DE analysis between conditions (time)¶
Time condition comparison AP-axis
Notebook for performing the DESeq2 analysis
Here we perform DEseq2 analysis for each condition comparison using time as a factor (i.e. testing DE at each timpoint between conditions).
This is performed on each condition separately.
library(DESeq2)
Loading required package: S4Vectors
Loading required package: stats4
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: ‘BiocGenerics’
The following objects are masked from ‘package:parallel’:
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply, parCapply, parLapply, parLapplyLB, parRapply,
parSapply, parSapplyLB
The following objects are masked from ‘package:stats’:
IQR, mad, sd, var, xtabs
The following objects are masked from ‘package:base’:
anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl,
intersect, is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce,
rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min
Attaching package: ‘S4Vectors’
The following object is masked from ‘package:base’:
expand.grid
Loading required package: IRanges
Loading required package: GenomicRanges
Loading required package: GenomeInfoDb
Loading required package: SummarizedExperiment
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages
'citation("pkgname")'.
Loading required package: DelayedArray
Loading required package: matrixStats
Attaching package: ‘matrixStats’
The following objects are masked from ‘package:Biobase’:
anyMissing, rowMedians
Attaching package: ‘DelayedArray’
The following objects are masked from ‘package:matrixStats’:
colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
The following objects are masked from ‘package:base’:
aperm, apply, rowsumproject_dir <- '../data/results/deseq2/'
date <- '20210124'
runDeseq2BetweenCondTime <- function (filename, output_filename) {
file_path <- paste(project_dir, filename, sep='')
print(paste("========================== RUNNING ", filename, " ============================", sep=""))
# https://bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html
counts <- read.csv(file_path, header = TRUE, sep = ",")
rownames(counts) <- counts$u_id
# Let's make sure our count data is in matrix format and is only the numeric columns i.e. everything but the genes
counts <- counts[,2:ncol(counts)]
sample_names <- colnames(counts) # Sample names
# Make sure we don't include the ID in our columns
count_matrix <- as.matrix(counts)
# We now set the row names to be the gene IDs
rownames(count_matrix) <- rownames(counts)
# Separate out each of the sample names to be the different experiment conditions
condition <- factor(sapply(sample_names, function(x){substr(x, start = 1, stop = 2)}))
time <- factor(sapply(sample_names, function(x){substr(x, start = 3, stop = 4)}))
tissue <- factor(sapply(sample_names, function(x){substr(x, start = 5, stop = 6)}))
condition_id <- factor(sapply(sample_names, function(x){if (grepl("ko", x, fixed = TRUE)) {1} else {0}}))
# For DEseq2 we need to turn this into a dataframe
sample_df = data.frame(sample_names = sample_names, condition = condition, time = time, tissue = tissue, condition_id=condition_id)
dds_mat <- DESeqDataSetFromMatrix(countData = count_matrix,
colData = sample_df,
design = ~tissue+condition_id) # Have tissue as a factor
dds <- estimateSizeFactors(dds_mat)
dds_mat$design # Print the design of the experiment
dds <- estimateSizeFactors(dds_mat)
num_samples_meeting_criteria <- 4 # be strict and enforce that at least half the samples need to meet the criteria (i.e. one full condition)
num_counts_in_gene <- 10 # They need at least 10 counts
keep <- rowSums(counts(dds_mat) >= num_counts_in_gene) >= num_samples_meeting_criteria
dds <- dds_mat[keep,] # Only keep the rows with this criteria
# Let's print the number of rows
print(paste("Dataset dimensions: ", nrow(dds), ncol(dds)))
if (ncol(dds) != 8) {
print(paste("================== WARNING WARNING WARNING YOUR COLUMNS MAY HAVE THE WRONG DIMS ===========================", sep=""))
}
# Run DEseq2
dds <- DESeq(dds)
# Build results table
res <- results(dds)
print(paste("Deseq2 design: ", design(dds)))
# Sumarise the results
summary(res)
# Lastly, we may want to see the results of the high logfoldchange e.g. > 1 with a padj value < 0.05
res_padj05_lfc1 <- results(dds, lfcThreshold=2)
table(res_padj05_lfc1$padj < 0.05)
# Save the results
res_ordered <- res[order(res$pvalue),]
output_filename <- paste(project_dir, output_filename, sep='')
write.csv(res_ordered, file = output_filename)
return(res_ordered)
}
Run each of the supplimentary DEseq 2 experiments
runDeseq2BetweenCondTime(paste('merged_df_posterior_11_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_posterior_11_', date, '.csv', sep=''))
[1] "========================== RUNNING merged_df_posterior_11_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 14884 8"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: tissue + condition_id"
out of 14884 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 62, 0.42%
LFC < 0 (down) : 8, 0.054%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 14884 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
12578-Cdkn2a 151.5313 7.221034 0.747206 9.66405 4.28578e-22 6.37895e-18
12151-Bmi1 6077.3530 0.752335 0.100942 7.45316 9.11332e-14 6.78213e-10
24113-Vax2 129.7000 4.444877 0.627088 7.08812 1.35943e-12 6.74457e-09
12032-Bcan 97.9164 1.873976 0.287800 6.51137 7.44661e-11 2.77088e-07
78593-Nrip3 78.3483 1.311626 0.236568 5.54439 2.94974e-08 8.78078e-05
... ... ... ... ... ... ...
22427-Wrn 1088.967 -4.81832e-05 0.0788431 -6.11127e-04 0.999512 0.999781
22152-Tubb3 44012.946 -1.12213e-04 0.2387785 -4.69947e-04 0.999625 0.999827
50793-Orc3 3436.838 -1.16617e-05 0.0560489 -2.08063e-04 0.999834 0.999935
66795-Atg10 216.396 3.51760e-05 0.2118081 1.66075e-04 0.999867 0.999935
54394-Crlf3 1325.691 -1.33851e-05 0.1631289 -8.20524e-05 0.999935 0.999935runDeseq2BetweenCondTime(paste('merged_df_posterior_13_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_posterior_13_', date, '.csv', sep=''))[1] "========================== RUNNING merged_df_posterior_13_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15043 8"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: tissue + condition_id"
out of 15043 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4156, 28%
LFC < 0 (down) : 3573, 24%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15043 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
245527-Eda2r 391.948 3.392528 0.1317647 25.7469 3.49473e-146 5.25712e-142
320429-Trank1 4195.667 2.038323 0.0946236 21.5414 6.37894e-103 4.79792e-99
20689-Sall3 5032.493 -0.902797 0.0424928 -21.2459 3.59635e-100 1.80333e-96
21349-Tal1 4476.607 2.585642 0.1391089 18.5872 4.08083e-77 1.53470e-73
13626-Eed 1077.450 -1.180654 0.0643553 -18.3459 3.56212e-75 1.07170e-71
... ... ... ... ... ... ...
53970-Rfx5 745.6879 -1.72120e-04 0.0654757 -0.00262876 0.997903 0.998168
243277-Adgrd1 19.7514 1.12995e-03 0.4932991 0.00229060 0.998172 0.998371
30050-Fbxw2 2049.3965 9.23083e-05 0.0494430 0.00186696 0.998510 0.998643
26425-Nubp1 1187.6584 8.07616e-05 0.0507485 0.00159141 0.998730 0.998797
71952-Riox1 934.3996 7.84622e-05 0.0686373 0.00114314 0.999088 0.999088runDeseq2BetweenCondTime(paste('merged_df_posterior_15_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_posterior_15_', date, '.csv', sep=''))[1] "========================== RUNNING merged_df_posterior_15_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15643 8"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: tissue + condition_id"
out of 15643 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 833, 5.3%
LFC < 0 (down) : 362, 2.3%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15643 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
12151-Bmi1 9272.095 1.14814 0.0614868 18.6730 8.20976e-78 1.28425e-73
20476-Six6 928.288 9.78025 0.5359886 18.2471 2.18109e-74 1.70594e-70
21349-Tal1 6417.605 3.44848 0.1938262 17.7916 8.21077e-71 4.28137e-67
24113-Vax2 567.385 8.73584 0.5009278 17.4393 4.15027e-68 1.62307e-64
12450-Ccng1 5613.820 1.22610 0.0735476 16.6709 2.13410e-62 6.67676e-59
... ... ... ... ... ... ...
51799-Rundc3a 11073.31 -1.37210e-04 0.1776573 -0.000772331 0.999384 0.999639
67398-Srpr 4379.06 8.45541e-05 0.1567136 0.000539545 0.999570 0.999761
434130-Ccdc8 1149.38 -1.12418e-04 0.3515756 -0.000319754 0.999745 0.999873
20333-Sec22b 3436.15 1.84246e-05 0.0855527 0.000215360 0.999828 0.999892
66241-Tmem9 4085.38 -1.26315e-05 0.1002142 -0.000126045 0.999899 0.999899runDeseq2BetweenCondTime(paste('merged_df_posterior_18_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_posterior_18_', date, '.csv', sep=''))[1] "========================== RUNNING merged_df_posterior_18_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15498 8"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: tissue + condition_id"
out of 15498 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2264, 15%
LFC < 0 (down) : 1678, 11%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15498 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
21349-Tal1 3243.017 3.13064 0.1010915 30.9684 1.43745e-210 2.22776e-206
21375-Tbr1 425.520 5.09250 0.2149681 23.6896 4.62003e-124 3.58006e-120
15228-Foxg1 397.421 5.90634 0.2518970 23.4474 1.40376e-121 7.25184e-118
21386-Tbx3 743.201 1.48186 0.0731583 20.2555 3.17929e-91 1.23182e-87
13798-En1 720.597 2.16826 0.1094159 19.8167 2.13834e-87 6.62800e-84
... ... ... ... ... ... ...
74362-Spag17 44.1697 1.69115e-04 0.2880849 5.87033e-04 0.999532 0.99979
240614-Ranbp6 3808.2142 -1.04189e-05 0.0818811 -1.27244e-04 0.999898 0.99999
381045-Ccdc58 464.5467 -7.41797e-06 0.0877941 -8.44928e-05 0.999933 0.99999
408062-Zfp873 311.6448 -9.81615e-06 0.1367160 -7.17996e-05 0.999943 0.99999
107272-Psat1 3831.9073 -7.48832e-07 0.0596016 -1.25640e-05 0.999990 0.99999runDeseq2BetweenCondTime(paste('merged_df_anterior_11_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_anterior_11_', date, '.csv', sep=''))[1] "========================== RUNNING merged_df_anterior_11_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 14784 8"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: tissue + condition_id"
out of 14784 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 114, 0.77%
LFC < 0 (down) : 20, 0.14%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 14784 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
12578-Cdkn2a 165.2090 7.62576 0.740688 10.29550 7.38331e-25 1.09155e-20
24113-Vax2 144.5959 4.33845 0.517341 8.38605 5.02762e-17 3.71642e-13
15416-Hoxb8 706.0535 7.22790 0.887593 8.14326 3.84765e-16 1.89612e-12
15415-Hoxb7 159.9976 8.40976 1.058035 7.94847 1.88830e-15 6.97916e-12
216166-Plk5 43.4126 2.62745 0.357956 7.34015 2.13353e-13 6.30842e-10
... ... ... ... ... ... ...
69938-Scrn1 2555.668 -1.05523e-04 0.1885943 -5.59521e-04 0.999554 0.999824
14760-Gpr19 681.998 -5.59130e-05 0.1671797 -3.34449e-04 0.999733 0.999913
56399-Akap8 7818.582 -2.02222e-05 0.0724646 -2.79063e-04 0.999777 0.999913
66234-Msmo1 5336.486 -1.20719e-05 0.1118192 -1.07960e-04 0.999914 0.999982
213499-Fbxo42 2736.754 7.37334e-07 0.0625216 1.17933e-05 0.999991 0.999991runDeseq2BetweenCondTime(paste('merged_df_anterior_13_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_anterior_13_', date, '.csv', sep=''))[1] "========================== RUNNING merged_df_anterior_13_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 14986 8"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: tissue + condition_id"
out of 14986 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4377, 29%
LFC < 0 (down) : 4218, 28%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 14986 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
15410-Hoxb3 1231.78 7.85710 0.2392313 32.8431 1.42771e-236 2.13957e-232
76161-Lamp5 3010.13 4.87498 0.1524921 31.9687 2.96872e-224 2.22446e-220
12151-Bmi1 7011.44 1.32665 0.0432139 30.6997 5.74892e-207 2.87178e-203
20675-Sox3 2633.26 -2.16810 0.0713168 -30.4010 5.33114e-203 1.99731e-199
27280-Phlda3 1033.35 2.10388 0.0731979 28.7424 1.12668e-181 3.37689e-178
... ... ... ... ... ... ...
56378-Arpc3 4334.18203 -7.40490e-05 0.0431619 -0.00171561 0.998631 0.998898
13419-Dnase1 308.12970 -1.72880e-04 0.1078681 -0.00160270 0.998721 0.998921
435811-Ldlrad2 8.81715 -9.24037e-04 0.7643027 -0.00120899 0.999035 0.999152
114606-Tle6 76.59359 -3.46786e-04 0.3091133 -0.00112187 0.999105 0.999152
77286-Nkrf 1131.81702 -7.28793e-05 0.0685811 -0.00106267 0.999152 0.999152runDeseq2BetweenCondTime(paste('merged_df_anterior_15_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_anterior_15_', date, '.csv', sep=''))[1] "========================== RUNNING merged_df_anterior_15_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15508 8"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: tissue + condition_id"
out of 15508 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 2610, 17%
LFC < 0 (down) : 1886, 12%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15508 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
15422-Hoxc13 2270.819 9.44095 0.387312 24.3756 3.10466e-131 4.81471e-127
15402-Hoxa5 555.272 6.93023 0.299430 23.1447 1.64275e-118 1.27379e-114
15438-Hoxd9 751.546 7.83114 0.347136 22.5593 1.08902e-112 5.62953e-109
15413-Hoxb5 499.452 7.06447 0.320937 22.0120 2.20927e-107 8.56535e-104
15430-Hoxd10 1265.550 8.51812 0.390317 21.8236 1.38586e-105 4.29839e-102
... ... ... ... ... ... ...
243910-Nfkbid 107.084 -1.84327e-04 0.1771694 -0.001040401 0.999170 0.999428
67544-Fam120b 4057.323 6.37699e-05 0.0862637 0.000739244 0.999410 0.999604
211401-Mtss1 12460.250 -5.98453e-05 0.1018736 -0.000587447 0.999531 0.999660
17354-Mllt10 3226.316 -4.84064e-05 0.1046439 -0.000462582 0.999631 0.999694
216177-AU041133 360.541 -4.89877e-05 0.1275424 -0.000384090 0.999694 0.999694runDeseq2BetweenCondTime(paste('merged_df_anterior_18_FEATURE_COUNTS_', date, '.csv', sep=''), paste('DEseq2_CNS_anterior_18_', date, '.csv', sep=''))[1] "========================== RUNNING merged_df_anterior_18_FEATURE_COUNTS_20210124.csv ============================"
[1] "Dataset dimensions: 15442 8"estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing[1] "Deseq2 design: ~" "Deseq2 design: tissue + condition_id"
out of 15442 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up) : 4191, 27%
LFC < 0 (down) : 4123, 27%
outliers [1] : 0, 0%
low counts [2] : 0, 0%
(mean count < 7)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
log2 fold change (MLE): condition id 1 vs 0
Wald test p-value: condition id 1 vs 0
DataFrame with 15442 rows and 6 columns
baseMean log2FoldChange lfcSE stat pvalue padj
<numeric> <numeric> <numeric> <numeric> <numeric> <numeric>
209448-Hoxc10 1311.682 8.75973 0.311277 28.1412 3.06769e-174 4.73712e-170
22160-Twist1 761.408 3.46965 0.135393 25.6265 7.73754e-145 5.97415e-141
15422-Hoxc13 1462.927 9.65259 0.399628 24.1539 6.79110e-129 3.49561e-125
15430-Hoxd10 746.998 8.50237 0.380516 22.3443 1.37223e-110 5.29748e-107
245527-Eda2r 484.995 2.99488 0.134576 22.2541 1.02906e-109 3.17813e-106
... ... ... ... ... ... ...
240174-Thada 1018.2475 -1.52725e-04 0.1368979 -0.001115611 0.999110 0.999331
55949-Eef1b2 13542.3669 -9.08883e-05 0.0840355 -0.001081547 0.999137 0.999331
442803-A830005F24Rik 34.6784 -2.30270e-04 0.3431027 -0.000671140 0.999465 0.999561
26373-Clcn7 2264.3380 -4.07776e-05 0.0648996 -0.000628318 0.999499 0.999561
11606-Agt 254.0168 5.14070e-04 0.9340988 0.000550338 0.999561 0.999561Print session info
sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] DESeq2_1.28.1 SummarizedExperiment_1.18.2 DelayedArray_0.14.1 matrixStats_0.58.0 Biobase_2.48.0
[6] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2 IRanges_2.22.2 S4Vectors_0.26.1 BiocGenerics_0.34.0
loaded via a namespace (and not attached):
[1] sass_0.4.0 splines_4.0.3 bit64_4.0.5 jsonlite_1.7.2 bslib_0.2.5 RcppParallel_5.1.4 StanHeaders_2.21.0-7
[8] assertthat_0.2.1 blob_1.2.1 GenomeInfoDbData_1.2.3 yaml_2.2.1 pillar_1.6.1 RSQLite_2.2.7 lattice_0.20-44
[15] glue_1.4.2 digest_0.6.27 RColorBrewer_1.1-2 XVector_0.28.0 colorspace_2.0-1 htmltools_0.5.1.1 Matrix_1.3-3
[22] XML_3.99-0.6 pkgconfig_2.0.3 sircle_0.0.0.9000 rstan_2.21.2 genefilter_1.70.0 zlibbioc_1.34.0 purrr_0.3.4
[29] xtable_1.8-4 scales_1.1.1 processx_3.5.2 BiocParallel_1.22.0 tibble_3.1.0 annotate_1.66.0 generics_0.1.0
[36] ggplot2_3.3.3 ellipsis_0.3.2 cachem_1.0.5 withr_2.4.2 cli_2.5.0 survival_3.2-11 magrittr_2.0.1
[43] crayon_1.4.1 memoise_2.0.0 evaluate_0.14 ps_1.6.0 fansi_0.4.2 pkgbuild_1.2.0 tools_4.0.3
[50] loo_2.4.1 prettyunits_1.1.1 lifecycle_1.0.0 stringr_1.4.0 V8_3.4.2 locfit_1.5-9.4 munsell_0.5.0
[57] AnnotationDbi_1.50.3 callr_3.7.0 compiler_4.0.3 jquerylib_0.1.4 rlang_0.4.11 grid_4.0.3 RCurl_1.98-1.3
[64] bitops_1.0-7 rmarkdown_2.8 gtable_0.3.0 codetools_0.2-18 inline_0.3.18 DBI_1.1.1 curl_4.3.1
[71] R6_2.5.0 gridExtra_2.3 knitr_1.33 dplyr_1.0.6 fastmap_1.1.0 bit_4.0.4 utf8_1.2.1
[78] stringi_1.5.3 Rcpp_1.0.6 geneplotter_1.66.0 vctrs_0.3.8 tidyselect_1.1.1 xfun_0.23 References:
Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550. doi: 10.1186/s13059-014-0550-8.
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