 0.8021 -6.6229
## 262547_at -0.090 1.33 -0.58 5.8e-01 0.8022 -6.6230
## 262823_at 0.104 2.29 0.58 5.8e-01 0.8023 -6.6231
## 245146_at 0.150 4.69 0.58 5.8e-01 0.8023 -6.6232
## 264391_at -0.091 1.49 -0.58 5.8e-01 0.8023 -6.6232
## 266806_at -0.105 5.95 -0.58 5.8e-01 0.8023 -6.6233
## 255682_at 0.121 4.09 0.58 5.8e-01 0.8023 -6.6233
## 246835_at -0.092 1.37 -0.58 5.8e-01 0.8023 -6.6233
## 248357_at 0.121 3.31 0.58 5.8e-01 0.8023 -6.6233
## 255759_at -0.122 2.15 -0.58 5.8e-01 0.8023 -6.6234
## 254149_at 0.117 1.36 0.58 5.8e-01 0.8025 -6.6235
## 266988_at -0.157 9.87 -0.58 5.8e-01 0.8025 -6.6236
## 255315_at -0.178 1.76 -0.58 5.8e-01 0.8025 -6.6236
## 255563_at 0.084 1.33 0.58 5.8e-01 0.8026 -6.6237
## 264813_at -0.089 1.12 -0.58 5.8e-01 0.8027 -6.6239
## 261462_at -0.118 1.39 -0.58 5.8e-01 0.8027 -6.6239
## 265554_at 0.074 1.36 0.57 5.8e-01 0.8029 -6.6242
## 257469_at -0.120 1.42 -0.57 5.8e-01 0.8029 -6.6242
## 249344_at 0.182 4.29 0.57 5.8e-01 0.8029 -6.6242
## 264298_at 0.144 1.76 0.57 5.8e-01 0.8029 -6.6242
## 260002_at -0.118 4.99 -0.57 5.8e-01 0.8029 -6.6242
## 253129_at -0.128 5.55 -0.57 5.8e-01 0.8029 -6.6242
## 248385_at 0.074 1.50 0.57 5.8e-01 0.8029 -6.6243
## 255324_s_at 0.095 1.15 0.57 5.8e-01 0.8030 -6.6244
## 246879_at -0.109 5.98 -0.57 5.8e-01 0.8031 -6.6245
## 255911_at -0.085 1.18 -0.57 5.8e-01 0.8032 -6.6246
## 247142_at -0.144 1.63 -0.57 5.8e-01 0.8033 -6.6246
## 261602_at 0.122 1.67 0.57 5.8e-01 0.8033 -6.6247
## 249732_at 0.101 1.63 0.57 5.8e-01 0.8033 -6.6248
## 256700_at -0.111 2.95 -0.57 5.8e-01 0.8033 -6.6248
## 255278_at 0.176 2.39 0.57 5.8e-01 0.8033 -6.6248
## 256091_at -0.099 8.44 -0.57 5.8e-01 0.8033 -6.6249
## 253006_at 0.115 6.38 0.57 5.8e-01 0.8033 -6.6249
## 254186_at 0.077 1.32 0.57 5.8e-01 0.8033 -6.6249
## 267469_at -0.145 4.40 -0.57 5.8e-01 0.8034 -6.6250
## 263038_at 0.106 1.39 0.57 5.8e-01 0.8034 -6.6251
## 264596_at -0.090 1.22 -0.57 5.8e-01 0.8034 -6.6251
## 254636_at -0.098 8.32 -0.57 5.8e-01 0.8034 -6.6251
## 264588_at -0.140 2.11 -0.57 5.8e-01 0.8035 -6.6252
## 262392_at -0.126 2.05 -0.57 5.8e-01 0.8035 -6.6252
## 256374_at -0.191 2.31 -0.57 5.8e-01 0.8035 -6.6253
## 264478_at -0.092 1.28 -0.57 5.8e-01 0.8035 -6.6253
## 254569_s_at 0.073 1.11 0.57 5.8e-01 0.8035 -6.6254
## 248778_at -0.154 1.96 -0.57 5.8e-01 0.8036 -6.6254
## 257596_at 0.103 1.73 0.57 5.8e-01 0.8036 -6.6255
## 246686_at -0.100 1.22 -0.57 5.8e-01 0.8036 -6.6255
## 255932_at -0.106 1.43 -0.57 5.8e-01 0.8037 -6.6256
## 245783_s_at 0.134 1.46 0.57 5.8e-01 0.8037 -6.6257
## 256157_at 0.077 1.21 0.57 5.8e-01 0.8037 -6.6257
## 246463_at -0.472 4.76 -0.57 5.8e-01 0.8039 -6.6258
## 255102_s_at 0.080 2.12 0.57 5.8e-01 0.8040 -6.6260
## 258766_at 0.095 3.25 0.57 5.8e-01 0.8040 -6.6260
## 261089_at 0.277 4.33 0.57 5.8e-01 0.8040 -6.6260
## 258214_at -0.143 3.00 -0.57 5.8e-01 0.8040 -6.6260
## 247375_at 0.136 1.72 0.57 5.9e-01 0.8041 -6.6262
## 265097_at 0.128 1.66 0.57 5.9e-01 0.8043 -6.6263
## 262957_s_at 0.090 5.60 0.57 5.9e-01 0.8043 -6.6263
## 256597_at -0.116 1.39 -0.57 5.9e-01 0.8044 -6.6265
## 255100_at 0.072 1.09 0.57 5.9e-01 0.8044 -6.6265
## 267440_at -0.080 1.76 -0.57 5.9e-01 0.8044 -6.6266
## 257002_at -0.096 10.00 -0.57 5.9e-01 0.8044 -6.6266
## 250389_at -0.074 1.52 -0.57 5.9e-01 0.8044 -6.6266
## 264096_at 0.085 1.94 0.57 5.9e-01 0.8044 -6.6266
## 261743_s_at 0.212 4.01 0.57 5.9e-01 0.8045 -6.6267
## 257555_at -0.096 1.42 -0.57 5.9e-01 0.8046 -6.6268
## 265326_at 0.136 2.68 0.57 5.9e-01 0.8046 -6.6269
## 251817_at -0.139 6.08 -0.57 5.9e-01 0.8046 -6.6269
## 267224_at -0.133 1.26 -0.57 5.9e-01 0.8047 -6.6270
## 254222_at -0.078 2.08 -0.57 5.9e-01 0.8047 -6.6271
## 266882_at 0.120 5.58 0.57 5.9e-01 0.8047 -6.6271
## 265798_at 0.088 3.11 0.57 5.9e-01 0.8047 -6.6271
## 258018_at -0.070 1.19 -0.57 5.9e-01 0.8047 -6.6271
## 261555_s_at 0.092 1.15 0.57 5.9e-01 0.8048 -6.6273
## 248246_at 0.141 1.85 0.57 5.9e-01 0.8048 -6.6273
## 255215_at -0.116 1.29 -0.57 5.9e-01 0.8048 -6.6273
## 253965_at 0.102 1.74 0.57 5.9e-01 0.8049 -6.6274
## 265608_at 0.074 1.37 0.57 5.9e-01 0.8049 -6.6275
## 255906_at 0.069 1.84 0.57 5.9e-01 0.8049 -6.6276
## 251284_at -0.110 1.11 -0.57 5.9e-01 0.8049 -6.6276
## 247592_at -0.082 1.35 -0.57 5.9e-01 0.8049 -6.6276
## 260991_at -0.115 1.33 -0.57 5.9e-01 0.8049 -6.6276
## 260930_at -0.071 1.29 -0.57 5.9e-01 0.8049 -6.6276
## 265435_s_at 0.074 1.35 0.57 5.9e-01 0.8049 -6.6276
## 262005_at 0.105 5.08 0.57 5.9e-01 0.8050 -6.6278
## 254787_at -0.207 3.25 -0.57 5.9e-01 0.8050 -6.6278
## 254526_at -0.110 6.70 -0.57 5.9e-01 0.8051 -6.6280
## 257420_at 0.164 2.46 0.57 5.9e-01 0.8051 -6.6280
## 256115_at -0.139 6.00 -0.57 5.9e-01 0.8051 -6.6280
## 256346_s_at -0.430 2.67 -0.57 5.9e-01 0.8051 -6.6280
## 253933_at 0.089 1.46 0.57 5.9e-01 0.8052 -6.6281
## 257496_s_at 0.074 3.13 0.57 5.9e-01 0.8052 -6.6282
## 265003_at 0.079 1.45 0.57 5.9e-01 0.8052 -6.6282
## 265100_at -0.116 2.76 -0.57 5.9e-01 0.8052 -6.6282
## 255393_at 0.098 1.05 0.57 5.9e-01 0.8052 -6.6283
## 260761_at 0.083 1.34 0.57 5.9e-01 0.8054 -6.6284
## 258812_at 0.135 5.10 0.57 5.9e-01 0.8054 -6.6285
## 259599_at -0.211 6.56 -0.57 5.9e-01 0.8054 -6.6285
## 259998_at 0.074 1.70 0.57 5.9e-01 0.8054 -6.6286
## 251758_at 0.124 10.08 0.57 5.9e-01 0.8054 -6.6286
## 265016_at -0.075 1.16 -0.57 5.9e-01 0.8054 -6.6286
## 260635_at -0.118 6.26 -0.57 5.9e-01 0.8055 -6.6287
## 257635_at 0.086 1.41 0.57 5.9e-01 0.8055 -6.6288
## 266285_at 0.087 1.44 0.57 5.9e-01 0.8055 -6.6288
## 251651_at -0.081 1.20 -0.57 5.9e-01 0.8056 -6.6289
## 248442_at -0.213 5.62 -0.57 5.9e-01 0.8056 -6.6289
## 262700_at -0.201 2.82 -0.57 5.9e-01 0.8056 -6.6289
## 265532_at -0.108 1.25 -0.57 5.9e-01 0.8056 -6.6290
## 257277_at 0.145 1.49 0.57 5.9e-01 0.8057 -6.6290
## 260192_at 0.137 1.58 0.57 5.9e-01 0.8057 -6.6291
## 256762_at -0.082 1.19 -0.57 5.9e-01 0.8057 -6.6291
## 258873_at -0.072 1.69 -0.57 5.9e-01 0.8057 -6.6292
## 262945_at -0.079 1.52 -0.57 5.9e-01 0.8057 -6.6292
## 257491_at -0.083 1.06 -0.57 5.9e-01 0.8057 -6.6293
## 253987_at 0.339 4.00 0.57 5.9e-01 0.8059 -6.6294
## 264389_at -0.109 2.51 -0.57 5.9e-01 0.8059 -6.6294
## 254992_at -0.131 6.60 -0.57 5.9e-01 0.8059 -6.6295
## 250393_at -0.096 1.68 -0.57 5.9e-01 0.8059 -6.6295
## 256911_at -0.110 1.49 -0.57 5.9e-01 0.8059 -6.6295
## 258962_at 0.078 2.38 0.57 5.9e-01 0.8059 -6.6296
## 266572_at 0.081 1.22 0.57 5.9e-01 0.8059 -6.6296
## 248625_at -0.153 1.73 -0.57 5.9e-01 0.8059 -6.6296
## 266315_at -0.165 4.43 -0.57 5.9e-01 0.8059 -6.6296
## 264645_at -0.110 7.80 -0.57 5.9e-01 0.8059 -6.6297
## 255512_at -0.082 2.47 -0.57 5.9e-01 0.8059 -6.6297
## 251468_at -0.152 4.09 -0.57 5.9e-01 0.8059 -6.6298
## [ reached 'max' / getOption("max.print") -- omitted 6144 rows ]Lets convert the accnum to a character value:
all2$ACCNUM <- as.character(all2$ACCNUM)## Error in eval(expr, envir, enclos): object 'all2' not foundwrite.table(all2, file = "BRIC_16_Final.csv", row.names = TRUE, col.names = TRUE, sep = "\t")## Error in eval(expr, envir, enclos): object 'all2' not foundcolumns(org.At.tair.db)## [1] "ARACYC" "ARACYCENZYME" "ENTREZID" "ENZYME" "EVIDENCE"
## [6] "EVIDENCEALL" "GENENAME" "GO" "GOALL" "ONTOLOGY"
## [11] "ONTOLOGYALL" "PATH" "PMID" "REFSEQ" "SYMBOL"
## [16] "TAIR"foldchanges <- as.data.frame(all2$logFC)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'all2' not foundall2$entrez = mapIds(org.At.tair.db,
keys = all2$ACCNUM,
column = "ENTREZID",
keytype = "TAIR",
multiVals = "first")## Error in eval(expr, envir, enclos): object 'all2' not foundhead(all2, 10)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'all2' not foundPath view
Load in data:
library("pathview")## ## ##############################################################################
## Pathview is an open source software package distributed under GNU General
## Public License version 3 (GPLv3). Details of GPLv3 is available at
## http://www.gnu.org/licenses/gpl-3.0.html. Particullary, users are required to
## formally cite the original Pathview paper (not just mention it) in publications
## or products. For details, do citation("pathview") within R.
##
## The pathview downloads and uses KEGG data. Non-academic uses may require a KEGG
## license agreement (details at http://www.kegg.jp/kegg/legal.html).
## ##############################################################################library("gage")## library("gageData")
data("kegg.sets.hs")foldchanges = all2$logFC## Error in eval(expr, envir, enclos): object 'all2' not foundnames(foldchanges) = all2$entrez## Error in eval(expr, envir, enclos): object 'all2' not foundhead(foldchanges)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'foldchanges' not foundkegg.ath = kegg.gsets("ath", id.type = "entrez")
kegg.ath.sigmet = kegg.ath$kg.sets[kegg.ath$sigmet.idx]Lets see the results
keggres = gage(foldchanges, gsets = kegg.ath.sigmet, same.dir = TRUE)## Error in eval(expr, envir, enclos): object 'foldchanges' not foundlapply(keggres, head)## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'lapply': object 'keggres' not foundgreater <- keggres$greater## Error in eval(expr, envir, enclos): object 'keggres' not foundlesser <- keggres$less## Error in eval(expr, envir, enclos): object 'keggres' not foundwrite.csv(greater, file = "BRIC_16_pathview_Greater_Pathways.csv")## Error in eval(expr, p): object 'greater' not foundwrite.csv(lesser, file = "BRIC_16_pathview_Lesser_Pathways.csv")## Error in eval(expr, p): object 'lesser' not foundLets map to pathways (greater)
keggrespathways = data.frame(id = rownames(keggres$greater), keggres$greater) %>%
tbl_df()%>%
filter(row_number() <=5) %>%
.$id %>%
as.character()## Warning: `tbl_df()` was deprecated in dplyr 1.0.0.
## ℹ Please use `tibble::as_tibble()` instead.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'keggres' not foundkeggrespathways## Error in eval(expr, envir, enclos): object 'keggrespathways' not foundkeggresids_greater = substr(keggrespathways, start =1, stop = 8)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'substr': object 'keggrespathways' not foundkeggresids_greater## Error in eval(expr, envir, enclos): object 'keggresids_greater' not foundgenedata <- as.character(all2$logFC)## Error in eval(expr, envir, enclos): object 'all2' not foundDefine a plotting funtion to apply next
plot_pathway = function(pid) pathview(gene.data = foldchanges, pathway.id = pid, specie = "ath", new.signatue = FALSE)Now, lets map to pathways (lesser)
keggrespathways = data.frame(id = rownames(keggres$less), keggres$less) %>%
tbl_df()%>%
filter(row_number() <=5) %>%
.$id %>%
as.character()## Warning: `tbl_df()` was deprecated in dplyr 1.0.0.
## ℹ Please use `tibble::as_tibble()` instead.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'keggres' not foundkeggrespathways## Error in eval(expr, envir, enclos): object 'keggrespathways' not foundkeggresids_less = substr(keggrespathways, start =1, stop = 8)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'substr': object 'keggrespathways' not foundkeggresids_less## Error in eval(expr, envir, enclos): object 'keggresids_less' not foundgenedata <- as.character(all2$logFC)## Error in eval(expr, envir, enclos): object 'all2' not foundDefine a plotting funtion to apply next
plot_pathway = function(pid) pathview(gene.data = foldchanges, pathway.id = pid, specie = "ath", new.signatue = FALSE)RNASeq and EdgeR
library("edgeR")
library("dplyr")
library("AnnotationDbi")
library("org.Mm.eg.db")## setwd("~/Desktop/classroom/myfiles")rawdata = read.csv("GLDS-102_rna_seq_Normalized_Counts.csv")
group <- factor(c('1', '1', '1' ,'1' , '1', '1', '2', '2', '2', '2', '2', '2'))
dgeGlm <- DGEList(counts = rawdata, group = group)## Setting first column of `counts` as gene annotation.str(dgeGlm)## Formal class 'DGEList' [package "edgeR"] with 1 slot
## ..@ .Data:List of 3
## .. ..$ : num [1:24035, 1:12] 2976.8 59.8 21.2 40.1 0 ...
## .. .. ..- attr(*, "dimnames")=List of 2
## .. .. .. ..$ : chr [1:24035] "1" "2" "3" "4" ...
## .. .. .. ..$ : chr [1:12] "Mmus_C57.6J_KDN_FLT_Rep1_M23" "Mmus_C57.6J_KDN_FLT_Rep2_M24" "Mmus_C57.6J_KDN_FLT_Rep3_M25" "Mmus_C57.6J_KDN_FLT_Rep4_M26" ...
## .. ..$ :'data.frame': 12 obs. of 3 variables:
## .. .. ..$ group : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 2 2 2 2 ...
## .. .. ..$ lib.size : num [1:12] 40266365 40740336 37739541 39196969 36820645 ...
## .. .. ..$ norm.factors: num [1:12] 1 1 1 1 1 1 1 1 1 1 ...
## .. ..$ :'data.frame': 24035 obs. of 1 variable:
## .. .. ..$ gene_ID: chr [1:24035] "ENSMUSG00000000001" "ENSMUSG00000000028" "ENSMUSG00000000031" "ENSMUSG00000000037" ...
## ..$ names: chr [1:3] "counts" "samples" "genes"str(group)## Factor w/ 2 levels "1","2": 1 1 1 1 1 1 2 2 2 2 ...keep <- rowSums(cpm(dgeGlm)>2) >=4
dgeGlm <- dgeGlm[keep,]
names(dgeGlm)## [1] "counts" "samples" "genes"dgeGlm[["samples"]]## group lib.size norm.factors
## Mmus_C57.6J_KDN_FLT_Rep1_M23 1 4.0e+07 1
## Mmus_C57.6J_KDN_FLT_Rep2_M24 1 4.1e+07 1
## Mmus_C57.6J_KDN_FLT_Rep3_M25 1 3.8e+07 1
## Mmus_C57.6J_KDN_FLT_Rep4_M26 1 3.9e+07 1
## Mmus_C57.6J_KDN_FLT_Rep5_M27 1 3.7e+07 1
## Mmus_C57.6J_KDN_FLT_Rep6_M28 1 3.6e+07 1
## Mmus_C57.6J_KDN_GC_Rep1_M33 2 3.7e+07 1
## Mmus_C57.6J_KDN_GC_Rep2_M34 2 3.7e+07 1
## Mmus_C57.6J_KDN_GC_Rep3_M35 2 4.0e+07 1
## Mmus_C57.6J_KDN_GC_Rep4_M36 2 3.6e+07 1
## Mmus_C57.6J_KDN_GC_Rep5_M37 2 3.8e+07 1
## Mmus_C57.6J_KDN_GC_Rep6_M38 2 3.5e+07 1design <- model.matrix(~group)
design## (Intercept) group2
## 1 1 0
## 2 1 0
## 3 1 0
## 4 1 0
## 5 1 0
## 6 1 0
## 7 1 1
## 8 1 1
## 9 1 1
## 10 1 1
## 11 1 1
## 12 1 1
## attr(,"assign")
## [1] 0 1
## attr(,"contrasts")
## attr(,"contrasts")$group
## [1] "contr.treatment"dgeGlmComDisp <- estimateGLMCommonDisp(dgeGlm, design, verbose = TRUE)## Disp = 0.026 , BCV = 0.16dgeGlmTrendDisp <- estimateGLMTrendedDisp(dgeGlmComDisp, design)
dgeGlmTagDisp <- estimateGLMTagwiseDisp(dgeGlmTrendDisp, design)
plotBCV(dgeGlmTagDisp) 
fit <- glmFit(dgeGlmTagDisp, design)
colnames(coef(fit))## [1] "(Intercept)" "group2"lrt <- glmLRT(fit, coef = 2)
ttGlm <- topTags(lrt, n = Inf)
class(ttGlm)## [1] "TopTags"
## attr(,"package")
## [1] "edgeR"summary(deGlm <- decideTestsDGE(lrt, p = 0.1, adjust = "fdr"))## group2
## Down 64
## NotSig 13390
## Up 159tagsGlm <- rownames(dgeGlmTagDisp)[as.logical((deGlm))]
plotSmear(lrt, de.tags = tagsGlm)
hits2 <- ttGlm$table[ttGlm$table$FDR < 0.1, ]
write.csv(hits2, "Mouse_EdgeR_Results_Zero_vs_1.csv")columns(org.Mm.eg.db)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "IPI" "MGI"
## [16] "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM" "PMID"
## [21] "PROSITE" "REFSEQ" "SYMBOL" "UNIPROT"ttGlm2 <- as.data.frame(ttGlm$table)
head(rownames(ttGlm))## [1] "4902" "1062" "3038" "364" "14467" "16306"keytypes(org.Mm.eg.db)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "IPI" "MGI"
## [16] "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM" "PMID"
## [21] "PROSITE" "REFSEQ" "SYMBOL" "UNIPROT"ttGlm2$symbol = mapIds(org.Mm.eg.db,
keys=row.names(ttGlm2),
column = "SYMBOL",
keytype = "ENTREZID",
multiVals = "first")## 'select()' returned 1:1 mapping between keys and columnsttGlm2$entrez = mapIds(org.Mm.eg.db,
keys=row.names(ttGlm2),
column = "ENTREZID",
keytype = "ENTREZID",
multiVals = "first")
ttGlm2$name = mapIds(org.Mm.eg.db,
keys=row.names(ttGlm2),
column = "GENENAME",
keytype = "ENTREZID",
multiVals = "first")## 'select()' returned 1:1 mapping between keys and columnshead(ttGlm2)## gene_ID logFC logCPM LR PValue FDR symbol entrez
## 4902 ENSMUSG00000026077 -1.36 3.6 80 4.3e-19 5.9e-15 <NA> 4902
## 1062 ENSMUSG00000007872 0.89 5.5 77 1.9e-18 1.3e-14 <NA> 1062
## 3038 ENSMUSG00000021775 0.95 6.2 63 2.0e-15 9.1e-12 <NA> 3038
## 364 ENSMUSG00000002250 -0.83 5.3 62 2.7e-15 9.2e-12 <NA> 364
## 14467 ENSMUSG00000059824 2.26 4.6 58 2.6e-14 7.2e-11 Nipsnap2 14467
## 16306 ENSMUSG00000074715 -1.99 3.8 47 7.0e-12 1.6e-08 In(8)16Rk-p 16306
## name
## 4902 <NA>
## 1062 <NA>
## 3038 <NA>
## 364 <NA>
## 14467 nipsnap homolog 2
## 16306 inversion, Chr 8, Roderick 16, proximalMy software was not following that of the lecture, so I had to troubleshoot and used “ENTREZID” in place of “ENSEMBL” as my row names were actually coding for “ENTREZID”
library("pathview")
library("gage")
library("gageData")
data("kegg.sets.mm")
data("go.subs.mm")
foldchanges <-ttGlm2$logFC
names(foldchanges) <- ttGlm2$entrezThis code does not run– I have included it for analysis:
kegg.sets.mm = kegg.gsets(“mouse”, id.type = “entrez”) kegg.mm.sigmet = kegg.sets.mm\(kg.sets[kegg.sets.mm\)sigmet.idx]
keggres = gage(foldchanges, gsets = kegg.sets.mm, same.dir = TRUE)
lapply(keggres, head)
greaters <- keggres\(greater lessers <- keggres\)less
keggresspathways = data.frame(id = rownames(keggres$greater), keggres$greater) %>%
tibble::as_tibble() %>%
filter(row_number()<=5) %>%
.$id %>%
as.character()## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'keggres' not foundkeggresspathways ## Error in eval(expr, envir, enclos): object 'keggresspathways' not foundkeggresids = substr(keggresspathways, start = 1, stop = 8)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'substr': object 'keggresspathways' not foundkeggresids## Error in eval(expr, envir, enclos): object 'keggresids' not foundplot_pathway = function(pid) pathview(gene.data = foldchanges, pathway.id = pid, species = "mouse", new.signature = FALSE)
tmp = sapply(keggresids, function(pid) pathview (gene.data = foldchanges, pathway.id = pid, species = "mouse"))## Error in h(simpleError(msg, call)): error in evaluating the argument 'X' in selecting a method for function 'sapply': object 'keggresids' not foundlibrary(imager)## Loading required package: magrittr##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extract##
## Attaching package: 'imager'## The following object is masked from 'package:magrittr':
##
## add## The following object is masked from 'package:Biostrings':
##
## width## The following object is masked from 'package:XVector':
##
## width## The following objects are masked from 'package:oligoClasses':
##
## B, B<-## The following objects are masked from 'package:IRanges':
##
## resize, width## The following object is masked from 'package:S4Vectors':
##
## width## The following object is masked from 'package:Biobase':
##
## channel## The following object is masked from 'package:BiocGenerics':
##
## width## The following object is masked from 'package:stringr':
##
## boundary## The following object is masked from 'package:dplyr':
##
## where## The following object is masked from 'package:tidyr':
##
## fill## The following objects are masked from 'package:stats':
##
## convolve, spectrum## The following object is masked from 'package:graphics':
##
## frame## The following object is masked from 'package:base':
##
## save.imagefilenames <- list.files(path = "E:/Bioinformatics/Bisc_450_Scripts/mouse_edgeR", pattern = ".*pathview.png")
all_images <- lapply(filenames, load.image)knitr::include_graphics(filenames)##DESeq
library("DESeq2")## Loading required package: GenomicRanges##
## Attaching package: 'GenomicRanges'## The following object is masked from 'package:magrittr':
##
## subtract## Loading required package: SummarizedExperiment## Loading required package: MatrixGenerics## Loading required package: matrixStats##
## Attaching package: 'matrixStats'## The following objects are masked from 'package:Biobase':
##
## anyMissing, rowMedians## The following object is masked from 'package:dplyr':
##
## count##
## Attaching package: 'MatrixGenerics'## The following objects are masked from 'package:matrixStats':
##
## colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,
## colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
## colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
## colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
## colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
## colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
## colWeightedMeans, colWeightedMedians, colWeightedSds,
## colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,
## rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,
## rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,
## rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,
## rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
## rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
## rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
## rowWeightedSds, rowWeightedVars## The following object is masked from 'package:Biobase':
##
## rowMedianslibrary("dplyr")
library("apeglm")countData <- read.csv("GLDS-102_rna_seq_Unnormalized_Counts.csv")
colData <- read.csv("PHENO_DATA_mouse.csv", sep = ",", row.names = 1)now we need to add leveling factors to the data
all(rownames(colData)) %in% colnames(countData)## Warning in all(rownames(colData)): coercing argument of type 'character' to
## logical## [1] FALSEdds <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~group)## Error in DESeqDataSetFromMatrix(countData = countData, colData = colData, : ncol(countData) == nrow(colData) is not TRUEdds <- dds[rowSums(counts(dds)>2) >=4]## Error in eval(expr, envir, enclos): object 'dds' not founddds <- DESeq(dds)## Error in eval(expr, envir, enclos): object 'dds' not foundres <- results(dds)## Error in eval(expr, envir, enclos): object 'dds' not foundresLFC <- lfcShrink(dds, coef = 2)## Error in eval(expr, envir, enclos): object 'dds' not found(resOrdered <- res[order(res$padj),])## Error in eval(expr, envir, enclos): object 'res' not foundsummary(res)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'res' not foundFLT_Vs_GC <- as.data.frame(res$log2FoldChange)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'res' not foundhead(FLT_Vs_GC)## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'FLT_Vs_GC' not foundplotMA(resLFC, ylim = c(-2,2))## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'plotMA': object 'resLFC' not foundpdf(file = "MA_Plot_FLT_vs_GC.pdf")
dev.off()## png
## 2write.csv(as.data.frame(resOrdered), file = "Mouse_DESeq.csv")## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'resOrdered' not foundcustom transformation
dds <- estimateSizeFactors(dds)## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'estimateSizeFactors': object 'dds' not foundse <- SummarizedExperiment(log2(counts(dds, normalize = TRUE) +1), colData = colData(dds))## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'counts': object 'dds' not foundpdf(file = "PCA_PLOT_FLT_vs_GC.pdf")
plotPCA(DESeqTransform(se), intgroup = "group")## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'plotPCA': 'SummarizedExperiment' must be a RangedSummarizedExperiment objectlets load our required packages
library("AnnotationDbi")
library("org.Mm.eg.db")columns(org.Mm.eg.db)## [1] "ACCNUM" "ALIAS" "ENSEMBL" "ENSEMBLPROT" "ENSEMBLTRANS"
## [6] "ENTREZID" "ENZYME" "EVIDENCE" "EVIDENCEALL" "GENENAME"
## [11] "GENETYPE" "GO" "GOALL" "IPI" "MGI"
## [16] "ONTOLOGY" "ONTOLOGYALL" "PATH" "PFAM" "PMID"
## [21] "PROSITE" "REFSEQ" "SYMBOL" "UNIPROT"foldchanges <- as.data.frame(res$log2FoldChange, row.names = row.names(res))## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'res' not foundres$symbol = mapIds(org.Mm.eg.db,
keys = row.names(res),
column = "SYMBOL",
keytype = "ENSEMBL",
multiVals = "first")## Error in eval(expr, envir, enclos): object 'res' not foundres$entrez = mapIds(org.Mm.eg.db,
keys = row.names(res),
column = "ENTREZID",
keytype = "ENSEMBL",
multiVals = "first")## Error in eval(expr, envir, enclos): object 'res' not foundres$symbol = mapIds(org.Mm.eg.db,
keys = row.names(res),
column = "GENENAME",
keytype = "ENSEMBL",
multiVals = "first")## Error in eval(expr, envir, enclos): object 'res' not foundhead(res, 10) ## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'head': object 'res' not foundPathview packages
library("pathview")
library("gage")
library("gageData")
data("kegg.sets.mm")
data("go.subs.mm")
foldchanges <- res$log2FoldChange## Error in eval(expr, envir, enclos): object 'res' not foundnames(foldchanges) = res$entrez## Error in eval(expr, envir, enclos): object 'res' not foundhead(foldchanges)## 4902 1062 3038 364 14467 16306
## -1.36 0.89 0.95 -0.83 2.26 -1.99map the results
keggres <- gage(foldchanges, gsets = kegg.sets.mm, same.dir = TRUE)
lapply(keggres, head)## $greater
## p.geomean stat.mean p.val
## mmu04973 Carbohydrate digestion and absorption 0.045 1.8 0.045
## mmu04960 Aldosterone-regulated sodium reabsorption 0.050 1.8 0.050
## mmu04662 B cell receptor signaling pathway 0.052 1.7 0.052
## mmu04722 Neurotrophin signaling pathway 0.052 1.7 0.052
## mmu04260 Cardiac muscle contraction 0.076 1.5 0.076
## mmu04914 Progesterone-mediated oocyte maturation 0.077 1.5 0.077
## q.val set.size exp1
## mmu04973 Carbohydrate digestion and absorption 0.75 11 0.045
## mmu04960 Aldosterone-regulated sodium reabsorption 0.75 11 0.050
## mmu04662 B cell receptor signaling pathway 0.75 18 0.052
## mmu04722 Neurotrophin signaling pathway 0.75 27 0.052
## mmu04260 Cardiac muscle contraction 0.75 21 0.076
## mmu04914 Progesterone-mediated oocyte maturation 0.75 19 0.077
##
## $less
## p.geomean
## mmu05219 Bladder cancer 0.089
## mmu04650 Natural killer cell mediated cytotoxicity 0.112
## mmu04540 Gap junction 0.163
## mmu05412 Arrhythmogenic right ventricular cardiomyopathy (ARVC) 0.174
## mmu04520 Adherens junction 0.203
## mmu05100 Bacterial invasion of epithelial cells 0.221
## stat.mean p.val
## mmu05219 Bladder cancer -1.38 0.089
## mmu04650 Natural killer cell mediated cytotoxicity -1.23 0.112
## mmu04540 Gap junction -1.00 0.163
## mmu05412 Arrhythmogenic right ventricular cardiomyopathy (ARVC) -0.95 0.174
## mmu04520 Adherens junction -0.84 0.203
## mmu05100 Bacterial invasion of epithelial cells -0.78 0.221
## q.val set.size
## mmu05219 Bladder cancer 0.95 18
## mmu04650 Natural killer cell mediated cytotoxicity 0.95 36
## mmu04540 Gap junction 0.95 28
## mmu05412 Arrhythmogenic right ventricular cardiomyopathy (ARVC) 0.95 26
## mmu04520 Adherens junction 0.95 22
## mmu05100 Bacterial invasion of epithelial cells 0.95 25
## exp1
## mmu05219 Bladder cancer 0.089
## mmu04650 Natural killer cell mediated cytotoxicity 0.112
## mmu04540 Gap junction 0.163
## mmu05412 Arrhythmogenic right ventricular cardiomyopathy (ARVC) 0.174
## mmu04520 Adherens junction 0.203
## mmu05100 Bacterial invasion of epithelial cells 0.221
##
## $stats
## stat.mean exp1
## mmu04973 Carbohydrate digestion and absorption 1.8 1.8
## mmu04960 Aldosterone-regulated sodium reabsorption 1.8 1.8
## mmu04662 B cell receptor signaling pathway 1.7 1.7
## mmu04722 Neurotrophin signaling pathway 1.7 1.7
## mmu04260 Cardiac muscle contraction 1.5 1.5
## mmu04914 Progesterone-mediated oocyte maturation 1.5 1.5greaters <- keggres$greater
lessers <- keggres$lesskeggrespathways <- data.frame(id = row.names(keggres$greater), keggres$greater) %>%
tbl_df() %>%
filter(row_number() <= 3) %>%
.$id %>%
as.character()## Warning: `tbl_df()` was deprecated in dplyr 1.0.0.
## ℹ Please use `tibble::as_tibble()` instead.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.keggrespathways## [1] "mmu04973 Carbohydrate digestion and absorption"
## [2] "mmu04960 Aldosterone-regulated sodium reabsorption"
## [3] "mmu04662 B cell receptor signaling pathway"keggressids <- substr(keggrespathways, start = 1, stop = 8)
keggresids## Error in eval(expr, envir, enclos): object 'keggresids' not foundPlot
plot_pathway = function(pid) pathview(gene.data = foldchanges, pathway.id = pid, species = "mouse", new.signature = FALSE)Other Tools
Protein Alignment
Run data:
library("msa")Create an allignment:
seq <- readAAStringSet("hglobin.fa")
seq## AAStringSet object of length 3:
## width seq names
## [1] 142 MVLSPADKTNVKAAWGKVGAHAG...PAVHASLDKFLASVSTVLTSKYR HBA_HUMAN
## [2] 142 MVLSGEDKSNIKAAWGKIGGHGA...PAVHASLDKFLASVSTVLTSKYR HBA_MOUSE
## [3] 142 MSLTRTERTIILSLWSKISTQAD...ADAHAAWDKFLSIVSGVLTEKYR HBAZ_CAPHIalignments <- msa(seq, method = "ClustalW")## use default substitution matrixalignments## CLUSTAL 2.1
##
## Call:
## msa(seq, method = "ClustalW")
##
## MsaAAMultipleAlignment with 3 rows and 142 columns
## aln names
## [1] MVLSPADKTNVKAAWGKVGAHAGEYG...FTPAVHASLDKFLASVSTVLTSKYR HBA_HUMAN
## [2] MVLSGEDKSNIKAAWGKIGGHGAEYG...FTPAVHASLDKFLASVSTVLTSKYR HBA_MOUSE
## [3] MSLTRTERTIILSLWSKISTQADVIG...FTADAHAAWDKFLSIVSGVLTEKYR HBAZ_CAPHI
## Con MVLS??DKTNIKAAWGKIG?HA?EYG...FTPAVHASLDKFLASVSTVLTSKYR ConsensusmsaPrettyPrint(alignments, output = "pdf", showNames = "left",
showLogo = "none", askForOverwrite = FALSE,
verbose = TRUE, file = "whole_align.pdf")## Multiple alignment written to temporary file /tmp/RtmpcjLNZi/seq2614aa5cf9f.fasta## File whole_align.tex created## Warning in texi2dvi(texfile, quiet = !verbose, pdf = identical(output, "pdf"),
## : texi2dvi script/program not available, using emulation## Output file whole_align.pdf createdmsaPrettyPrint(alignments, c(10,30), output = "pdf",
file = "Zoomed_align.pdf", showLogo = "top", askForOverwrite = FALSE,
verbose = TRUE)## Multiple alignment written to temporary file /tmp/RtmpcjLNZi/seq26145f0bdb30.fasta## File Zoomed_align.tex created## Warning in texi2dvi(texfile, quiet = !verbose, pdf = identical(output, "pdf"),
## : texi2dvi script/program not available, using emulation## Output file Zoomed_align.pdf createdLets make a tree from our alignment
library(ape)##
## Attaching package: 'ape'## The following object is masked from 'package:imager':
##
## where## The following object is masked from 'package:Biostrings':
##
## complement## The following object is masked from 'package:dplyr':
##
## wherelibrary(seqinr)##
## Attaching package: 'seqinr'## The following objects are masked from 'package:ape':
##
## as.alignment, consensus## The following object is masked from 'package:matrixStats':
##
## count## The following object is masked from 'package:Biostrings':
##
## translate## The following object is masked from 'package:limma':
##
## zscore## The following object is masked from 'package:dplyr':
##
## countConvert to seqinr alignment and get distances to make tree
alignment_seqinr <- msaConvert(alignments, type = "seqinr::alignment")
distances1 <- seqinr::dist.alignment(alignment_seqinr, "identity")
tree <- ape::nj(distances1)
plot(tree, main = "Phlogenetic Tree of HBA Sequences")
In the first step, we load the libraries and sequence into log sequence.
This is a DNAStringSet object:
library(DECIPHER)## Loading required package: RSQLite## Loading required package: parallelsetwd("~/Desktop/classroom/myfiles")
long_seq <- readDNAStringSet("plastid_genomes.fa")
long_seq## DNAStringSet object of length 5:
## width seq names
## [1] 130584 GGCATAAGCTATCTTCCCAAAGG...ATGATTCAAACATAAAAGTCCT NC_018523.1 Sacch...
## [2] 161592 ATGGGCGAACGACGGGAATTGAA...AAAGAAAAAAAAATAGGAGTAA NC_022431.1 Ascle...
## [3] 117672 ATGAGTACAACTCGAAAGTCCAC...TTGATTTCATCCACAAACGAAC NC_022259.1 Nanno...
## [4] 154731 TTATCCATTTGTAGATGGAACTT...CATATACACTAAGACAAAAGTC NC_022417.1 Cocos...
## [5] 156618 GGGCGAACGACGGGAATTGAACC...CCTTTTGTAGCGAATCCGTTAT NC_022459.1 Camel...Lets build a temp SQLite database
Seqs2DB(long_seq, "XStringSet", "long_db", names(long_seq))## Adding 5 sequences to the database.
##
## Added 5 new sequences to table Seqs.
## 55 total sequences in table Seqs.
## Time difference of 0.22 secsSynteny
Now that we built the data base, we can do the following: Find the syntetic blocks
synteny <- FindSynteny("long_db") ## ================================================================================
##
## Time difference of 9.9 secsView blocks with plotting
pairs(synteny)
plot(synteny)
Make an alignment file
alignment <- AlignSynteny(synteny, "long_db")## ================================================================================
##
## Time difference of 77 secsCreate structure holding all aligned synthenic blocks for a pair of sequences
block <- unlist(alignment[[1]])We can write to one file to align
writeXStringSet(block, "genome_blocks_out.fa")Unannotated Regions
Load in data:
library(locfit)## locfit 1.5-9.8 2023-06-11##
## Attaching package: 'locfit'## The following object is masked from 'package:purrr':
##
## nonelibrary(Rsamtools)Create a function that will load a gene region in a GFF
setwd("~/Desktop/classroom/myfiles")
get_annotated_regions_from_gff <- function(file.name) {
gff <- rtracklayer::import.gff(file.name)
as(gff, "GRanges")
}Get count in windows across the genome in 500bp segments
whole_genome <- csaw::windowCounts(
file.path("~/Desktop/classroom/myfiles", "windows.bam"),
bin = TRUE,
filter = 0,
width = 500,
param = csaw::readParam(
minq = 20, #determines what is a passing read
dedup = TRUE, #removes PCR duplicate
pe = "both"
)
)Since this is a single column of data, lets rename it
colnames(whole_genome) <- c("small_data")
annotated_regions <- get_annotated_regions_from_gff(file.path("~/Desktop/classroom/myfiles", "genes.gff"))Now that we have windows of high expression, we want to see if any of them overlap with annotated regions
library(IRanges)
library(SummarizedExperiment)Find the overlap between the windows we made
windows_in_genes <- IRanges::overlapsAny(
SummarizedExperiment::rowRanges(whole_genome),
annotated_regions)
windows_in_genes## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE TRUE FALSEHere we subset the whole_genome object into annotated and nonannotated regions
annotated_window_counts <- whole_genome[windows_in_genes,]
non_annotated_window_counts <- whole_genome[!windows_in_genes,]Use assay () to extract the actual counts from the Granges object
assay(non_annotated_window_counts)## small_data
## [1,] 0
## [2,] 31
## [3,] 25
## [4,] 0
## [5,] 0
## [6,] 24
## [7,] 25
## [8,] 0
## [9,] 0In this step, we use Rsamtools Puleup function to get a per-base coverage dataframe.
library(bumphunter)
pile_df <- Rsamtools::pileup(file.path("~/Desktop/classroom/myfiles", "windows.bam"))This step groups the reads with certain distance of each other into a cluster. We give the sequence names, position, and distance
clusters <- bumphunter::clusterMaker(pile_df$seqnames, pile_df$pos, maxGap = 100)## Error in eval(expr, envir, enclos): object 'pile_df' not foundtable(clusters)## Error in eval(expr, envir, enclos): object 'clusters' not foundIn this step we will map the reads to the regions we found in the genome
bumphunter::regionFinder(pile_df$count, pile_df$seqnames, pile_df$pos, clusters, cutoff = 1)## Error in eval(expr, envir, enclos): object 'pile_df' not foundPhylogenetic Analysis
Treeio
Load in data:
library(ggplot2)
library(ggtree)
library(treeio)First we will load raw tree data
itol <- ape::read.tree("itol.nwk")Now we print out very basic phylogenetic tree
ggtree(itol)## Error in ggtree(itol): could not find function "ggtree"We can make it circular
ggtree(itol, layout = "circular") ## Error in ggtree(itol, layout = "circular"): could not find function "ggtree"We can change left/right, up/down
ggtree(itol) + coord_flip() + scale_x_reverse()## Error in ggtree(itol): could not find function "ggtree"By using the geom_tipla we can add names to the ends of tips
ggtree(itol) + geom_tiplab(color = "indianred", size = 1.5)## Error in ggtree(itol): could not find function "ggtree"We can annotate clades by adding geom_strip
ggtree(itol, layout = "unrooted") + geom_highlight(node = 11, type = "encircle", fill = "steelblue")## Error in ggtree(itol, layout = "unrooted"): could not find function "ggtree"install.packages('BAMMtools')
library(BAMMtools) We can use the MRCA function to find the node we want
ggtree(itol, layout = "unrooted") + geom_highlight(node = 206, type = "encircle", fill = "steelblue")## Error in ggtree(itol, layout = "unrooted"): could not find function "ggtree"Tree space
Load in data:
library(ape)
library(adegraphics)
library(treespace)Load tree files into multiphylo object
treefiles <- list.files(file.path("~/Desktop/classroom/myfiles/genetrees", "genetrees"), full.names = TRUE)
tree_list <- lapply(treefiles, read.tree)
class(tree_list)## [1] "list"class(tree_list) <- "multiPhylo"
class(tree_list)## [1] "multiPhylo"Now we can compute the kendall-coljin distance between trees. This function does ALOT OF analyses. First runs pairwise comparison, then carries out clustering
comparison <- treespace(tree_list, nf = 3)## Error in treespace(tree_list, nf = 3): could not find function "treespace"We can plot the pairwise distances between trees with table.image
table.image(comparison$D, nclass = 25)## Error in table.image(comparison$D, nclass = 25): could not find function "table.image"Print PCA and clusters
plotGroves(comparison$pco, lab.show = TRUE, lab.cex = 1.5)## Error in plotGroves(comparison$pco, lab.show = TRUE, lab.cex = 1.5): could not find function "plotGroves"groves <- findGroves(comparison, nclust = 6)## Error in findGroves(comparison, nclust = 6): could not find function "findGroves"plotGroves(groves)## Error in plotGroves(groves): could not find function "plotGroves"Subtrees
Load in data:
library(ape)
newick <- read.tree("mammal_tree.nwk")
one <- subtrees(newick)
plot(newick)
Extract the tree manually
small_tree <- extract.clade(newick, 9)Now what if we want to bind two trees together
new_tree <- bind.tree(newick, small_tree, 3)
plot(new_tree)
Phangorn
Load in data:
library(Biostrings)
library(msa)
library(phangorn)First we will load the sequences into a “seqs” variable
seqs <- readAAStringSet("abc.fa")Lets construct an alignment
aln <- msa::msa(seqs, method = c("ClustalOmega"))## using GonnetTo create a tree, we need to convert the alignment to a phyData objects
aln <- as.phyDat(aln, type = "AA")
class(aln)## [1] "phyDat"In this step, we are actually making the trees
dist_mat <- dist.ml(aln)Now we pass the distance matrix to upgma to make tree
upgma_tree <- upgma(dist_mat)
plot(upgma_tree, main = "UPGMA")
Conversly pass the distance matrix to a neighbpr joining function
nj_tree <- NJ(dist_mat)
plot(nj_tree, main = "NJ")
Compute bootstrap support for the branches– these are confidence
intervals
fit <- pml(nj_tree, aln)
bootstraps <- bootstrap.phyDat(aln, FUN = function(x) {NJ(dist.ml(x))}, bs =100)
plotBS(nj_tree, bootstraps, p = 10)
GWAS
Variant Tools
Load in data:
library(GenomicRanges)
library(gmapR)
library(rtracklayer)
library(VariantAnnotation)##
## Attaching package: 'VariantAnnotation'## The following object is masked from 'package:stringr':
##
## fixed## The following object is masked from 'package:base':
##
## tabulatelibrary(VariantTools)Load in other data sets
bam_file <- file.path("~/Desktop/classroom/myfiles", "hg17_snps.bam")
fasta_file <- file.path("~/Desktop/classroom/myfiles", "chr17_83k.fa")Now we need to set up genome object and perameters
fa <- rtracklayer::FastaFile(fasta_file)Create GMapGenome object– describes the genome to later varient calling function
genome <- gmapR::GmapGenome(fa, create = TRUE)## NOTE: genome 'chr17_83k' already exists, not overwritingThe next steps are to set parameters for what is considered variant, there can be alot of fine tuning to this function– we are using standard settings
qual_params <- TallyVariantsParam(
genome = genome,
minimum_mapq = 20
)
var_params <- VariantCallingFilters(read.count = 19, p.lower = 0.01)Now we can use callVariants from function in accordance with our parameters we defines
called_variants <- callVariants(bam_file,
qual_params,
calling.filter = var_params)
head(called_variants)## VRanges object with 6 ranges and 17 metadata columns:
## seqnames ranges strand ref alt totalDepth
## <Rle> <IRanges> <Rle> <character> <characterOrRle> <integerOrRle>
## [1] NC_000017.10 64 * G T 759
## [2] NC_000017.10 69 * G T 812
## [3] NC_000017.10 70 * G T 818
## [4] NC_000017.10 73 * T A 814
## [5] NC_000017.10 77 * T A 802
## [6] NC_000017.10 78 * G T 798
## refDepth altDepth sampleNames softFilterMatrix | n.read.pos
## <integerOrRle> <integerOrRle> <factorOrRle> <matrix> | <integer>
## [1] 739 20 <NA> | 17
## [2] 790 22 <NA> | 19
## [3] 796 22 <NA> | 20
## [4] 795 19 <NA> | 13
## [5] 780 22 <NA> | 19
## [6] 777 21 <NA> | 17
## n.read.pos.ref raw.count.total count.plus count.plus.ref count.minus
## <integer> <integer> <integer> <integer> <integer>
## [1] 64 759 20 739 0
## [2] 69 812 22 790 0
## [3] 70 818 22 796 0
## [4] 70 814 19 795 0
## [5] 70 802 22 780 0
## [6] 70 798 21 777 0
## count.minus.ref count.del.plus count.del.minus read.pos.mean
## <integer> <integer> <integer> <numeric>
## [1] 0 0 0 30.9000
## [2] 0 0 0 40.7273
## [3] 0 0 0 34.7727
## [4] 0 0 0 36.1579
## [5] 0 0 0 38.3636
## [6] 0 0 0 39.7143
## read.pos.mean.ref read.pos.var read.pos.var.ref mdfne mdfne.ref
## <numeric> <numeric> <numeric> <numeric> <numeric>
## [1] 32.8755 318.558 347.804 NA NA
## [2] 35.4190 377.004 398.876 NA NA
## [3] 36.3442 497.762 402.360 NA NA
## [4] 36.2176 519.551 402.843 NA NA
## [5] 36.0064 472.327 397.070 NA NA
## [6] 35.9241 609.076 390.463 NA NA
## count.high.nm count.high.nm.ref
## <integer> <integer>
## [1] 20 738
## [2] 22 789
## [3] 22 796
## [4] 19 769
## [5] 22 780
## [6] 21 777
## -------
## seqinfo: 1 sequence from chr17_83k genome
## hardFilters(4): nonRef nonNRef readCount likelihoodRatioNow, we move on to annotation and load the feature position info
get_annotated_regions_from_gff <- function(file_name) {
gff <- rtracklayer::import.gff(file_name)
as(gff, "GRanges")
}You can also load this data from a bed file
genes <- get_annotated_regions_from_gff("chr17.83k.gff3")Now we can calculate which variants overlap which genes
overlaps <- GenomicRanges::findOverlaps(called_variants, genes)
overlaps## Hits object with 12684 hits and 0 metadata columns:
## queryHits subjectHits
## <integer> <integer>
## [1] 35176 1
## [2] 35176 2
## [3] 35176 3
## [4] 35177 1
## [5] 35177 2
## ... ... ...
## [12680] 40944 2
## [12681] 40944 7
## [12682] 40945 1
## [12683] 40945 2
## [12684] 40945 7
## -------
## queryLength: 44949 / subjectLength: 8Lastly we subset the genes with the list of overlaps
genes[subjectHits(overlaps)]## GRanges object with 12684 ranges and 20 metadata columns:
## seqnames ranges strand | source type score
## <Rle> <IRanges> <Rle> | <factor> <factor> <numeric>
## [1] NC_000017.10 64099-76866 - | havana ncRNA_gene NA
## [2] NC_000017.10 64099-76866 - | havana lnc_RNA NA
## [3] NC_000017.10 64099-65736 - | havana exon NA
## [4] NC_000017.10 64099-76866 - | havana ncRNA_gene NA
## [5] NC_000017.10 64099-76866 - | havana lnc_RNA NA
## ... ... ... ... . ... ... ...
## [12680] NC_000017.10 64099-76866 - | havana lnc_RNA NA
## [12681] NC_000017.10 76723-76866 - | havana exon NA
## [12682] NC_000017.10 64099-76866 - | havana ncRNA_gene NA
## [12683] NC_000017.10 64099-76866 - | havana lnc_RNA NA
## [12684] NC_000017.10 76723-76866 - | havana exon NA
## phase ID Name biotype
## <integer> <character> <character> <character>
## [1] <NA> gene:ENSG00000280279 AC240565.2 lincRNA
## [2] <NA> transcript:ENST00000.. AC240565.2-201 lincRNA
## [3] <NA> <NA> ENSE00003759547 <NA>
## [4] <NA> gene:ENSG00000280279 AC240565.2 lincRNA
## [5] <NA> transcript:ENST00000.. AC240565.2-201 lincRNA
## ... ... ... ... ...
## [12680] <NA> transcript:ENST00000.. AC240565.2-201 lincRNA
## [12681] <NA> <NA> ENSE00003756684 <NA>
## [12682] <NA> gene:ENSG00000280279 AC240565.2 lincRNA
## [12683] <NA> transcript:ENST00000.. AC240565.2-201 lincRNA
## [12684] <NA> <NA> ENSE00003756684 <NA>
## description gene_id logic_name version
## <character> <character> <character> <character>
## [1] novel transcript ENSG00000280279 havana 1
## [2] <NA> <NA> <NA> 1
## [3] <NA> <NA> <NA> 1
## [4] novel transcript ENSG00000280279 havana 1
## [5] <NA> <NA> <NA> 1
## ... ... ... ... ...
## [12680] <NA> <NA> <NA> 1
## [12681] <NA> <NA> <NA> 1
## [12682] novel transcript ENSG00000280279 havana 1
## [12683] <NA> <NA> <NA> 1
## [12684] <NA> <NA> <NA> 1
## Parent tag transcript_id
## <CharacterList> <character> <character>
## [1] <NA> <NA>
## [2] gene:ENSG00000280279 basic ENST00000623180
## [3] transcript:ENST00000.. <NA> <NA>
## [4] <NA> <NA>
## [5] gene:ENSG00000280279 basic ENST00000623180
## ... ... ... ...
## [12680] gene:ENSG00000280279 basic ENST00000623180
## [12681] transcript:ENST00000.. <NA> <NA>
## [12682] <NA> <NA>
## [12683] gene:ENSG00000280279 basic ENST00000623180
## [12684] transcript:ENST00000.. <NA> <NA>
## transcript_support_level constitutive ensembl_end_phase ensembl_phase
## <character> <character> <character> <character>
## [1] <NA> <NA> <NA> <NA>
## [2] 5 <NA> <NA> <NA>
## [3] <NA> 1 -1 -1
## [4] <NA> <NA> <NA> <NA>
## [5] 5 <NA> <NA> <NA>
## ... ... ... ... ...
## [12680] 5 <NA> <NA> <NA>
## [12681] <NA> 1 -1 -1
## [12682] <NA> <NA> <NA> <NA>
## [12683] 5 <NA> <NA> <NA>
## [12684] <NA> 1 -1 -1
## exon_id rank
## <character> <character>
## [1] <NA> <NA>
## [2] <NA> <NA>
## [3] ENSE00003759547 5
## [4] <NA> <NA>
## [5] <NA> <NA>
## ... ... ...
## [12680] <NA> <NA>
## [12681] ENSE00003756684 1
## [12682] <NA> <NA>
## [12683] <NA> <NA>
## [12684] ENSE00003756684 1
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsPredicting ORF
Load in data:
library(Biostrings)
library(systemPipeR)## Loading required package: ShortRead## Loading required package: BiocParallel## Loading required package: GenomicAlignments##
## Attaching package: 'GenomicAlignments'## The following object is masked from 'package:dplyr':
##
## last##
## Attaching package: 'ShortRead'## The following objects are masked from 'package:locfit':
##
## left, right## The following object is masked from 'package:ape':
##
## zoom## The following object is masked from 'package:imager':
##
## clean## The following object is masked from 'package:magrittr':
##
## functions## The following object is masked from 'package:oligo':
##
## intensity## The following objects are masked from 'package:oligoClasses':
##
## chromosome, position## The following object is masked from 'package:affy':
##
## intensity## The following object is masked from 'package:dplyr':
##
## id## The following object is masked from 'package:purrr':
##
## compose## The following object is masked from 'package:tibble':
##
## view##
## Attaching package: 'systemPipeR'## The following object is masked from 'package:VariantAnnotation':
##
## reference## The following object is masked from 'package:DESeq2':
##
## resultsLets load the data into DNAStrings object
dna_object <- readDNAStringSet("arabidopsis_chloroplast.fa")Now lets predict the orf with predORF()
predict_orfs <- predORF(dna_object, n = 'all', type = 'gr', mode = 'ORF', strand = 'both',
longest_disjoint = TRUE)
predict_orfs## GRanges object with 2501 ranges and 2 metadata columns:
## seqnames ranges strand | subject_id inframe2end
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## 1 chloroplast 86762-93358 + | 1 2
## 1162 chloroplast 2056-2532 - | 1 3
## 2 chloroplast 72371-73897 + | 2 2
## 1163 chloroplast 77901-78362 - | 2 1
## 3 chloroplast 54937-56397 + | 3 3
## ... ... ... ... . ... ...
## 2497 chloroplast 129757-129762 - | 1336 3
## 2498 chloroplast 139258-139263 - | 1337 3
## 2499 chloroplast 140026-140031 - | 1338 3
## 2500 chloroplast 143947-143952 - | 1339 3
## 2501 chloroplast 153619-153624 - | 1340 3
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsTo filter out erronous ORFs, we do simulation
bases <- c("A", "T", "G", "C")
raw_seq_string <- strsplit(as.character(dna_object), "")Now we needs to ensure that our randome nucleotides match the proportion of nuc. in our chloroplast genome so we have no bias
seq_length <- width(dna_object[1])
counts <- lapply(bases, function(x) {sum(grepl(x, raw_seq_string))})
probs <- unlist(lapply(counts, function(base_count){signif(base_count/seq_length, 2)}))
probs ## [1] 6.5e-06 6.5e-06 6.5e-06 6.5e-06Now we can build our function to simulate a genome
get_longest_orf_in_random_genome <- function(x,
length = 1000,
probs = c(0.25, 0.25, 0.25, 0.25),
bases = c("A", "T", "G", "C")){}Here we create our random genome
random_genome <- paste0(sample(bases, replace = TRUE, prob = probs), collapse = "")
random_dna_object <- DNAStringSet(random_genome)
names(random_dna_object) <- c("random_dna_string")Now we use the function we just created to predict the ORFs in 10 random genomes
random_lenghts <- unlist(lapply(1:10, get_longest_orf_in_random_genome, length = seq_length, probs = probs, bases = bases))Lets pull out the longest length from 10 simulations
longest_random_orf <- max(random_lenghts)## Warning in max(random_lenghts): no non-missing arguments to max; returning -InfLets only keep frames longer in chloroplast genome
keep <- width(predict_orfs) > longest_random_orf
orfs_to_keep <- predict_orfs[keep]
orfs_to_keep## GRanges object with 2501 ranges and 2 metadata columns:
## seqnames ranges strand | subject_id inframe2end
## <Rle> <IRanges> <Rle> | <integer> <numeric>
## 1 chloroplast 86762-93358 + | 1 2
## 1162 chloroplast 2056-2532 - | 1 3
## 2 chloroplast 72371-73897 + | 2 2
## 1163 chloroplast 77901-78362 - | 2 1
## 3 chloroplast 54937-56397 + | 3 3
## ... ... ... ... . ... ...
## 2497 chloroplast 129757-129762 - | 1336 3
## 2498 chloroplast 139258-139263 - | 1337 3
## 2499 chloroplast 140026-140031 - | 1338 3
## 2500 chloroplast 143947-143952 - | 1339 3
## 2501 chloroplast 153619-153624 - | 1340 3
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsWrite this to file
extracted_orfs <- BSgenome::getSeq(dna_object, orfs_to_keep)
names(extracted_orfs) <- paste0("orf_", 1:length(orfs_to_keep))
writeXStringSet(extracted_orfs, "saved_orfs.fa")KaryoploteR
Load in data:
library(karyoploteR)## Loading required package: regioneRlibrary(GenomicRanges)Set up genome object for our karyotype
genome_df <- data.frame(
chr = paste0("chr", 1:5),
start = rep(1, 5),
end = c(34964571, 22037565, 25499034, 20862711, 31270811)
)Now we convert datafram to a granges subject
genome_gr <- makeGRangesFromDataFrame(genome_df)Lets create some snp positions to map out
snp_pos <- sample(1:1e7, 25)
snps <- data.frame(
chr = paste0("chr", sample(1:5, 25, replace = TRUE)),
start = snp_pos,
end = snp_pos
)Again we convert the data frame to granges
snps_gr <- makeGRangesFromDataFrame(snps)Lets create some snp labels
snp_labels <- paste0("snp_", 1:25)Set margine of our plot
plot.params <- getDefaultPlotParams(plot.type = 1)
plot.params$data1outmargin <- 600Plot snps
kp <- plotKaryotype(genome= genome_gr, plot.type = 1, plot.params = plot.params)## No predefined canonical chromosomes found for the requested genome. Applying a heuristic chromosome filtering.## To get the unfiltered genome, please set chromosomes="all" in the plotKaryotype callkpPlotMarkers(kp, snps_gr, labels = snp_labels)
We can also add some numeric date– generate 100 random numbers that plot
100 windows on chromosome 4
numeric_data <- data.frame(
y = rnorm(100, mean = 1, sd = 0.5),
chr = rep("chr4", 100),
start = seq(1,20862711, 20862711/100),
end = seq(1,20682711, 20862711/100)
)Lets make the data a granges object
numeric_data_gr <- makeGRangesFromDataFrame(numeric_data)Set plot parameters again
plot.params <- getDefaultPlotParams(plot.type = 2)
plot.params$data1outmargin <- 800
plot.params$data2outmargin <- 800
plot.params$topmargin <- 800
kp <- plotKaryotype(genome = genome_gr, plot.type = 1, plot.params = plot.params)## No predefined canonical chromosomes found for the requested genome. Applying a heuristic chromosome filtering.## To get the unfiltered genome, please set chromosomes="all" in the plotKaryotype callkpPlotMarkers(kp, snps_gr, labels = snp_labels)
kpLines(kp, numeric_data_gr, y = numeric_data$y, data.panel = 2)