I am using a code based on Deseq2. One of my goals is to plot a heatmap of data.
heatmap.data <- counts(dds)[topGenes,]
The error I am getting is
Error in counts(dds)[topGenes, ]: subscript out of bounds
the first few line sof my counts(dds) function looks like this.
99h1 99h2 99h3 99h4 wth1 wth2
ENSDARG00000000002 243 196 187 117 91 96
ENSDARG00000000018 42 55 53 32 48 48
ENSDARG00000000019 91 91 108 64 95 94
ENSDARG00000000068 3 10 10 10 30 21
ENSDARG00000000069 55 47 43 53 51 30
ENSDARG00000000086 46 26 36 18 37 29
ENSDARG00000000103 301 289 289 199 347 386
ENSDARG00000000151 18 19 17 14 22 19
ENSDARG00000000161 16 17 9 19 10 20
ENSDARG00000000175 10 9 10 6 16 12
ENSDARG00000000183 12 8 15 11 8 9
ENSDARG00000000189 16 17 13 10 13 21
ENSDARG00000000212 227 208 259 234 78 69
ENSDARG00000000229 68 72 95 44 71 64
ENSDARG00000000241 71 92 67 76 88 74
ENSDARG00000000324 11 9 6 2 8 9
ENSDARG00000000370 12 5 7 8 0 5
ENSDARG00000000394 390 356 339 283 313 286
ENSDARG00000000423 0 0 2 2 7 1
ENSDARG00000000442 1 1 0 0 1 1
ENSDARG00000000472 16 8 3 5 7 8
ENSDARG00000000476 2 1 2 4 6 3
ENSDARG00000000489 221 203 169 144 84 114
ENSDARG00000000503 133 118 139 89 91 112
ENSDARG00000000529 31 25 17 26 15 24
ENSDARG00000000540 25 17 17 10 28 19
ENSDARG00000000542 15 9 9 6 15 12
How do I ensure all the elements of the top genes are present in it?
When I try to see 20 top genes in the dataset. it looks like a list of genes
6339" "12416" "1241" "3025" "12791" "846" "15090"
[8] "6529" "14564" "4863" "12777" "1122" "7454" "13716"
[15] "5790" "3328" "1231" "13734" "2797" "9072" with the column head V1.
I have used both
topGenes <- read.table("E://mir99h50 Cheng data//topGenesresordered.txt",header = TRUE)
and
topGenes <- read.table("E://mir99h50 Cheng data//topGenesresordered.txt",header = FALSE)
to see if the out of bounds error is removed. However it was of no use. I guess the V1 head is causing the issue.
The top genes function has been generated using the above code snippet.
resordered <- res[order(res$padj),]
#Reorder gene list by increasing pAdj
resordered <- as.data.frame(res[order(res$padj),])
#Filter for genes that are differentially expressed with an FDR < 0.01
ii <- which(res$padj < 0.01)
length(ii)
# Use the rownames() function to get the top 20 differentially expressed genes from our results table
topGenes <- rownames(resordered[1:20,])
topGenes
# Get the counts from the DESeqDataSet using the counts() function
heatmap.data <- counts(dds)[topGenes,]
Perhaps this will do what you want?
counts_dds <- counts(dds)
topgenes <- c("ENSDARG00000000002", "ENSDARG00000000489", "ENSDARG00000000503",
"ENSDARG00000000540", "ENSDARG00000000529", "ENSDARG00000000542")
heatmap.data <- counts_dds[rownames(counts_dds) %in% topgenes,]
If you provide more information it will be easier to advise you on how to fix your problem.
I am sure this is a super easy answer but I am struggling with how to add a column with two different variables to my dataframe. Currently, this is what it looks like
vcv.index model.index par.index grid index estimate se lcl ucl fixed
1 6 6 16 A 16 0.8856724 0.07033280 0.6650468 0.9679751
2 7 7 17 A 17 0.6298118 0.06925471 0.4873052 0.7528014
3 8 8 18 A 18 0.6299359 0.06658557 0.4930263 0.7487169
4 9 9 19 A 19 0.6297988 0.05511771 0.5169948 0.7300157
5 10 10 20 A 20 0.7575811 0.05033490 0.6461758 0.8424612
6 21 21 61 B 61 0.8713467 0.07638687 0.6404598 0.9626184
7 22 22 62 B 62 0.6074379 0.06881230 0.4677827 0.7314827
8 23 23 63 B 63 0.6041054 0.06107520 0.4805279 0.7156792
9 24 24 64 B 64 0.5806565 0.06927308 0.4422237 0.7074601
10 25 25 65 B 65 0.7370944 0.05892108 0.6070620 0.8357394
11 41 41 121 C 121 0.8048479 0.09684385 0.5519097 0.9324759
12 42 42 122 C 122 0.5259547 0.07165218 0.3871380 0.6608721
13 43 43 123 C 123 0.5427100 0.07127273 0.4033255 0.6757137
14 44 44 124 C 124 0.5168820 0.06156392 0.3975561 0.6343132
15 45 45 125 C 125 0.6550049 0.07378403 0.5002851 0.7826343
16 196 196 586 A 586 0.8536314 0.08709394 0.5979992 0.9580976
17 197 197 587 A 587 0.5672194 0.07079508 0.4268452 0.6975725
18 198 198 588 A 588 0.5675415 0.06380445 0.4408540 0.6859714
19 199 199 589 A 589 0.5666874 0.06499899 0.4377071 0.6872233
20 200 200 590 A 590 0.7058542 0.05985868 0.5769484 0.8085177
21 211 211 631 B 631 0.8360614 0.09413427 0.5703031 0.9514472
22 212 212 632 B 632 0.5432872 0.07906200 0.3891364 0.6895701
23 213 213 633 B 633 0.5400994 0.06497607 0.4129055 0.6622759
24 214 214 634 B 634 0.5161692 0.06292706 0.3943257 0.6361202
25 215 215 635 B 635 0.6821667 0.07280044 0.5263841 0.8056298
26 226 226 676 C 676 0.7621875 0.10484478 0.5077465 0.9087471
27 227 227 677 C 677 0.4607440 0.07326970 0.3240229 0.6036386
28 228 228 678 C 678 0.4775168 0.08336433 0.3219349 0.6375872
29 229 229 679 C 679 0.4517655 0.06393339 0.3319262 0.5774725
30 230 230 680 C 680 0.5944330 0.07210672 0.4491995 0.7248303
then I am adding a column with periods 1-5 repeated until reaches the end
with this code
SurJagPred$estimates %<>% mutate(Primary = rep(1:5, 6))
and I also need to add sex( F, M) as well. the numbers 1-15 are female and the 16-30 are male. So overall it should look like this.
> vcv.index model.index par.index grid index estimate se lcl ucl fixed Primary Sex
F
1 6 6 16 A 16 0.8856724 0.07033280 0.6650468 0.9679751 1 F
2 7 7 17 A 17 0.6298118 0.06925471 0.4873052 0.7528014 2 F
3 8 8 18 A 18 0.6299359 0.06658557 0.4930263 0.7487169 3 F
4 9 9 19 A 19 0.6297988 0.05511771 0.5169948 0.7300157 4 F
We can use rep with each on a vector of values to replicate each element of the vector to that many times
SurJagPred$estimates %<>%
mutate(Sex = rep(c("F", "M"), each = 15))
I have a data frame of baseball player information:
playerID nameFirst nameLast bats throws yearID stint teamID lgID G AB R H X2B X3B HR RBI SB CS BB SO IBB
81955 rolliji01 Jimmy Rollins B R 2007 1 PHI NL 162 716 139 212 38 20 30 94 41 6 49 85 5
103358 wilsowi02 Willie Wilson B R 1980 1 KCA AL 161 705 133 230 28 15 3 49 79 10 28 81 3
93082 suzukic01 Ichiro Suzuki L R 2004 1 SEA AL 161 704 101 262 24 5 8 60 36 11 49 63 19
83973 samueju01 Juan Samuel R R 1984 1 PHI NL 160 701 105 191 36 19 15 69 72 15 28 168 2
15201 cashda01 Dave Cash R R 1975 1 PHI NL 162 699 111 213 40 3 4 57 13 6 56 34 5
75531 pierrju01 Juan Pierre L L 2006 1 CHN NL 162 699 87 204 32 13 3 40 58 20 32 38 0
HBP SH SF GIDP average
81955 7 0 6 11 0.2960894
103358 6 5 1 4 0.3262411
93082 4 2 3 6 0.3721591
83973 7 0 1 6 0.2724679
15201 4 0 7 8 0.3047210
75531 8 10 1 6 0.2918455
I want to return a maximum value of the batting average ('average') column where the at-bats ('AB') are greater than 100. There are also 'NaN' in the average column.
If you want to return the entire row for which the two conditions are TRUE, you can do something like this.
library(tidyverse)
data <- tibble(
AB = sample(seq(50, 150, 10), 10),
avg = c(runif(9), NaN)
)
data %>%
filter(AB >= 100) %>%
filter(avg == max(avg, na.rm = TRUE))
Where the first filter is to only keep rows where AB is greater than or equal to 100 and the second filter is to select the entire row where it is max. If you want to to only get the maximum value, you can do something like this:
data %>%
filter(AB >= 100) %>%
summarise(max = max(avg, na.rm = TRUE))
I have a data set with closing and opening dates of public schools in California. Available here or dput() at the bottom of the question. The data also lists what type of school it is and where it is. I am trying to create a running total column which also takes into account school closings as well as school type.
Here is the solution I've come up with, which basically entails me encoding a lot of different 1's and 0's based on the conditions using ifelse:
# open charter schools
pubschls$open_chart <- ifelse(pubschls$Charter=="Y" & is.na(pubschls$ClosedDate)==TRUE, 1, 0)
# open public schools
pubschls$open_pub <- ifelse(pubschls$Charter=="N" & is.na(pubschls$ClosedDate)==TRUE, 1, 0)
# closed charters
pubschls$closed_chart <- ifelse(pubschls$Charter=="Y" & is.na(pubschls$ClosedDate)==FALSE, 1, 0)
# closed public schools
pubschls$closed_pub <- ifelse(pubschls$Charter=="N" & is.na(pubschls$ClosedDate)==FALSE, 1, 0)
lausd <- filter(pubschls, NCESDist=="0622710")
# count number open during each year
Then I subtract the columns from each other to get totals.
la_schools_count <- aggregate(lausd[c('open_chart','closed_chart','open_pub','closed_pub')],
by=list(year(lausd$OpenDate)), sum)
# find net charters by subtracting closed from open
la_schools_count$net_chart <- la_schools_count$open_chart - la_schools_count$closed_chart
# find net public schools by subtracting closed from open
la_schools_count$net_pub <- la_schools_count$open_pub - la_schools_count$closed_pub
# add running totals
la_schools_count$cum_chart <- cumsum(la_schools_count$net_chart)
la_schools_count$cum_pub <- cumsum(la_schools_count$net_pub)
# total totals
la_schools_count$total <- la_schools_count$cum_chart + la_schools_count$cum_pub
My output looks like this:
la_schools_count <- select(la_schools_count, "year", "cum_chart", "cum_pub", "pen_rate", "total")
year cum_chart cum_pub pen_rate total
1 1952 1 0 100.00000 1
2 1956 1 1 50.00000 2
3 1969 1 2 33.33333 3
4 1980 55 469 10.49618 524
5 1989 55 470 10.47619 525
6 1990 55 470 10.47619 525
7 1991 55 473 10.41667 528
8 1992 55 476 10.35782 531
9 1993 55 477 10.33835 532
10 1994 56 478 10.48689 534
11 1995 57 478 10.65421 535
12 1996 57 479 10.63433 536
13 1997 58 481 10.76067 539
14 1998 59 480 10.94620 539
15 1999 61 480 11.27542 541
16 2000 61 481 11.25461 542
17 2001 62 482 11.39706 544
18 2002 64 484 11.67883 548
19 2003 73 485 13.08244 558
20 2004 83 496 14.33506 579
21 2005 90 524 14.65798 614
22 2006 96 532 15.28662 628
23 2007 90 534 14.42308 624
24 2008 97 539 15.25157 636
25 2009 108 546 16.51376 654
26 2010 124 566 17.97101 690
27 2011 140 580 19.44444 720
28 2012 144 605 19.22563 749
29 2013 162 609 21.01167 771
30 2014 179 611 22.65823 790
31 2015 195 611 24.19355 806
32 2016 203 614 24.84700 817
33 2017 211 619 25.42169 830
I'm just wondering if this could be done in a better way. Like an apply statement to all rows based on the conditions?
dput:
structure(list(CDSCode = c("19647330100289", "19647330100297",
"19647330100669", "19647330100677", "19647330100743", "19647330100750"
), OpenDate = structure(c(12324, 12297, 12240, 12299, 12634,
12310), class = "Date"), ClosedDate = structure(c(NA, 15176,
NA, NA, NA, NA), class = "Date"), Charter = c("Y", "Y", "Y",
"Y", "Y", "Y")), .Names = c("CDSCode", "OpenDate", "ClosedDate",
"Charter"), row.names = c(NA, -6L), class = c("tbl_df", "tbl",
"data.frame"))
I followed your code and learned what you were doing except pen_rate. It seems that pen_rate is calculated dividing cum_chart by total. I download the original data set and did the following. I called the data set foo. Whenclosed_pub), I combined Charter and ClosedDate. I checked if ClosedDate is NA or not, and converted the logical output to numbers (1 = open, 0 = closed). This is how I created the four groups (i.e., open_chart, closed_chart, open_pub, and closed_pub). I guess this would ask you to do less typing. Since the dates are in character, I extracted year using substr(). If you have a date object, you need to do something else. Once you have year, you group the data with it and calculate how many schools exist for each type of school using count(). This part is the equivalent of your aggregate() code. Then, Convert the output to a wide-format data with spread() and did the rest of the calculation as you demonstrated in your codes. The final output seems different from what you have in your question, but my outcome was identical to one that I obtained by running your codes. I hope this will help you.
library(dplyr)
library(tidyr)
library(readxl)
# Get the necessary data
foo <- read_xls("pubschls.xls") %>%
select(NCESDist, CDSCode, OpenDate, ClosedDate, Charter) %>%
filter(NCESDist == "0622710" & (!Charter %in% NA))
mutate(foo, group = paste(Charter, as.numeric(is.na(ClosedDate)), sep = "_"),
year = substr(OpenDate, star = nchar(OpenDate) - 3, stop = nchar(OpenDate))) %>%
count(year, group) %>%
spread(key = group, value = n, fill = 0) %>%
mutate(net_chart = Y_1 - Y_0,
net_pub = N_1 - N_0,
cum_chart = cumsum(net_chart),
cum_pub = cumsum(net_pub),
total = cum_chart + cum_pub,
pen_rate = cum_chart / total)
# A part of the outcome
# year N_0 N_1 Y_0 Y_1 net_chart net_pub cum_chart cum_pub total pen_rate
#1 1866 0 1 0 0 0 1 0 1 1 0.00000000
#2 1873 0 1 0 0 0 1 0 2 2 0.00000000
#3 1878 0 1 0 0 0 1 0 3 3 0.00000000
#4 1881 0 1 0 0 0 1 0 4 4 0.00000000
#5 1882 0 2 0 0 0 2 0 6 6 0.00000000
#110 2007 0 2 15 9 -6 2 87 393 480 0.18125000
#111 2008 2 8 9 15 6 6 93 399 492 0.18902439
#112 2009 1 9 4 15 11 8 104 407 511 0.20352250
#113 2010 5 26 5 21 16 21 120 428 548 0.21897810
#114 2011 2 16 2 18 16 14 136 442 578 0.23529412
#115 2012 2 27 3 7 4 25 140 467 607 0.23064250
#116 2013 1 5 1 19 18 4 158 471 629 0.25119237
#117 2014 1 3 1 18 17 2 175 473 648 0.27006173
#118 2015 0 0 2 18 16 0 191 473 664 0.28765060
#119 2016 0 3 0 8 8 3 199 476 675 0.29481481
#120 2017 0 5 0 9 9 5 208 481 689 0.30188679
I have a data frame below and I want to find the average row value for all columns with header *R and all columns with *G.
The output should then be four columns: Rfam, Classes, avg.rowR, avg.rowG
I was playing around with the rowMeans() function, but I am not sure how to specify the columns.
Rfam Classes 26G 26R 35G 35R 46G 46R 48G 48R 55G 55R
5_8S_rRNA rRNA 63 39 8 27 26 17 28 43 41 17
5S_rRNA rRNA 171 149 119 109 681 47 95 161 417 153
7SK 7SK 53 282 748 371 248 42 425 384 316 198
ACA64 Other 7 8 19 2 10 1 36 10 10 4
let-7 miRNA 121825 73207 25259 75080 54301 63510 30444 53800 78961 47533
lin-4 miRNA 10149 16263 5629 19680 11297 37866 3816 9677 11713 10068
Metazoa_SRP SRP 317 1629 1008 418 1205 407 1116 1225 1413 1075
mir-1 miRNA 3 4 1 2 0 26 1 1 0 4
mir-10 miRNA 912163 1411287 523793 1487160 517017 1466085 107597 551381 727720 788201
mir-101 miRNA 461 320 199 553 174 460 278 297 256 254
mir-103 miRNA 937 419 202 497 318 217 328 343 891 439
mir-1180 miRNA 110 32 4 17 53 47 6 29 35 22
mir-1226 miRNA 11 3 0 3 6 0 1 2 5 4
mir-1237 miRNA 3 2 1 1 0 1 0 2 1 1
mir-1249 miRNA 5 14 2 9 4 5 9 5 7 7
newcols <- sapply(c("R$", "G$"), function(x) rowMeans(df[grep(x, names(df))]))
setNames(cbind(df[1:2], newcols), c(names(df)[1:2], "avg.rowR", "avg.rowG"))
# Rfam Classes avg.rowR avg.rowG
# 1 5_8S_rRNA rRNA 28.6 33.2
# 2 5S_rRNA rRNA 123.8 296.6
# 3 7SK 7SK 255.4 358.0
# 4 ACA64 Other 5.0 16.4
# 5 let-7 miRNA 62626.0 62158.0
# 6 lin-4 miRNA 18710.8 8520.8
# 7 Metazoa_SRP SRP 950.8 1011.8
# 8 mir-1 miRNA 7.4 1.0
# 9 mir-10 miRNA 1140822.8 557658.0
# 10 mir-101 miRNA 376.8 273.6
# 11 mir-103 miRNA 383.0 535.2
# 12 mir-1180 miRNA 29.4 41.6
# 13 mir-1226 miRNA 2.4 4.6
# 14 mir-1237 miRNA 1.4 1.0
# 15 mir-1249 miRNA 8.0 5.4
One way to look for patterns in column names is to use the grep family of functions. The function call grep("R$", names(df)) will return the index of all column names that end with R. When we use it with sapply we can search for the R and G columns in one expression.
The core of the second line is cbind(df[1:2], newcols). That is the binding of the first two columns of df and the two new columns of mean values. Wrapping it with setNames(.., c(names(df)f[1:2]....)) formats the column names to match your desired output.