Last time, we covered much of the basics of slicing matrices but there are still some topics and some helper functions that will be useful to know when trying to accomplish certain tasks.
Not only can you extract a slice of a matrix to analyze or plot but you can also assign values to that slice. First, create a matrix of all zeros to manipulate:
allz = matrix(0, nrow = 6, ncol = 6)
allz [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 0 0 0 0 0 0
[2,] 0 0 0 0 0 0
[3,] 0 0 0 0 0 0
[4,] 0 0 0 0 0 0
[5,] 0 0 0 0 0 0
[6,] 0 0 0 0 0 0As before, you slice the first row.
allz[1,][1] 0 0 0 0 0 0However, you can also assign values to it.
allz[1,] = 1:6
allz [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 2 3 4 5 6
[2,] 0 0 0 0 0 0
[3,] 0 0 0 0 0 0
[4,] 0 0 0 0 0 0
[5,] 0 0 0 0 0 0
[6,] 0 0 0 0 0 0Note that when assigning to a slice, the right-hand side must be of the same dimensionality as the left-hand side. For example, the following will not work:
allz[1,] = 1:4The one exception to this rule is when the number of items on the right hand side is a multiple of the number of elements in the slice. The simplest example is
allz[1,] = 1
allz [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 1 1 1 1 1
[2,] 0 0 0 0 0 0
[3,] 0 0 0 0 0 0
[4,] 0 0 0 0 0 0
[5,] 0 0 0 0 0 0
[6,] 0 0 0 0 0 0but you can also do
allz[1,] = 1:3
allz [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 2 3 1 2 3
[2,] 0 0 0 0 0 0
[3,] 0 0 0 0 0 0
[4,] 0 0 0 0 0 0
[5,] 0 0 0 0 0 0
[6,] 0 0 0 0 0 0where the right hand side is use as many times as necessary to fill the slice.
Sorting numeric and character values is an important task that comes up in many applications. The sort function has reasonable defaults where it produces increasing numeric values
set.seed(100)
rvec = sample(1:100, 20, replace = TRUE)
rvec [1] 74 89 78 23 86 70 4 55 70 98 7 7 55 43 82 61 12 99 51 72sort(rvec) [1] 4 7 7 12 23 43 51 55 55 61 70 70 72 74 78 82 86 89 98 99or character values
svec = c("hello", "world", "goodbye", "grand", "planet")
sort(svec, decreasing=TRUE)[1] "world" "planet" "hello" "grand" "goodbye"You can reverse the sort order by setting the argument decreasing = TRUE.
Often, you will want to sort not only a vector by the rows of a data matrix based on some column of the matrix. Thus, you need the list of positions each row will go to (e.g., row 1 to row 10 because its 10th in the sorted order, etc). To obtain this, you can use the order function
svec[1] "hello" "world" "goodbye" "grand" "planet" order(svec)[1] 3 4 1 5 2which output precisely that list of indices. If you stick these indices back into the vector, you will obtain the original sort operation
svec[ order(svec) ][1] "goodbye" "grand" "hello" "planet" "world" sort(svec)[1] "goodbye" "grand" "hello" "planet" "world" You can also use the “sort order” of one column to order the rows of a whole matrix or data table. For example, using a matrix of random values,
set.seed(42)
rmatx = matrix(sample(1:20, 36, replace = TRUE), nrow = 6, ncol = 6)
rmatx [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 17 17 20 5 3 4
[2,] 5 15 18 13 1 4
[3,] 1 7 15 5 10 18
[4,] 10 4 3 20 11 13
[5,] 4 5 9 2 15 5
[6,] 18 14 4 8 8 4you could then sort the rows based on elements in the first column by first obtaining the indices used to sort that column
order(rmatx[,1])[1] 3 5 2 4 1 6and using the indices to order the rows
rmatx [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 17 17 20 5 3 4
[2,] 5 15 18 13 1 4
[3,] 1 7 15 5 10 18
[4,] 10 4 3 20 11 13
[5,] 4 5 9 2 15 5
[6,] 18 14 4 8 8 4rmatx[ order(rmatx[,1]), ] [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 7 15 5 10 18
[2,] 4 5 9 2 15 5
[3,] 5 15 18 13 1 4
[4,] 10 4 3 20 11 13
[5,] 17 17 20 5 3 4
[6,] 18 14 4 8 8 4Recall that you can slice by creating a logical condition (generating TRUE and FALSE values) and use that in the index of a matrix. Sometimes, you want the actual indices of the elements of that matrix that are sliced; i.e., you want the indices of the elements where the conditions is TRUE. To get these indices, you use the which function. For example, the logical vector and slice are
rmatx[,1] > 10[1] TRUE FALSE FALSE FALSE FALSE TRUErmatx[ rmatx[,1] > 10, ] [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 17 17 20 5 3 4
[2,] 18 14 4 8 8 4You can slice the same way with which:
which( rmatx[,1] > 10 )[1] 1 6rmatx[ which( rmatx[,1] > 10 ), ] [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 17 17 20 5 3 4
[2,] 18 14 4 8 8 4Finally, there some special versions of the which function that give you the first index of the max or min element of a vector, which.max and which.min.
We’ve talked about attributes and names before but there are some helpful functions for getting and setting the names associated with arrays and lists. You have already seen with lists how each element can be given a name.
l = list(a = 1, b = "one hundred")
named_svec = c(s1 = "hello", s2 = "world", s3 = "goodbye", s4 = "grand", s5 = "planet")
named_svec s1 s2 s3 s4 s5
"hello" "world" "goodbye" "grand" "planet" str(named_svec) Named chr [1:5] "hello" "world" "goodbye" "grand" "planet"
- attr(*, "names")= chr [1:5] "s1" "s2" "s3" "s4" ...You can recover those names with the names function:
names(named_svec)[1] "s1" "s2" "s3" "s4" "s5"You can also set the names afterwards by assigning to names:
svec[1] "hello" "world" "goodbye" "grand" "planet" names(svec) = c("s1", "s2", "s3", "s4", "s5")
svec s1 s2 s3 s4 s5
"hello" "world" "goodbye" "grand" "planet" Finally, you can return a version of the vector with the names stripped using the function uname
unnamed_svec = unname(named_svec)
unnamed_svec[1] "hello" "world" "goodbye" "grand" "planet" though note that this hasn’t changed the original vector:
named_svec s1 s2 s3 s4 s5
"hello" "world" "goodbye" "grand" "planet" Finally, you can get rid of the names entirely by assigning names to NULL
names(named_svec) = NULL
named_svec[1] "hello" "world" "goodbye" "grand" "planet" Just as reminder, while we can name elements of vectors, they still have to hold the same data type, unlike lists that can hold anything.
str(list(a=1, b="two"))List of 2
$ a: num 1
$ b: chr "two"str(c(a=1, b="two")) Named chr [1:2] "1" "two"
- attr(*, "names")= chr [1:2] "a" "b"A special object that you will see when dealing with data frames is called a “factor”. A factor is a vector that can contain only predefined values and essentially stores categorical data (e.g., “tall”, “medium”, and “short” for plant height). Factors have a “levels” attribute that lists the allowable values. For example
fac_factor = factor(c("Famulski", "Burger", "Seifert", "Santollo", "Duncan", "Singh"))
fac_factor[1] Famulski Burger Seifert Santollo Duncan Singh
Levels: Burger Duncan Famulski Santollo Seifert SinghYou can get the levels of a factor with
levels(fac_factor)[1] "Burger" "Duncan" "Famulski" "Santollo" "Seifert" "Singh" If you try to set an element of the factor object to a value outside of levels, you will receive a warning
fac_factor[1] = "Van Cleve"Warning in `[<-.factor`(`*tmp*`, 1, value = "Van Cleve"): invalid factor level,
NA generatedfac_factor[1] <NA> Burger Seifert Santollo Duncan Singh
Levels: Burger Duncan Famulski Santollo Seifert Singhand the element will be converted to the NA value, which is used for missing data.
Many R functions that read data tables take advantage of this behavior of factors so that columns may only contain certain values and the other values are missing data. This occurs when the function runs into a column with string data and the R function will often convert that column to a factor. Some of the functions that read data tables have nice arguments that let you tell them that specific strings, say “-”, represent missing data and should be be converted to NA.
While useful, factors are extremely annoying when your data are converted to them when you don’t expect it as further changes to the data table may result in NA values when you really wanted to add a new string value. This paper gives a good history of why factors are useful in R. It mostly comes down to factors being useful for categorical variables in regression models.
The main place factors are used that we’ll encounter in this course is when plotting categorical variables. In those cases, the order the variables are plotted in will be determined the order of the levels in levels. In those cases, you may want to reorder the factors so that the variables are plotted in a specific order (say in descending order of frequency in the data). For this, there is a nice package called forcats that is included in the tidyverse that has the function fct_reorder that can help. Another thing we’ll run into is changing factor levels so that they have more descriptive labels. For this forcats has fct_recode. We’ll see examples of these kinds of scenarios later on when we’re plotting using ggplot2
Finally we have reached data frames. Data frames are the most common way of storing data in R. Essentially, a data frame is a list object containing vectors of equal length (i.e., the number of rows of the table). Put another way, a data frame is a list version of a matrix. Thus, data frames have properties such as length(), rnow(), ncol() colnames(), and rownames().
Creating a data frame is like creating a list where you name your elements, which here are columns (data not guaranteed to be accurate…):
dframe = data.frame(height_rank = 1:4, last_name = c("Van Cleve", "Linnen", "Seifert", "Pendergast"), first_name = c("Jeremy", "Catherine", "Ashley", "Julie"))
dframe height_rank last_name first_name
1 1 Van Cleve Jeremy
2 2 Linnen Catherine
3 3 Seifert Ashley
4 4 Pendergast JulieSlicing a data frame works like slicing a matrix or a list. Often, we will use the list convention where columns can be obtained with $. For example,
dframe$first_name[1] "Jeremy" "Catherine" "Ashley" "Julie" dframe$last_name[1] "Van Cleve" "Linnen" "Seifert" "Pendergast"Adding columns to a data frame is done with cbind (“column bind”), which glues together columns,
cbind(dframe, building = c("THM", "THM", "THM", "THM"), floor = c(2,2,2,3)) height_rank last_name first_name building floor
1 1 Van Cleve Jeremy THM 2
2 2 Linnen Catherine THM 2
3 3 Seifert Ashley THM 2
4 4 Pendergast Julie THM 3and adding rows with rbind (“row bind”), which glues together rows,
rbind(dframe, data.frame(height_rank = 0, last_name = "Smith", first_name = "Jeramiah")) height_rank last_name first_name
1 1 Van Cleve Jeremy
2 2 Linnen Catherine
3 3 Seifert Ashley
4 4 Pendergast Julie
5 0 Smith JeramiahAgain, note that each of these commands returned a new data.frame and the original is unchanged until we explicitly save back to that variable name:
dframe height_rank last_name first_name
1 1 Van Cleve Jeremy
2 2 Linnen Catherine
3 3 Seifert Ashley
4 4 Pendergast JulieFunctions like cbind, rbind, and others that do operations on arrays and data frames usually create a copy of the data and return the modified copy. This is usually what you want since you’re not modifying your original variable/data until you explicitly assign the old variable to the new data. One case where you might not want to do this is when your data are so big (e.g., whole genomes, billions of tweets) that they take up a large fraction of the computer’s RAM, in which case you have to be very careful about creating copies of your data.
Now that you know about data frames, you can start using some nice R functions to read in data. We have already seen this when loading data for the homeworks. As in those examples, we load the a few packages before loading the data since they are nice for reading csv and excel files. For reading excel files, you’ll need to install the readxl package if you don’t have it, which you can do with:
install.packages("readxl")Then load:
library(tidyverse) # loads the `readr` package that loads things like csv files
library(readxl) # package for reading Excel filesNow, you can use the read_csv function to load csv or “comma separated value” files. For example to load COVID-19 and respiratory virus data from the CDC that was saved as a csv file, we load us_hosps_deaths_cdc_2020-01_2024-09.csv, which is in the project folder and course GitHub repo.
us_deaths = read_csv("us_hosps_deaths_cdc_2020-01_2024-09.csv")Rows: 11128 Columns: 72
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): state, state_abbrv
dbl (69): covid_19_deaths, total_deaths, percent_of_expected_deaths, pneumo...
dttm (1): week_end_date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Notice that read_csv gives you some nice output telling us about the table you just read. This function and others like it (i.e., from the readr and readxl packages) do a lot for you automatically and have many nice features. For example, read_csv has the argument col_names = TRUE by default, which means it uses the first row of the table as the column names. Some tables may simple just straight into data without column names in which case you can set col_names = FALSE and it will give automatic names or give col_names a vector of column names manually. Sometimes data tables will have the first few lines with text describing the data and you can skip them by giving the argument skip the number of lines to skip. There are many other options so looking at the help with ?read_csv is recommended when you’re having trouble getting the data loaded correctly.
Loading excel files in no harder. We’ll load some data from a RNA-seq paper on genomic imprinting (Babak et al. 2015. Nat Gen, http://dx.doi.org/10.1038/ng.3274), babak-etal-2015_imprinted-mouse.xlsx (located in project folder and course GitHub repo), with read_excel
imprint = read_excel("babak-etal-2015_imprinted-mouse.xlsx", na = "NaN")Note that you have to tell the function what strings in the Excel spreadsheet correspond to NA or missing data (“NaN” in this case). The first column are the gene names for each row
imprint$Genes [1] "PEC3" "UBE2NL" "TRAPPC9"
[4] "EIF2C2" "MIR344D-3" "MIR344G"
[7] "A930009L07RIK" "KCNK9" "RASGRF1"
[10] "INPP5F" "NAP1L5" "USP29"
[13] "HERC3-new" "GM9801-new" "BEGAIN"
[16] "PX00010K13" "PEG13" "1110006E14RIK"
[19] "MBII-343" "MIRG" "ZDBF2"
[22] "IMPACT" "NDN" "NIBP"
[25] "AK139287" "CCDC40-AS" "PX00113D24"
[28] "PEC2" "ZIM3" "AK142849"
[31] "MKRN3" "IPW" "B830012L14RIK"
[34] "ADAM23" "COMMD1" "UBE3A"
[37] "WARS" "BCL2L1" "BLCAP"
[40] "CALCR" "MAGEL2" "CDH15"
[43] "COPG2" "U80893" "NR_015479-new"
[46] "GRB10" "PEG10" "SGCE"
[49] "PEG1" "MCTS2" "H13"
[52] "SNRPN" "ZRSR1" "MEG3"
[55] "RIAN" "PEG3" "PLAGL1"
[58] "1110014L15RIK-new" "ASB4" "ZIM1"
[61] "KLHDC10" "DLK1" "GNAS"
[64] "PPP1R9A" "PDE4D" "A19"
[67] "ZFP264" "KCNQ1OT1" "AK050713"
[70] "RTL1" "CDKN1C" "PEG12"
[73] "KCNQ1" "NESPAS" "SLC38A4"
[76] "H19" "IGF2" "INS1"
[79] "TFPI2" "AK043599" "EDN3-new"
[82] "TMEM106A-new" "DDC" "RDM1"
[85] "TNK1-new" "TREM1-new" "2400006E01RIK-new"
[88] "IGF2AS" "UC008IHS.1-new" "5133400J02RIK-new"
[91] "PHLDA2" "AF357359" "GAB1"
[94] "SFMBT2" "CD81" "PHF17"
[97] "TSSC4" "DACT2-new" "SLC22A2"
[100] "TSSC5" "SLC22A3" "KLF14"
[103] "ATP10A" "DCN" "PON2"
[106] "PON3" "AMPD3" "GABRB3"
[109] "GATM" "TBC1D12" "DIO3"
[112] "NAP1L4" "MSUIT" "ASCL2"
[115] "OSBPL5" "TNFRSF23" "AIRN"
[118] "HTR2A" "MAPT" "MKRN1-PS1"
[121] "DLX5" "IGF2R" "TRY4-new"
[124] "TSPAN32" "ZIM2" and the column names are the tissue type that RNA expression was measured in
colnames(imprint) [1] "Genes" "Preoptic Area (ref)"
[3] "e17.5 Brain" "Hypothalamus"
[5] "e15 Brain (ref)" "e9.5 Yolk Sac"
[7] "Prefrontal Cortex (ref)" "e9.5 Placenta"
[9] "Whole Brain" "Adrenal Gland"
[11] "Olfactory Bulb" "Cortex"
[13] "e9.5 Embryo (ref)" "Hippocampus"
[15] "TSCs (ref)" "Cerebellum"
[17] "Striatum" "e17.5 Placenta (ref)"
[19] "Pancreatic Islets" "MEFs (ref)"
[21] "Bladder" "Lung"
[23] "Duodenum" "White Adipose"
[25] "Skeletal Muscle" "Skin"
[27] "Heart" "Stomach"
[29] "Thymus" "Kidney"
[31] "Liver" "Whole Bone Marrow"
[33] "Spleen" "Testes" where the first element is the column name of the “Genes” column. You will manipulate these data later when we talk about tidy data and dplyr.
Finally, if you look at both the COVID-19 data and the imprinting data
us_deaths# A tibble: 11,128 × 72
week_end_date state covid_19_deaths total_deaths percent_of_expected_…¹
<dttm> <chr> <dbl> <dbl> <dbl>
1 2020-08-08 04:00:00 Alab… 264 1379 143
2 2020-08-15 04:00:00 Alab… 230 1305 137
3 2020-08-22 04:00:00 Alab… 209 1303 140
4 2020-08-29 04:00:00 Alab… 185 1216 127
5 2020-09-05 04:00:00 Alab… 156 1216 125
6 2020-09-12 04:00:00 Alab… 138 1232 128
7 2020-09-19 04:00:00 Alab… 139 1200 125
8 2020-09-26 04:00:00 Alab… 105 1183 119
9 2020-10-03 04:00:00 Alab… 111 1103 116
10 2020-10-10 04:00:00 Alab… 137 1204 127
# ℹ 11,118 more rows
# ℹ abbreviated name: ¹percent_of_expected_deaths
# ℹ 67 more variables: pneumonia_deaths <dbl>,
# pneumonia_and_covid_19_deaths <dbl>, influenza_deaths <dbl>,
# pneumonia_influenza_or_covid_19_deaths <dbl>, state_abbrv <chr>,
# weekly_actual_days_reporting_any_data <dbl>,
# weekly_percent_days_reporting_any_data <dbl>, …imprint# A tibble: 125 × 34
Genes `Preoptic Area (ref)` `e17.5 Brain` Hypothalamus `e15 Brain (ref)`
<chr> <dbl> <dbl> <dbl> <dbl>
1 PEC3 -10 -10 -10 -10
2 UBE2NL -10 -10 -10 -10
3 TRAPPC9 10 10 9.37 10
4 EIF2C2 10 10 10 10
5 MIR344D-3 -10 -10 -10 -10
6 MIR344G -10 -10 -10 -10
7 A930009L0… 10 10 10 10
8 KCNK9 10 10 7.53 6.02
9 RASGRF1 -10 -10 -10 -10
10 INPP5F -10 -10 -10 -10
# ℹ 115 more rows
# ℹ 29 more variables: `e9.5 Yolk Sac` <dbl>, `Prefrontal Cortex (ref)` <dbl>,
# `e9.5 Placenta` <dbl>, `Whole Brain` <dbl>, `Adrenal Gland` <dbl>,
# `Olfactory Bulb` <dbl>, Cortex <dbl>, `e9.5 Embryo (ref)` <dbl>,
# Hippocampus <dbl>, `TSCs (ref)` <dbl>, Cerebellum <dbl>, Striatum <dbl>,
# `e17.5 Placenta (ref)` <dbl>, `Pancreatic Islets` <dbl>,
# `MEFs (ref)` <dbl>, Bladder <dbl>, Lung <dbl>, Duodenum <dbl>, …you should notice that both are of the tibble type. A tibble is a data.frame but with enhancements. First and maybe most importantly, it prints nicely when you evaluate it at the command line and in Quarto notebooks. Second, it leaves the column names alone on conversion to a data frame. Thus, we get columns like Preoptic Area (ref) in the imprinting data instead of
make.names("Preoptic Area (ref)")[1] "Preoptic.Area..ref."So a “normal” data.frame would do this to the data:
data.frame(imprint) Genes Preoptic.Area..ref. e17.5.Brain Hypothalamus
1 PEC3 -10.0000 -10.00000 -10.00000
2 UBE2NL -10.0000 -10.00000 -10.00000
3 TRAPPC9 10.0000 10.00000 9.36860
4 EIF2C2 10.0000 10.00000 10.00000
5 MIR344D-3 -10.0000 -10.00000 -10.00000
6 MIR344G -10.0000 -10.00000 -10.00000
7 A930009L07RIK 10.0000 10.00000 10.00000
8 KCNK9 10.0000 10.00000 7.52570
9 RASGRF1 -10.0000 -10.00000 -10.00000
10 INPP5F -10.0000 -10.00000 -10.00000
11 NAP1L5 -12.0000 -10.00000 -10.00000
12 USP29 -10.0000 -10.00000 -10.00000
13 HERC3-new 10.0000 4.35630 10.00000
14 GM9801-new -3.5000 -10.00000 -10.00000
15 BEGAIN -10.0000 -3.97640 -10.00000
16 PX00010K13 -10.0000 -10.00000 -10.00000
17 PEG13 -10.0000 -10.00000 -10.00000
18 1110006E14RIK 10.0000 10.00000 9.63620
19 MBII-343 10.0000 10.00000 10.00000
20 MIRG 10.0000 10.00000 10.00000
21 ZDBF2 -10.0000 -10.00000 -10.00000
22 IMPACT -10.0000 -10.00000 -10.00000
23 NDN -10.0000 -10.00000 -10.00000
24 NIBP -10.0000 -10.00000 -10.00000
25 AK139287 11.2413 -9.03090 -12.00000
26 CCDC40-AS -0.4000 -10.00000 -1.80000
27 PX00113D24 -4.8000 -10.00000 -10.00000
28 PEC2 -12.0000 -10.00000 -3.86220
29 ZIM3 -12.0000 -10.00000 -10.00000
30 AK142849 1.2600 10.00000 0.30103
31 MKRN3 -2.4000 -10.00000 -0.60206
32 IPW -1.7000 -10.00000 -1.02400
33 B830012L14RIK 2.7000 10.00000 1.20410
34 ADAM23 -5.6000 -2.36250 -3.34180
35 COMMD1 -10.0000 0.59229 13.41050
36 UBE3A -9.8000 -0.85767 13.49680
37 WARS -1.1000 13.76700 13.94380
38 BCL2L1 -2.2000 -0.35194 -4.03450
39 BLCAP 8.2900 10.00000 1.60100
40 CALCR 10.0000 10.00000 9.63300
41 MAGEL2 -10.0000 -10.00000 -10.00000
42 CDH15 -10.0000 -10.00000 -0.67372
43 COPG2 -10.0000 10.00000 10.00000
44 U80893 -10.0000 -3.61240 -6.68070
45 NR_015479-new 5.1100 10.00000 1.90750
46 GRB10 -10.0000 10.00000 -10.00000
47 PEG10 -10.0000 -10.00000 -10.00000
48 SGCE -10.0000 -10.00000 -10.00000
49 PEG1 13.3883 -10.00000 -1.45130
50 MCTS2 -6.3000 -2.80790 -0.87477
51 H13 10.0000 10.00000 10.00000
52 SNRPN -10.0000 -10.00000 -10.00000
53 ZRSR1 -10.0000 -10.00000 -10.00000
54 MEG3 10.0000 10.00000 10.00000
55 RIAN 10.0000 10.00000 10.00000
56 PEG3 -10.0000 -10.00000 -12.00000
57 PLAGL1 -10.0000 -10.00000 -12.00000
58 1110014L15RIK-new 10.0000 11.86260 10.00000
59 ASB4 10.0000 10.00000 10.00000
60 ZIM1 10.0000 10.00000 10.00000
61 KLHDC10 1.2300 4.72600 3.49160
62 DLK1 -10.0000 -10.00000 -10.00000
63 GNAS 10.0000 10.00000 10.00000
64 PPP1R9A 10.0000 1.98370 10.00000
65 PDE4D 0.8700 -2.20540 -0.64566
66 A19 -12.0000 -12.00000 -12.00000
67 ZFP264 -12.0000 -12.00000 -12.00000
68 KCNQ1OT1 -4.9000 -10.00000 -10.00000
69 AK050713 -12.0000 7.22470 -12.00000
70 RTL1 -8.6000 9.79750 -1.07810
71 CDKN1C 0.4000 10.00000 2.70930
72 PEG12 11.0000 -10.00000 -11.00000
73 KCNQ1 -9.2000 0.81325 -11.00000
74 NESPAS -1.8000 -12.00000 -12.00000
75 SLC38A4 11.0000 -10.00000 -11.00000
76 H19 -12.0000 10.00000 0.72700
77 IGF2 5.2100 -10.00000 2.13570
78 INS1 -12.0000 -12.00000 -12.00000
79 TFPI2 -12.0000 0.46376 -12.00000
80 AK043599 0.9600 1.81130 3.91340
81 EDN3-new 12.0112 11.12800 -11.00000
82 TMEM106A-new 0.3500 -11.00000 -12.00000
83 DDC 0.7200 12.20970 3.48480
84 RDM1 12.6094 -0.37552 -12.00000
85 TNK1-new 11.6994 -0.19605 -12.00000
86 TREM1-new -12.0000 -12.00000 -12.00000
87 2400006E01RIK-new -12.0000 -12.00000 -12.00000
88 IGF2AS -12.0000 -2.84050 -12.00000
89 UC008IHS.1-new -12.0000 -12.00000 -12.00000
90 5133400J02RIK-new -12.0000 -12.00000 -12.00000
91 PHLDA2 -12.0000 -11.00000 -12.00000
92 AF357359 1.5000 6.02060 4.21440
93 GAB1 13.1799 12.49410 1.00960
94 SFMBT2 -0.5000 -0.37051 -0.33822
95 CD81 16.3169 14.94790 0.35510
96 PHF17 12.7728 11.64660 12.79730
97 TSSC4 13.6834 0.80918 13.55120
98 DACT2-new 12.2244 0.63047 12.32020
99 SLC22A2 -12.0000 0.67372 -12.00000
100 TSSC5 -12.0000 0.60206 -11.00000
101 SLC22A3 11.0000 -11.00000 -11.00000
102 KLF14 NA 0.30103 -12.00000
103 ATP10A 12.1562 -1.88490 -0.42798
104 DCN 13.2672 12.38120 0.38981
105 PON2 13.3491 11.84640 12.88390
106 PON3 11.3954 0.84004 0.30103
107 AMPD3 -0.3000 12.17010 12.99300
108 GABRB3 -1.8000 14.06110 14.29740
109 GATM 14.0721 12.80860 13.73260
110 TBC1D12 0.4700 11.69720 11.99160
111 DIO3 -3.0000 -1.81130 -11.00000
112 NAP1L4 1.0600 0.33750 14.65260
113 MSUIT -12.0000 0.30103 -12.00000
114 ASCL2 -12.0000 -12.00000 -12.00000
115 OSBPL5 12.4792 1.29110 12.84880
116 TNFRSF23 11.0000 1.57230 -11.00000
117 AIRN 12.6812 -3.58600 -0.90309
118 HTR2A 11.1261 -11.00000 11.56130
119 MAPT 15.9355 16.00000 15.32530
120 MKRN1-PS1 11.8964 -11.00000 -11.00000
121 DLX5 11.0000 13.25390 -0.12494
122 IGF2R 1.4000 3.89980 1.00580
123 TRY4-new -12.0000 -12.00000 -12.00000
124 TSPAN32 11.0000 -0.19605 -11.00000
125 ZIM2 -12.0000 -12.00000 -12.00000
e15.Brain..ref. e9.5.Yolk.Sac Prefrontal.Cortex..ref. e9.5.Placenta
1 -10.0000 -12.00000 -10.0000 0.00000
2 -10.0000 -12.00000 -10.0000 -12.00000
3 10.0000 -11.00000 10.0000 -11.00000
4 10.0000 13.04470 5.8600 -11.00000
5 -10.0000 -12.00000 -10.0000 -12.00000
6 -10.0000 -12.00000 -10.0000 -12.00000
7 10.0000 -12.00000 10.0000 -12.00000
8 6.0200 -12.00000 7.5400 -12.00000
9 -10.0000 -12.00000 -10.0000 -12.00000
10 -10.0000 13.20060 -10.0000 -11.00000
11 -10.0000 0.30103 -10.0000 -11.00000
12 -10.0000 -4.21440 -10.0000 -4.51540
13 -3.4000 12.08290 -0.7000 -11.00000
14 -10.0000 -12.00000 -2.2000 -12.00000
15 -6.4000 -1.58100 -2.7000 -11.00000
16 -10.0000 -7.03220 -10.0000 -0.90695
17 -10.0000 -2.67980 -10.0000 -11.00000
18 10.0000 10.00000 10.0000 -11.00000
19 10.0000 10.00000 10.0000 -11.00000
20 10.0000 10.00000 10.0000 -11.00000
21 -10.0000 -9.63620 -10.0000 -1.20410
22 -10.0000 -10.00000 -10.0000 -11.00000
23 -10.0000 -7.03220 -10.0000 -0.90695
24 -10.0000 -1.09290 -10.0000 -11.00000
25 -1.8000 -12.00000 11.0000 -12.00000
26 11.2521 -11.00000 11.0000 -11.00000
27 -8.1000 -1.50510 -3.0000 -2.70930
28 NA -12.00000 12.9526 -12.00000
29 -12.0000 -12.00000 -12.0000 -0.30103
30 9.3300 2.18910 3.9100 -11.00000
31 -10.0000 -12.00000 11.1358 -11.00000
32 -10.0000 -12.00000 -6.1000 -12.00000
33 6.0900 7.82680 3.0100 -11.00000
34 -10.0000 12.03490 -4.0000 -0.16273
35 -10.0000 15.04870 -10.0000 -11.00000
36 -10.0000 12.55580 -10.0000 -11.00000
37 -10.0000 13.77850 -5.3000 -11.00000
38 -3.3000 14.10200 -2.3000 -11.00000
39 10.0000 13.24600 0.9300 -11.00000
40 9.6300 0.30103 -12.0000 -12.00000
41 -10.0000 -1.20410 11.0000 -4.10650
42 -4.2000 11.77190 -8.9000 -11.00000
43 -10.0000 13.25970 -10.0000 -11.00000
44 -10.0000 -12.00000 -10.0000 -12.00000
45 10.0000 12.09780 10.0000 -11.00000
46 3.5100 10.00000 -10.0000 -11.00000
47 -10.0000 -10.00000 -10.0000 -10.00000
48 -10.0000 -10.00000 -10.0000 -10.00000
49 -10.0000 -10.00000 -6.7000 -10.00000
50 -8.0000 -3.61240 -5.7000 -0.82137
51 7.9500 6.74360 12.6560 -11.00000
52 -10.0000 -10.00000 -10.0000 -10.00000
53 -10.0000 -4.51540 -10.0000 -11.00000
54 10.0000 10.00000 10.0000 -11.00000
55 10.0000 10.00000 10.0000 -11.00000
56 -10.0000 -10.00000 -10.0000 -10.00000
57 -10.0000 -10.00000 -10.0000 -10.00000
58 7.9800 5.01540 0.9400 -11.00000
59 10.0000 10.00000 11.0000 -11.00000
60 10.0000 10.00000 0.3900 -11.00000
61 2.3100 4.46870 13.6117 -11.00000
62 -10.0000 -10.00000 -0.3000 -10.00000
63 0.9000 10.00000 -0.7000 -11.00000
64 13.8063 7.53280 1.7600 -11.00000
65 12.2137 -11.00000 13.2206 -11.00000
66 -12.0000 -12.00000 -12.0000 -12.00000
67 NA -0.30103 -12.0000 -12.00000
68 -10.0000 -10.00000 -10.0000 -10.00000
69 2.4000 1.20410 11.0000 -11.00000
70 0.7600 2.44680 1.4500 11.58190
71 10.0000 10.00000 2.9700 -11.00000
72 -10.0000 -3.01030 -12.0000 -0.37736
73 -10.0000 0.30103 -10.0000 -11.00000
74 -4.4100 -12.00000 NA -12.00000
75 -10.0000 -10.00000 11.0000 -10.00000
76 10.0000 10.00000 -12.0000 -11.00000
77 -10.0000 -10.00000 5.8800 -10.00000
78 -12.0000 -12.00000 -12.0000 -12.00000
79 -12.0000 11.18970 -12.0000 -11.00000
80 -12.0000 -12.00000 1.0400 -12.00000
81 0.5500 0.30103 -0.4000 -11.00000
82 13.0977 13.17320 -0.3000 -11.00000
83 1.2100 13.33510 -0.3000 -0.30103
84 13.2501 0.37650 11.9127 -11.00000
85 11.0000 12.52850 11.6720 -11.00000
86 -12.0000 -11.00000 -12.0000 -11.00000
87 -12.0000 -11.00000 -12.0000 -10.00000
88 -0.3000 -10.00000 -12.0000 -10.00000
89 -12.0000 -6.39540 -12.0000 -10.00000
90 -12.0000 -4.51540 -12.0000 -2.84050
91 -12.0000 10.00000 -12.0000 -11.00000
92 0.6000 9.84810 -12.0000 -11.00000
93 12.3884 -10.00000 0.3200 -10.00000
94 0.3200 -10.00000 11.4211 -10.00000
95 2.3200 10.00000 15.7044 -11.00000
96 13.4857 -4.15270 12.6979 -11.00000
97 13.9728 3.16310 -0.3000 -11.00000
98 11.0000 7.96680 14.2684 -11.00000
99 11.0300 10.00000 -12.0000 -12.00000
100 11.6467 10.00000 11.0000 -11.00000
101 11.0000 3.79950 11.2880 -11.00000
102 -12.0000 2.32060 -12.0000 -11.00000
103 14.9647 -11.00000 -0.6000 -11.00000
104 16.3843 -0.42640 0.3000 -11.00000
105 13.6065 12.55130 13.3129 -11.00000
106 13.3827 0.30103 11.3866 -11.00000
107 13.4317 -11.00000 12.5719 -11.00000
108 12.7100 -12.00000 14.6459 -12.00000
109 12.3004 11.64800 14.1101 -11.00000
110 12.5794 -11.00000 -0.6000 -11.00000
111 11.9765 -1.62030 11.0369 -11.00000
112 0.5400 1.60980 14.3327 -11.00000
113 -12.0000 -12.00000 -12.0000 -12.00000
114 -12.0000 -12.00000 11.0000 -11.00000
115 12.8144 1.19970 12.9982 -11.00000
116 11.3259 0.30103 11.0832 -11.00000
117 -10.0000 -2.23210 11.1078 -0.50515
118 0.7700 -12.00000 12.2549 -11.00000
119 10.0000 0.19605 15.4131 -11.00000
120 0.5100 -0.30103 0.4700 -11.00000
121 NA -12.00000 11.7390 -12.00000
122 13.1517 10.00000 12.5278 -11.00000
123 -12.0000 -12.00000 -12.0000 -12.00000
124 11.1042 11.82390 11.0000 -11.00000
125 -12.0000 -12.00000 -12.0000 -12.00000
Whole.Brain Adrenal.Gland Olfactory.Bulb Cortex e9.5.Embryo..ref.
1 -10.00000 -12.00000 -10.00000 -10.00000 -12.0000
2 -10.00000 -12.00000 -10.00000 -10.00000 -12.0000
3 9.24530 -12.00000 10.00000 10.00000 0.8400
4 10.00000 1.14430 8.15340 10.00000 1.0500
5 -7.53950 -0.60206 -10.00000 -10.00000 -1.9000
6 -4.69840 -12.00000 -10.00000 -10.00000 -12.0000
7 10.00000 10.00000 10.00000 10.00000 -12.0000
8 5.54350 9.03090 8.12780 9.66530 -12.0000
9 -10.00000 -10.00000 -10.00000 -10.00000 -12.0000
10 -10.00000 -0.92400 -10.00000 -10.00000 13.5257
11 -10.00000 -0.60206 -10.00000 -10.00000 -12.0000
12 -10.00000 -5.11750 -10.00000 -10.00000 -5.8000
13 8.02800 11.92190 2.02380 10.00000 12.5960
14 -3.91340 -12.00000 -10.00000 -10.00000 -10.0000
15 -9.33690 -1.50510 -4.31450 -1.88190 11.0951
16 -10.00000 -12.00000 -10.00000 -10.00000 -12.0000
17 -10.00000 -12.00000 -10.00000 -10.00000 -10.0000
18 10.00000 10.00000 10.00000 6.94460 -12.0000
19 10.00000 10.00000 10.00000 10.00000 4.9700
20 10.00000 10.00000 10.00000 10.00000 10.0000
21 -10.00000 -10.00000 -10.00000 -10.00000 -10.0000
22 -10.00000 -10.00000 -10.00000 -10.00000 -10.0000
23 -10.00000 -10.00000 -10.00000 -10.00000 -12.0000
24 -10.00000 -10.00000 -10.00000 -10.00000 12.7936
25 -11.00000 -12.00000 -1.80620 -12.00000 11.0000
26 -11.00000 -11.00000 -1.80000 -2.40000 11.0000
27 -3.31130 -0.30103 -10.00000 -10.00000 -1.5000
28 -3.86220 -12.00000 -8.69100 -4.81650 -12.0000
29 -10.00000 -12.00000 -10.00000 -10.00000 -12.0000
30 1.96890 2.40820 6.62270 0.30103 0.3000
31 -0.30103 -12.00000 -7.53950 -0.30103 -12.0000
32 -2.43270 -12.00000 -3.19940 13.95030 -12.0000
33 5.82680 3.91340 10.00000 1.50510 0.6000
34 -0.85067 -0.86766 -5.26470 -2.65180 13.0481
35 0.96108 0.96108 0.30103 -12.00000 15.1332
36 0.69523 13.27210 -2.94960 12.84000 13.9337
37 13.68710 -0.46095 13.70410 13.86160 14.0957
38 14.31420 0.76330 -0.84301 14.16630 14.4235
39 0.25794 -0.55502 14.50990 13.79540 -12.0000
40 2.70930 -12.00000 -12.00000 -12.00000 -12.0000
41 -3.61240 -1.20410 -12.00000 -12.00000 -12.0000
42 12.24020 -0.76479 -1.74840 -0.96108 -12.0000
43 3.70310 0.69211 0.89648 9.10600 14.7958
44 -1.50510 -12.00000 -2.10720 -3.14540 -12.0000
45 4.96890 -12.00000 12.95140 1.20440 NA
46 -10.00000 10.00000 -10.00000 -10.00000 10.0000
47 -10.00000 -10.00000 -10.00000 -10.00000 -10.0000
48 -10.00000 -10.00000 -10.00000 -10.00000 -10.0000
49 -7.19290 -10.00000 -10.00000 -10.00000 -6.6000
50 -1.13670 -6.90950 -1.00090 -3.79040 -12.0000
51 10.00000 3.27360 10.00000 2.96890 1.2600
52 -10.00000 -10.00000 -10.00000 -10.00000 -12.0000
53 -10.00000 -10.00000 -10.00000 -10.00000 -12.0000
54 10.00000 10.00000 10.00000 10.00000 10.0000
55 10.00000 10.00000 10.00000 10.00000 10.0000
56 -10.00000 -10.00000 -10.00000 -10.00000 -10.0000
57 -10.00000 -10.00000 -10.00000 -10.00000 -10.0000
58 -11.00000 0.16273 -11.00000 -11.00000 -12.0000
59 0.30103 7.57960 -11.00000 0.12494 10.0000
60 2.01050 0.50515 0.30103 -0.41206 10.0000
61 0.43794 13.64640 13.26410 0.71776 6.0100
62 -10.00000 -10.00000 -1.05680 -0.30103 -7.5000
63 10.00000 2.85300 7.84390 1.44400 16.0336
64 13.32800 10.00000 13.17780 14.22690 0.3100
65 12.23180 0.63856 13.41230 13.09920 13.4644
66 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
67 -12.00000 -12.00000 -0.90309 -12.00000 -12.0000
68 -10.00000 -10.00000 -10.00000 -10.00000 -12.0000
69 0.90309 -12.00000 3.31130 -12.00000 5.4100
70 4.25100 -0.50515 7.80850 11.17690 8.1200
71 0.96108 8.72990 1.61050 2.70930 10.0000
72 -0.30103 -12.00000 -0.90309 -12.00000 -12.0000
73 0.30103 11.55000 -11.00000 -0.60206 -10.0000
74 -12.00000 -12.00000 -12.00000 -12.00000 -2.1000
75 -11.00000 -6.36250 -11.00000 -11.00000 -8.3000
76 2.40820 -12.00000 10.00000 -12.00000 10.0000
77 10.00000 -0.30103 10.00000 0.76479 -10.0000
78 -12.00000 -12.00000 -0.43870 -12.00000 -12.0000
79 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
80 4.97920 10.00000 7.91050 3.36560 -12.0000
81 0.30103 4.83130 0.30103 -0.53681 -12.0000
82 -11.00000 4.93330 -11.00000 -11.00000 -12.0000
83 12.73610 -4.51080 12.18790 0.42371 12.5414
84 12.18730 0.41402 11.90630 -0.59229 -12.0000
85 -0.30103 -12.00000 0.30103 11.16620 -12.0000
86 -12.00000 -12.00000 -11.00000 -12.00000 -12.0000
87 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
88 -11.00000 -0.30103 -11.00000 -12.00000 -1.2000
89 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
90 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
91 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
92 3.31130 0.60206 2.70930 3.91340 0.9000
93 12.49310 -1.12300 -0.51717 0.34606 12.9091
94 -0.53681 -11.00000 -11.00000 -11.00000 11.6355
95 15.99100 0.84497 15.66140 15.48850 -12.0000
96 11.73660 13.31840 0.48959 0.44078 13.6469
97 13.27740 13.48150 13.27560 13.25630 14.5999
98 13.57340 -12.00000 11.97360 -0.39263 -12.0000
99 -12.00000 -12.00000 0.23846 -12.00000 -12.0000
100 -11.00000 0.90309 0.59533 -11.00000 -12.0000
101 -11.00000 -12.00000 -11.00000 -11.00000 -12.0000
102 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
103 11.15860 14.34720 12.15800 11.07360 -0.6000
104 0.26049 16.00000 12.95370 -11.00000 -12.0000
105 12.81420 13.32950 12.81090 13.11130 0.8000
106 -0.20548 13.28200 -11.00000 -11.00000 -12.0000
107 12.24720 13.29180 12.65460 0.62088 -12.0000
108 13.68450 12.12240 14.46400 -0.39315 11.4819
109 14.12940 11.75490 13.65550 13.92780 -12.0000
110 11.04570 11.96380 -0.50936 11.82030 12.6830
111 -0.18293 11.01050 -11.00000 -11.00000 11.1931
112 14.46700 14.09320 14.36620 14.12760 15.6128
113 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
114 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
115 0.71606 12.39300 12.34840 12.74380 14.0452
116 0.43976 0.46376 -11.00000 -11.00000 -12.0000
117 -2.40820 -4.51540 -10.00000 -0.90309 -10.0000
118 11.38690 -12.00000 0.47378 11.99690 -12.0000
119 14.93340 14.24830 -0.64229 0.38636 12.3452
120 -11.00000 -12.00000 -11.00000 -11.00000 -12.0000
121 -0.56763 -12.00000 -1.07520 11.23760 -12.0000
122 0.30103 10.00000 10.00000 1.03250 2.1200
123 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
124 -12.00000 -11.00000 -11.00000 -11.00000 11.2922
125 -12.00000 -12.00000 -12.00000 -12.00000 -12.0000
Hippocampus TSCs..ref. Cerebellum Striatum e17.5.Placenta..ref.
1 -10.00000 -12.0000 -2.40820 -5.11750 -12.0000
2 -10.00000 -12.0000 -1.80620 -9.26890 -12.0000
3 10.00000 11.0000 5.10170 -12.00000 0.8000
4 8.53000 12.1818 6.57550 2.73780 1.0300
5 -3.61240 -12.0000 -10.00000 -9.26890 -12.0000
6 -2.76730 -12.0000 -10.00000 -6.92370 -12.0000
7 6.11080 -12.0000 10.00000 0.60206 -12.0000
8 2.23210 -12.0000 2.42380 0.90309 -12.0000
9 -10.00000 -12.0000 -10.00000 -10.00000 -12.0000
10 -10.00000 13.3267 -10.00000 -10.00000 12.2244
11 -10.00000 -12.0000 -10.00000 -9.63300 -12.0000
12 -10.00000 -12.0000 -10.00000 -10.00000 -10.0000
13 2.96370 0.6000 4.15290 5.38390 2.1100
14 -4.21440 -12.0000 -3.91340 -2.43270 -12.0000
15 -4.73660 -12.0000 13.03330 -2.77800 -12.0000
16 -10.00000 -12.0000 -10.00000 -12.00000 -2.6000
17 -10.00000 -12.0000 -10.00000 -12.00000 -5.1000
18 10.00000 -12.0000 10.00000 10.00000 -12.0000
19 10.00000 -12.0000 10.00000 10.00000 -12.0000
20 10.00000 2.4000 10.00000 10.00000 10.0000
21 -5.41850 -12.0000 -5.71960 -10.00000 -0.5000
22 -10.00000 -0.5000 -10.00000 -10.00000 -10.0000
23 -10.00000 -12.0000 -10.00000 -10.00000 -2.6000
24 -10.00000 -0.8000 -10.00000 -10.00000 -0.5000
25 -0.30103 -12.0000 -0.30103 -0.60206 -12.0000
26 -2.10000 -12.0000 -11.00000 -11.00000 -12.0000
27 -6.32160 -10.0000 -7.82680 -3.31130 -12.0000
28 -1.80620 -12.0000 -12.00000 -0.30103 -12.0000
29 -10.00000 -12.0000 -10.00000 -12.00000 -12.0000
30 0.30103 -12.0000 2.70930 1.20410 -12.0000
31 -0.60206 -0.3000 -1.20410 -12.00000 -12.0000
32 -2.42380 -12.0000 -7.91050 -0.84004 -12.0000
33 1.50510 -12.0000 5.71960 5.71960 0.6700
34 13.43920 -12.0000 13.99980 -3.75400 -1.9000
35 1.68410 -12.0000 0.30103 0.60206 -12.0000
36 12.79600 13.0068 12.99940 3.80630 -12.0000
37 13.61540 -0.5000 13.75710 -0.31874 -12.0000
38 14.34300 -0.4000 14.92150 -0.23613 14.4629
39 14.14720 14.7147 14.79260 12.26590 -12.0000
40 -12.00000 -12.0000 -12.00000 0.30103 -12.0000
41 -0.90309 -12.0000 -0.30103 -4.21440 -12.0000
42 -5.63370 -12.0000 14.13670 -0.41206 -12.0000
43 2.14300 -12.0000 0.91793 1.57150 -1.0000
44 -0.90309 -12.0000 -12.00000 -12.00000 -12.0000
45 5.98330 12.6591 4.53930 -3.61240 -12.0000
46 -2.23210 10.0000 -8.01300 -6.39540 -12.0000
47 -7.82680 -10.0000 -10.00000 -10.00000 -6.7000
48 -10.00000 -10.0000 -10.00000 -10.00000 -2.6000
49 -2.24890 -12.0000 -1.95070 -10.00000 14.9535
50 -2.01750 -3.0000 -0.90309 -6.39540 -12.0000
51 8.41680 0.3300 0.84109 2.45510 0.3900
52 -10.00000 -12.0000 -10.00000 -10.00000 -12.0000
53 -10.00000 -12.0000 -10.00000 -10.00000 -12.0000
54 10.00000 -12.0000 10.00000 10.00000 -12.0000
55 10.00000 -12.0000 10.00000 10.00000 7.8000
56 -10.00000 -12.0000 -10.00000 -10.00000 -12.0000
57 -10.00000 -12.0000 -10.00000 -10.00000 -10.0000
58 -11.00000 -12.0000 -11.00000 -11.00000 -12.0000
59 -11.00000 -12.0000 -11.00000 0.30103 1.7600
60 -11.00000 -12.0000 -11.00000 -11.00000 -12.0000
61 13.04630 -12.0000 13.42660 0.43062 -12.0000
62 -1.96890 -12.0000 -1.20410 -10.00000 -9.7000
63 15.30910 -3.9000 1.22070 1.49380 8.5700
64 13.50960 12.8573 12.86500 14.93070 5.2900
65 12.22410 -12.0000 12.53510 -1.38220 1.5400
66 -12.00000 -12.0000 -12.00000 -12.00000 -12.0000
67 -0.30103 -12.0000 -12.00000 -12.00000 -12.0000
68 -10.00000 -12.0000 -10.00000 -10.00000 -10.0000
69 0.30103 -12.0000 1.50510 -12.00000 -12.0000
70 2.49840 1.5000 3.43850 -12.00000 -3.1000
71 -12.00000 -12.0000 2.40820 -12.00000 -12.0000
72 -0.30103 0.4600 -12.00000 -12.00000 -12.0000
73 0.30103 -9.9000 0.30103 0.30103 -9.4000
74 -12.00000 -9.9000 -12.00000 -12.00000 -4.6000
75 -11.00000 -10.0000 -12.00000 0.30103 -10.0000
76 0.30103 -12.0000 7.22470 -12.00000 -12.0000
77 10.00000 -12.0000 10.00000 0.60206 -12.0000
78 -12.00000 -12.0000 -12.00000 -12.00000 -12.0000
79 -12.00000 -12.0000 -12.00000 -12.00000 -12.0000
80 1.50510 -12.0000 2.66320 1.80620 -12.0000
81 0.54395 -12.0000 0.63696 -0.30103 -12.0000
82 -0.96108 14.1389 -11.00000 -11.00000 13.3883
83 11.25080 15.6899 0.40311 0.43976 -12.0000
84 11.80480 14.6233 11.91180 12.02900 13.3116
85 -11.00000 -0.4000 -12.00000 0.30103 -12.0000
86 -12.00000 -12.0000 -12.00000 -12.00000 -12.0000
87 -12.00000 -12.0000 -12.00000 -12.00000 -12.0000
88 0.30103 11.0000 -0.72700 -12.00000 -12.0000
89 -12.00000 -1.5000 -12.00000 -12.00000 -12.0000
90 -12.00000 -4.7000 -12.00000 -12.00000 0.3900
91 -12.00000 10.0000 -12.00000 -12.00000 -12.0000
92 0.90309 10.0000 0.60206 1.50510 -12.0000
93 -0.48086 -10.0000 12.58520 12.07680 -4.0000
94 -11.00000 -10.0000 -0.41206 -11.00000 -10.0000
95 15.64540 9.7000 15.91680 16.00000 -12.0000
96 11.42320 -10.0000 0.79548 12.65820 -5.9000
97 -0.81142 2.5200 -0.93101 13.42260 -12.0000
98 13.03250 1.9100 11.49920 12.05820 -12.0000
99 -12.00000 -12.0000 -12.00000 -12.00000 -12.0000
100 -12.00000 0.7600 -11.00000 -0.30103 1.9700
101 -0.30103 -12.0000 -0.12494 0.62812 6.9200
102 -12.00000 -12.0000 -12.00000 -12.00000 4.0100
103 0.88200 -12.0000 11.47200 -0.54777 -12.0000
104 12.01520 -12.0000 12.75360 12.15430 -12.0000
105 12.53320 -12.0000 12.81330 12.90150 0.3800
106 0.50515 -12.0000 -11.00000 -11.00000 -12.0000
107 0.65499 -12.0000 12.35000 12.94270 -0.5000
108 14.53670 -12.0000 13.72620 14.58180 -12.0000
109 -0.28167 -12.0000 13.87990 14.30690 -12.0000
110 11.13340 -0.6000 11.57410 -0.86171 -12.0000
111 0.30103 11.0000 0.12494 -0.30103 -12.0000
112 14.18020 15.2339 -0.60139 0.50973 15.1800
113 -12.00000 -12.0000 -12.00000 -12.00000 -12.0000
114 -12.00000 1.2000 -12.00000 -12.00000 14.1592
115 11.66530 1.0200 11.66590 11.63020 13.1799
116 -11.00000 -12.0000 0.50515 -0.30103 -12.0000
117 -12.00000 -3.0000 -10.00000 -0.60206 -10.0000
118 0.30103 -12.0000 -0.18293 -0.30103 -12.0000
119 14.90530 12.7038 15.27730 15.17110 11.5230
120 -11.00000 -12.0000 -0.37416 0.30103 -12.0000
121 11.19040 -12.0000 -12.00000 12.35690 -12.0000
122 11.80560 10.0000 10.00000 11.70800 13.9329
123 -12.00000 -12.0000 -12.00000 -12.00000 -12.0000
124 -12.00000 -12.0000 -12.00000 -0.12494 -12.0000
125 -12.00000 -12.0000 -12.00000 -12.00000 -12.0000
Pancreatic.Islets MEFs..ref. Bladder Lung Duodenum White.Adipose
1 -12.00000 -12.0000 -12.00000 0.00000 -12.00000 -12.00000
2 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
3 1.05310 -12.0000 -12.00000 1.75490 0.56672 -12.00000
4 1.22330 13.5886 -11.00000 11.82630 0.30103 0.30103
5 -4.95140 -12.0000 -0.60206 -0.30103 -0.30103 -12.00000
6 -0.69782 -12.0000 -0.90309 -0.90309 -0.90309 -12.00000
7 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
8 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
9 13.12390 -12.0000 -12.00000 -11.00000 -12.00000 -12.00000
10 -10.00000 14.4902 12.61700 -0.81648 12.61830 12.22320
11 -10.00000 -12.0000 -12.00000 -1.50510 -3.91340 -12.00000
12 -6.02060 -12.0000 -12.00000 -12.00000 -0.90309 -12.00000
13 -1.03420 12.6959 0.26869 11.08790 0.79286 12.43920
14 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
15 -0.67372 -12.0000 -12.00000 -11.00000 -11.00000 -0.12494
16 -6.62270 -10.0000 -12.00000 -10.00000 -7.52570 -12.00000
17 -10.00000 -3.9000 -12.00000 -10.00000 -10.00000 -12.00000
18 8.12780 -12.0000 7.22470 1.96890 0.30103 3.01030
19 10.00000 -12.0000 8.42880 0.60206 5.41850 3.01030
20 10.00000 10.0000 10.00000 1.50510 3.61240 1.20410
21 -10.00000 -9.5000 -2.70930 -12.00000 -1.20410 -0.30103
22 -10.00000 -10.0000 -10.00000 -10.00000 -10.00000 -10.00000
23 -6.62270 -10.0000 -6.36250 -10.00000 -7.52570 -10.00000
24 -2.62830 -2.6000 -10.00000 -10.00000 -10.00000 -3.69870
25 -12.00000 -12.0000 -12.00000 -11.00000 -12.00000 -12.00000
26 -11.00000 -12.0000 -12.00000 11.64490 -12.00000 -12.00000
27 -7.52570 -2.1000 -12.00000 -0.30103 -4.51540 -12.00000
28 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
29 -1.50510 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
30 -12.00000 0.9000 -12.00000 0.30103 -12.00000 -12.00000
31 -12.00000 -12.0000 -12.00000 -0.90309 -12.00000 -12.00000
32 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
33 0.30103 -12.0000 2.70930 0.50515 -12.00000 0.60206
34 -11.00000 13.0864 12.09520 -2.75140 -0.24973 12.00250
35 13.67350 0.5900 -0.16273 0.65554 0.16273 -0.20548
36 12.09440 1.0200 0.43143 -0.41090 12.38190 13.33170
37 1.86030 -12.0000 0.36056 13.86970 13.96830 13.26840
38 13.76330 14.6533 13.36260 15.10170 -1.01600 0.37416
39 0.43910 15.3495 12.09650 13.51930 13.68170 2.84050
40 -12.00000 -12.0000 -12.00000 -11.00000 -12.00000 -12.00000
41 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
42 -12.00000 -12.0000 -12.00000 -11.00000 -12.00000 -12.00000
43 12.86530 -12.0000 13.56480 12.85930 0.34803 1.65670
44 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
45 0.67372 12.9324 -12.00000 11.56970 0.43976 -12.00000
46 7.22470 -12.0000 10.00000 10.00000 4.13980 10.00000
47 -12.00000 -10.0000 -10.00000 -3.61240 -1.20410 -10.00000
48 -8.28070 -10.0000 -10.00000 -10.00000 -10.00000 -10.00000
49 -2.93000 -12.0000 -4.51540 -3.79950 -0.47911 -10.00000
50 -0.46376 -12.0000 -4.81650 -2.67980 -1.96890 -10.00000
51 1.01510 -12.0000 1.00050 10.00000 1.76590 9.57010
52 -10.00000 -10.0000 -10.00000 -10.00000 -10.00000 -10.00000
53 -10.00000 -12.0000 -10.00000 -10.00000 -9.63300 -10.00000
54 10.00000 10.0000 10.00000 10.00000 10.00000 10.00000
55 10.00000 10.0000 10.00000 9.63300 10.00000 10.00000
56 -10.00000 -10.0000 -8.72990 -10.00000 -10.00000 -10.00000
57 -9.33190 -10.0000 -10.00000 -10.00000 -10.00000 -10.00000
58 10.00000 1.5000 1.80620 -11.00000 -12.00000 0.00000
59 -11.00000 5.6300 1.03420 -11.00000 8.84750 0.30103
60 10.00000 -12.0000 -12.00000 -12.00000 -12.00000 0.30103
61 0.55824 -12.0000 0.40253 12.38880 13.13620 13.31980
62 -0.60206 -1.7000 -7.22470 -0.12494 -0.30103 -12.00000
63 15.37470 -1.5000 15.64500 0.69211 1.66340 15.89600
64 -0.34948 -12.0000 12.13800 12.22020 0.90471 12.21350
65 -0.46992 12.6569 12.59060 11.50420 11.99670 0.53813
66 -0.30103 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
67 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
68 -10.00000 -12.0000 -10.00000 -10.00000 -6.03820 -6.68070
69 -12.00000 10.0000 0.30103 -12.00000 -12.00000 -12.00000
70 -12.00000 10.0000 -11.00000 -11.00000 -12.00000 -12.00000
71 0.60206 10.0000 -12.00000 2.93000 1.61050 1.50510
72 -12.00000 -10.0000 -12.00000 -3.31130 -0.30103 -12.00000
73 0.61363 -10.0000 -11.00000 12.42040 13.58200 -0.40311
74 -12.00000 -4.8000 -12.00000 -12.00000 -12.00000 -12.00000
75 14.52850 -10.0000 -2.10720 11.32540 -1.83130 -12.00000
76 -12.00000 -12.0000 1.50510 4.17970 6.62270 -12.00000
77 -0.94584 -12.0000 -0.50515 -8.29050 -1.80620 -0.30103
78 16.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
79 11.53510 -12.0000 -11.00000 -0.46376 -11.00000 -11.00000
80 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
81 -11.00000 -12.0000 0.43976 13.16200 2.01320 -12.00000
82 0.84424 -12.0000 2.28410 12.03290 4.06220 0.89147
83 -0.75845 -0.4000 11.54980 11.14270 14.69610 -12.00000
84 12.48100 11.8889 13.67570 13.70920 13.65530 13.17990
85 12.69130 -12.0000 0.34315 12.84660 0.27699 -0.96108
86 -12.00000 -12.0000 -12.00000 -0.61363 0.12494 -12.00000
87 -11.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
88 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
89 -11.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
90 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
91 -12.00000 -12.0000 12.16220 11.04610 11.33490 -12.00000
92 0.30103 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
93 11.45430 12.8352 -0.68814 0.31448 12.49550 11.94770
94 -11.00000 11.1709 -0.51767 0.40311 -0.64481 -0.43976
95 -0.32706 -12.0000 16.00000 15.97490 15.95170 16.00000
96 12.59380 11.9248 12.78190 11.79810 -0.56494 1.14760
97 14.83290 -12.0000 0.93692 13.29960 14.02200 13.24800
98 13.16590 -12.0000 0.60206 -0.43917 11.74560 -0.20548
99 -11.00000 -12.0000 -12.00000 -12.00000 -12.00000 -11.00000
100 1.14430 -12.0000 1.14830 1.14430 0.32028 1.20410
101 -11.00000 -12.0000 -11.00000 11.35040 -11.00000 13.45530
102 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 0.30103
103 -11.00000 11.3703 0.32732 11.42040 -0.34803 12.85540
104 13.94090 0.3500 16.00000 14.26060 -12.00000 16.00000
105 12.52920 12.0933 13.77180 13.75200 14.80370 13.87150
106 11.52350 -12.0000 12.98640 13.13640 12.49390 14.24630
107 -11.00000 12.2872 11.98320 0.32136 12.86100 12.95330
108 0.95556 -12.0000 -11.00000 0.30103 -0.59189 -12.00000
109 13.91150 -12.0000 11.70320 11.33090 11.89430 11.68470
110 11.15140 11.9990 11.70770 11.05270 -0.30103 0.26446
111 -12.00000 -12.0000 -12.00000 -0.12494 -12.00000 -12.00000
112 13.89690 14.9810 14.14040 14.33960 -0.40575 14.30780
113 -12.00000 -12.0000 -12.00000 -0.12494 -12.00000 -12.00000
114 -12.00000 -12.0000 -12.00000 -12.00000 12.14400 -12.00000
115 12.17570 13.1345 13.05090 13.39700 13.04470 12.60000
116 11.51250 -12.0000 -11.00000 -11.00000 12.03100 -0.59908
117 -2.40820 -12.0000 -10.00000 -3.01030 -4.21440 -3.31130
118 -12.00000 11.3106 -12.00000 -12.00000 0.30103 -12.00000
119 12.95380 -12.0000 0.40906 14.38800 0.38608 11.02740
120 -11.00000 -12.0000 -12.00000 -0.30103 -11.00000 -0.30103
121 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
122 10.00000 10.0000 10.00000 10.00000 10.00000 10.00000
123 0.38825 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
124 -0.12494 -12.0000 -0.12494 11.63630 0.30103 -11.00000
125 -12.00000 -12.0000 -12.00000 -12.00000 -12.00000 -12.00000
Skeletal.Muscle Skin Heart Stomach Thymus Kidney Liver
1 -11.00000 -12.00000 -12.00000 -12.00000 -12.00000 0.00000 -11.00000
2 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
3 -11.00000 0.40265 -12.00000 -11.00000 0.69371 0.83190 -11.00000
4 12.40700 -0.30103 -0.30103 11.52860 12.00680 11.55380 -11.00000
5 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
6 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
7 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
8 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
9 -12.00000 -2.40820 -12.00000 -0.90309 -11.00000 -12.00000 -12.00000
10 -11.00000 12.28860 0.30103 11.33300 12.59200 12.78240 11.52870
11 -0.30103 -12.00000 -12.00000 -0.64481 -12.00000 -1.50510 -12.00000
12 -0.30103 -12.00000 -12.00000 -0.60206 -12.00000 -12.00000 -12.00000
13 0.71274 12.06900 12.49260 11.96390 13.01840 12.13390 -0.24973
14 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
15 0.50515 -11.00000 -12.00000 -0.30103 -11.00000 -11.00000 -12.00000
16 -5.41850 -2.70930 -12.00000 -0.96108 -0.60206 -12.00000 -0.90309
17 -4.81650 -2.10720 -12.00000 -2.70930 -2.10720 -5.71960 -2.40820
18 0.90309 0.30103 0.30103 0.60206 0.30103 -12.00000 -12.00000
19 1.20410 3.91340 0.60206 -12.00000 1.20410 -12.00000 0.00000
20 2.10720 0.30103 0.60206 1.20410 1.45400 -12.00000 -12.00000
21 -11.00000 -0.90309 -0.60206 -12.00000 -11.00000 -12.00000 -0.30103
22 -7.69860 -8.58460 -2.64290 -4.10670 -3.99200 -10.00000 -2.88990
23 -5.41850 -2.70930 -0.72700 -0.96108 -0.60206 -12.00000 -0.90309
24 -2.31700 -1.20290 -3.31600 -5.19920 -2.19860 -2.10180 -1.23040
25 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -11.00000 0.12494
26 -12.00000 -12.00000 -12.00000 -12.00000 -11.00000 -12.00000 -0.30103
27 -0.30103 -1.50510 -3.91340 -12.00000 -12.00000 -1.80620 -0.30103
28 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
29 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
30 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
31 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
32 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
33 0.30103 0.30103 -12.00000 -12.00000 0.90309 -12.00000 -12.00000
34 -0.30103 11.24830 -11.00000 -11.00000 -0.55279 -0.30103 11.15640
35 1.02750 -12.00000 14.16530 0.38100 13.83340 0.25298 14.38450
36 12.02160 -1.22910 12.98720 11.61020 -1.07320 12.24930 11.91970
37 12.59900 -1.15880 12.56820 13.18720 -0.65787 12.88580 12.06230
38 12.60280 13.32220 12.29860 13.20340 14.61010 14.44410 13.40460
39 13.38400 13.80230 11.79740 13.07550 0.90309 13.36700 0.61363
40 -11.00000 -11.00000 -12.00000 -12.00000 -12.00000 -11.00000 -12.00000
41 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
42 12.68350 -11.00000 -12.00000 -12.00000 -11.00000 -12.00000 0.56159
43 0.22316 1.24930 -1.78060 12.58350 13.15270 12.39460 12.31670
44 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
45 11.63350 -0.51742 -12.00000 -11.00000 11.79860 0.30103 -11.00000
46 10.00000 0.30103 6.96640 1.80620 0.30103 6.96640 0.21269
47 -1.80620 -0.90309 -1.50510 -0.72700 -0.30103 -0.60206 -0.30103
48 -6.32160 -8.42880 -10.00000 -5.11750 -3.01030 -3.91340 -8.42880
49 -0.79047 -9.43810 -7.82680 -11.00000 11.63200 11.37160 11.17850
50 -2.70930 -8.12780 -7.52570 -0.12494 -0.30103 -2.10720 -4.13980
51 10.00000 0.51956 5.36830 5.96450 13.43150 10.00000 10.00000
52 -10.00000 -8.72990 -10.00000 -5.71960 -0.90309 -10.00000 -10.00000
53 -10.00000 -8.72990 -8.72990 -8.72990 -0.90309 -9.12690 -10.00000
54 10.00000 10.00000 2.49840 10.00000 10.00000 0.90309 10.00000
55 10.00000 10.00000 9.03090 3.61240 8.40250 -12.00000 9.63300
56 -10.00000 -0.90309 -1.50510 -10.00000 -0.90309 -10.00000 -3.31130
57 -7.52570 -10.00000 -10.00000 -10.00000 -8.17140 -9.36380 -2.10720
58 -11.00000 2.70930 -12.00000 -12.00000 -12.00000 -11.00000 -11.00000
59 -11.00000 -11.00000 11.30500 -12.00000 -11.00000 -12.00000 -11.00000
60 0.67372 -12.00000 -12.00000 -12.00000 -12.00000 0.60206 -12.00000
61 12.11130 -0.47315 12.58830 0.52146 -0.30103 13.11500 12.43890
62 -2.10720 -12.00000 -12.00000 -1.20410 -7.18430 -12.00000 -12.00000
63 0.69522 1.05030 15.58810 0.43422 -0.27171 0.30103 14.44930
64 -11.00000 11.15790 11.42450 -0.41774 -11.00000 11.30360 -11.00000
65 12.96870 -0.89952 11.84960 -11.00000 12.80690 11.76120 -11.00000
66 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
67 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
68 -10.00000 -10.00000 -1.45400 -10.00000 -10.00000 -10.00000 -10.00000
69 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
70 1.20410 -12.00000 -12.00000 -12.00000 0.30103 -12.00000 -11.00000
71 3.18300 -12.00000 0.96108 0.96108 0.90309 0.90309 -12.00000
72 -11.00000 -12.00000 -12.00000 -0.60206 -12.00000 -0.30103 -0.30103
73 0.43976 0.30103 3.28290 13.99300 -11.00000 12.57570 -11.00000
74 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
75 -10.00000 -10.00000 -2.93000 -11.00000 -0.30103 -0.16273 15.50400
76 10.00000 10.00000 5.71960 3.01030 4.81650 0.30103 3.01030
77 -10.00000 -12.00000 -2.10720 -0.87477 -0.60206 -0.60206 -0.30103
78 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
79 -11.00000 -0.30103 -11.00000 -11.00000 -11.00000 -11.00000 12.61380
80 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
81 0.38100 -11.00000 -11.00000 -11.00000 0.30103 11.59030 -12.00000
82 -11.00000 12.85600 1.31410 -11.00000 12.25550 13.49180 11.17990
83 -12.00000 -12.00000 -0.90309 11.06700 -11.00000 13.62010 14.56270
84 13.40230 -0.28180 13.67830 12.21010 13.42290 13.51440 -0.63979
85 -11.00000 13.55520 -12.00000 12.19240 12.40390 11.91700 -11.00000
86 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
87 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
88 -0.30103 -12.00000 -12.00000 -0.30103 -12.00000 -12.00000 -12.00000
89 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
90 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
91 -12.00000 -12.00000 -12.00000 12.13400 -12.00000 -0.67947 0.48972
92 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
93 11.96180 0.26777 11.83740 11.88320 11.02330 -0.38194 -0.63884
94 -12.00000 -12.00000 -11.00000 -12.00000 -11.00000 -12.00000 -11.00000
95 14.47230 15.44920 -0.30103 14.64900 15.10610 -0.59378 0.93523
96 11.46950 -0.27959 -0.30103 -0.30103 12.97100 -0.71927 -0.62420
97 11.79630 13.26890 0.73131 12.33630 13.64230 -0.53099 12.74460
98 11.32320 -12.00000 -12.00000 -0.30103 12.68640 13.28390 12.69730
99 -12.00000 0.30103 -12.00000 -12.00000 -12.00000 15.83760 -12.00000
100 -12.00000 1.17460 -12.00000 1.58230 0.30103 5.16000 15.01230
101 -11.00000 12.72520 -11.00000 -0.30103 -11.00000 -11.00000 5.44100
102 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
103 -11.00000 12.03250 -0.30103 -11.00000 13.16860 -0.16273 -11.00000
104 0.55521 -0.37805 16.00000 13.64820 14.82440 13.45390 -1.48000
105 12.51720 13.79530 12.40720 13.91730 12.85040 12.17220 12.79340
106 12.51790 13.08260 11.79560 12.37050 11.48630 11.17230 13.30430
107 11.10600 12.67670 12.71380 11.64770 -0.30103 11.99060 -0.30103
108 -12.00000 -12.00000 -11.00000 -11.00000 -0.60206 -11.00000 -11.00000
109 11.56110 11.88220 11.58120 -0.37416 0.20548 15.51830 11.46140
110 -11.00000 11.51560 11.25710 -11.00000 -11.00000 -0.43870 -0.69354
111 -11.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
112 -0.80658 14.26630 0.57295 13.59090 -0.57863 14.01400 13.47790
113 0.30103 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
114 -12.00000 -0.30103 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
115 -0.60489 -0.47711 -0.53412 12.03090 11.23610 12.27570 -11.00000
116 -11.00000 -1.04680 -11.00000 -11.00000 -11.00000 0.64481 0.50515
117 -0.72700 -0.90309 -2.10720 -0.90309 -1.45400 -0.30103 -12.00000
118 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
119 12.77680 11.71530 0.60583 -11.00000 -11.00000 14.32270 -11.00000
120 -12.00000 -11.00000 -12.00000 -11.00000 -12.00000 -12.00000 -12.00000
121 -12.00000 -0.30103 -12.00000 -0.30103 -12.00000 -12.00000 -12.00000
122 10.00000 7.45980 10.00000 10.00000 4.35670 9.93280 10.00000
123 -12.00000 -12.00000 -12.00000 -10.00000 -12.00000 -12.00000 -12.00000
124 -11.00000 -0.30103 -0.46376 -0.30103 13.66420 -0.30103 0.60206
125 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000 -12.00000
Whole.Bone.Marrow Spleen Testes
1 -12.00000 11.22570 -12.00000
2 -12.00000 -12.00000 -12.00000
3 -11.00000 -11.00000 -11.00000
4 -0.50515 -0.77949 0.30103
5 -12.00000 -12.00000 -11.00000
6 -12.00000 -12.00000 -11.00000
7 -12.00000 -12.00000 -12.00000
8 -12.00000 -12.00000 -12.00000
9 -12.00000 0.30103 11.51380
10 11.47270 12.35300 11.95070
11 -12.00000 -12.00000 -12.00000
12 -12.00000 -12.00000 -11.00000
13 0.30103 11.25640 11.24270
14 -12.00000 -12.00000 -12.00000
15 -12.00000 -11.00000 11.34390
16 -12.00000 -12.00000 -12.00000
17 -12.00000 -2.40820 -12.00000
18 -12.00000 -12.00000 -12.00000
19 -12.00000 -12.00000 -11.00000
20 -12.00000 -12.00000 12.22140
21 -12.00000 -12.00000 -0.75852
22 -12.00000 -0.42371 -1.94580
23 -12.00000 -12.00000 -0.43976
24 -11.00000 -8.73330 12.64770
25 -12.00000 0.30103 -12.00000
26 -12.00000 -12.00000 14.22990
27 -12.00000 -12.00000 -11.00000
28 -12.00000 -12.00000 -12.00000
29 -12.00000 -12.00000 11.12090
30 -12.00000 -12.00000 -12.00000
31 -12.00000 -12.00000 -11.00000
32 -12.00000 -12.00000 -12.00000
33 -12.00000 -12.00000 -12.00000
34 -12.00000 -0.78022 -11.00000
35 -12.00000 -12.00000 0.24414
36 12.05120 0.33907 12.77950
37 0.25722 12.08010 13.02330
38 -0.37802 -0.30103 14.24130
39 -11.00000 12.52800 12.68010
40 -12.00000 -12.00000 -12.00000
41 -12.00000 -12.00000 -12.00000
42 -12.00000 -12.00000 0.18293
43 -12.00000 -0.20548 13.76230
44 -12.00000 -12.00000 -12.00000
45 -12.00000 -11.00000 -12.00000
46 -12.00000 0.60206 12.05660
47 -12.00000 -0.30103 -10.00000
48 -12.00000 -4.21440 -5.68660
49 0.30103 11.23750 -0.50515
50 -12.00000 0.00000 -0.69152
51 12.31320 12.54590 12.78870
52 -12.00000 -0.90309 11.91460
53 -12.00000 -1.20410 -3.26420
54 -12.00000 1.48530 10.00000
55 -12.00000 -12.00000 0.46289
56 -12.00000 -1.20410 -11.00000
57 1.20410 -0.60206 -10.00000
58 11.11250 12.83400 -12.00000
59 -12.00000 -12.00000 12.39790
60 -12.00000 -12.00000 -11.00000
61 -0.45481 11.41870 13.31440
62 -12.00000 -11.00000 -0.30103
63 -0.51305 1.17020 13.39610
64 0.51767 11.13610 0.41129
65 -11.00000 0.40265 -11.00000
66 -12.00000 -12.00000 13.05140
67 -12.00000 -12.00000 11.04650
68 3.91340 -10.00000 -11.00000
69 -12.00000 -11.00000 -12.00000
70 -11.00000 -11.00000 -12.00000
71 -12.00000 0.30103 -0.20548
72 -12.00000 -0.60206 -0.30103
73 -12.00000 -11.00000 -11.00000
74 -12.00000 -12.00000 -11.00000
75 -12.00000 -12.00000 -11.00000
76 -12.00000 -12.00000 -11.00000
77 -12.00000 -3.91340 -0.37416
78 -12.00000 -12.00000 -12.00000
79 -12.00000 -12.00000 -12.00000
80 -12.00000 -12.00000 -12.00000
81 -12.00000 -11.00000 -12.00000
82 -0.76479 11.47530 12.59780
83 -12.00000 -12.00000 -11.00000
84 -4.51540 13.38720 11.39820
85 -4.27550 11.59180 -11.00000
86 10.00000 -11.00000 0.59533
87 -12.00000 -12.00000 -0.30103
88 -12.00000 -12.00000 0.30103
89 -12.00000 -12.00000 11.66190
90 -12.00000 -12.00000 11.19220
91 -12.00000 -12.00000 -12.00000
92 -12.00000 -12.00000 -12.00000
93 -11.00000 -0.39012 -0.27915
94 -12.00000 0.43976 12.54630
95 13.53910 14.98910 -0.28711
96 -0.44364 11.98030 -0.36987
97 13.61890 13.20330 -0.66397
98 -12.00000 -0.96108 0.68305
99 -12.00000 -12.00000 -12.00000
100 -12.00000 0.30103 0.30103
101 -11.00000 11.15530 -11.00000
102 -12.00000 -12.00000 -12.00000
103 -1.50510 -11.00000 13.28190
104 0.30103 12.64660 13.31470
105 12.42770 12.33870 12.73050
106 -11.00000 -12.00000 11.27790
107 -0.38487 0.21843 -11.00000
108 -12.00000 -12.00000 -11.00000
109 -0.51767 0.30103 14.48150
110 -12.00000 -11.00000 0.44223
111 -12.00000 -12.00000 -11.00000
112 -0.30103 13.67430 0.46498
113 -12.00000 -12.00000 -0.30103
114 -12.00000 -12.00000 0.30103
115 -12.00000 0.30103 -0.74427
116 -0.30103 -11.00000 -0.16273
117 -12.00000 -12.00000 -12.00000
118 -12.00000 -12.00000 -12.00000
119 -11.00000 11.72870 0.66243
120 -0.30103 -11.00000 -11.00000
121 -12.00000 -12.00000 11.24010
122 -2.10720 1.80620 3.26260
123 -12.00000 -12.00000 -12.00000
124 13.50890 0.48869 -12.00000
125 -12.00000 -12.00000 -11.00000Note also that in the html, the full data frame is printed, which means tons of scrolling, whereas only a preview of the tibble is printed, which is usually more convenient. The tibble type also doesn’t automatically convert character columns to factors. In old versions of R (pre 4.0.0), data.frame automatically did this to the consternation of many.
Now that you have all the essential elements of slicing, let’s do some more things with COVID-19 data, but this time with world wide data from “Our World in Data”: https://docs.owid.io/projects/etl/api/covid/. This is a big data set, so it might take a few moments to download.
Note. The cases and deaths are only reported every week in these data so new_cases, new_deaths, etc are the total for the week (I assume the date is the end of the week though the website wasn’t clear 🤷).
library(tidyverse)
owid = read_csv("https://catalog.ourworldindata.org/garden/covid/latest/compact/compact.csv")Before starting on the problems, take a look at which columns are provided in the table. This will help for solving the problems.
Plot the new cases per week per million people for the pandemic for the United States. Use a line plot.
(hint: use the help for plot, ?base::plot, to figure out how to set the plot type.)
(hint: try to make plot not too jagged by removing rows where new_cases == 0)
What week had the highest number of new deaths in the United States?
(hint: use the order (remember to sort descending) or which.max functions.)
What date and in what country was the worst (i.e., highest) for per capita death due to COVID-19?
What date and in what country was the worst (i.e., highest) for positivity rate COVID-19?
Create a new data frame using the data from owid called new_cases_per_100k consisting of the following columns, location, date, and cases, where cases is the number of new cases per 100,000 people. In two separate plots, plot the number of new cases per 100k people over time for the United Kingdom (plot 1) and Canada (plot 2). If you wanted to present these plots side by side so as to compare the severity of the pandemic in the UK vs Canada, what might you have to do to make them more comparable?
In 2021, on how many days did the United States have fewer than 0.7 deaths per million people due to COVID-19? What is answer the United Kingdom? Use the column new_deaths_smoothed_per_million to answer this question.
Challenge problem (+3 extra credit)
Plot a heatmap of the imprinting data using the heatmap function. The rows and columns of the heatmap should be labeled properly with the gene names (rows) and tissue names (columns). The Babak et al. (2015) paper has a similar heatmap in Fig 1. Hint: read carefully the help for the heatmap function and know that you can convert data frames to matrices with as.matrix.