diff --git a/README.md b/README.md
index 032029158c6e02cedcfc9df7492c10b9245d2e5f..cba26069829914736a5a2c2c94eb15a526da9443 100644
--- a/README.md
+++ b/README.md
@@ -6,8 +6,27 @@
The EMOTE-tk R library focuses on exact 5'-end and/or 3'-end quantification of RNAs *via* targeted RNA-Seq.
+\
+**Figure 1: Overview schema of the features provided by the EMOTE-tk R library regrouped by
+phases of an RNA-end sequencing project.** EMOTE-tk provides functions (A) for FASTQ reads pre-
+processing, manipulation and validation based on a read_features table describing the structure of
+sequencing reads; (B) for aligning reads on reference sequences and compute abundances at the
+single nucleotide resolution to obtain (C) a count table representing the abundances (count) of RNA
+3' or 5' ends mapped to a reference sequence (rname) at a certain location (position) on a strand. In
+this example, the reads contain UMI extracted in the pre-processing step that are used at this step
+to remove PCR amplification duplicates introduced in the library preparation. (D) the count table
+can be further manipulated to apply some normalization or filtering to prepare the downstream
+analyses. (E) EMOTE-tk provides functions for downstream analysis of the count table for TSS
+identification, analysis of RNA-ends species proportions (e.g. 5'P/5’PPP in WT vs. mutant strain),
+periodic signal analysis such as ribosome translation speed profiling, or the exploration of 3' end
+modifications. (F) EMOTE-tk count table can also be analyzed further by other libraries: we illustrate
+this by a differential expression analysis to identify endoribonucleolytic cleavage sites by comparing
+abundances between WT and endoRNase deletion mutant strains.
+
Features:
-* Pre-processing raw reads fastq file
+
+* [Pre-processing raw reads fastq file](scripts/Pre-processing.ipynb)
* Reads parsing and validation
* Reads feature extraction
* Demultiplexing into separate fastq files
@@ -22,6 +41,7 @@ Features:
* Differential proportion statistical analysis
Applications:
+
* TSS identification → [notebook](scripts/TSS.identification.Prados-2016.ipynb)
* Endoribonucleolytic cleavage sites identification → [notebook](scripts/Endoribonucleolytic.cleavage.sites.identification.Broglia-2020.ipynb)
* co-translational degradation → [notebook](scripts/Co-translational.exoribonucleolytic.degradation.Khemici-2015.ipynb)
diff --git a/img/EMOTE-tk.topics.png b/img/EMOTE-tk.topics.png
new file mode 100644
index 0000000000000000000000000000000000000000..7e5e2fa711a4e40c122e94c0f3c591f2011f5333
Binary files /dev/null and b/img/EMOTE-tk.topics.png differ
diff --git a/scripts/Post-transcriptional.modifications.and.differential.proportion.analysis.Xu-2023.ipynb b/scripts/Post-transcriptional.modifications.and.differential.proportion.analysis.Xu-2023.ipynb
index ed766ce31cf8de34be0752377725dab049acdb66..89edda8b6e507ec25a0132bfa293015ac47dadec 100644
--- a/scripts/Post-transcriptional.modifications.and.differential.proportion.analysis.Xu-2023.ipynb
+++ b/scripts/Post-transcriptional.modifications.and.differential.proportion.analysis.Xu-2023.ipynb
@@ -4,19 +4,19 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# *EMOTE-tk* tutorial on 3'-end post transcriptional modifications exploration and differential proportion analysis\n",
+ "# *EMOTE-tk* notebook on 3'-end post-transcriptional modifications exploration and differential proportion analysis\n",
"\n",
"From \"MenT nucleotidyltransferase toxins extend tRNA acceptor stems and can be inhibited by asymmetrical antitoxin binding\" by Xu *et al.*, Nature commun. 2023. https://doi.org/10.1038/s41467-023-40264-3\n",
"\n",
"## Download dataset\n",
"\n",
- "Data accompanying the article publicly available on European Nucleotide Archive (ENA) at EBI https://www.ebi.ac.uk/ena/browser/view/PRJEB62085\n",
+ "Data accompanying the article are publicly available on European Nucleotide Archive (ENA) at EBI https://www.ebi.ac.uk/ena/browser/view/PRJEB62085\n",
"\n",
- "For this notebook, we will use some RNA-Seq samples multiplexed into one raw fastq file to reproduce results on the action of toxin MenT1 which adds ribonucleotides at the 3'-end of tRNAs as illustrated in [figure 3E of the original paper](https://www.nature.com/articles/s41467-023-40264-3/figures/3).\n",
+ "For this notebook, we will use some RNA-Seq samples multiplexed into one raw FASTQ file to reproduce some results on the action of toxin MenT1 which adds ribonucleotides at the 3'-end of tRNAs as illustrated in [figure 3E of the original paper](https://www.nature.com/articles/s41467-023-40264-3/figures/3).\n",
"\n",
- "The dataset will be directly downloaded in the `../data/MenT1` directory of the project (that should be readily available if the GitLab project is cloned).\n",
+ "The dataset will be directly downloaded in the [../data/MenT1](../data/MenT1) directory of the project (that should be readily available if the GitLab project is cloned).\n",
"\n",
- "The intial `data/MenT1` directory contains also the reference sequences of the tRNAs (multifasta file) to which we will align the 3'-end sequencing reads: `Msm.reference.tRNAs.with.CCA.fasta`"
+ "The initial [../data/MenT1](../data/MenT1) directory contains also the reference sequences of the tRNAs (multifasta file) to which we will align the 3'-end sequencing reads: [Msm.reference.tRNAs.with.CCA.fasta](../data/MenT1/Msm.reference.tRNAs.with.CCA.fasta)."
]
},
{
@@ -29,11 +29,14 @@
},
"outputs": [],
"source": [
+ "options(width = 250)\n",
+ "options(crayon.enabled = FALSE)\n",
+ "\n",
"data_dir <- \"../data/MenT1\"\n",
"\n",
"raw_fastq <- paste0(data_dir, \"/Msm.total.RNA.MenT1.Library.fq.gz\")\n",
"\n",
- "if(!file.exists(raw_fastq))\n",
+ "if (!file.exists(raw_fastq))\n",
" download.file(\"ftp://ftp.sra.ebi.ac.uk/vol1/run/ERR114/ERR11439143/Msm.total.RNA.MenT1.Library.fq.gz\",\n",
" destfile=\"../data/MenT1/Msm.total.RNA.MenT1.Library.fq.gz\")"
]
@@ -59,16 +62,16 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "── \u001b[1mAttaching core tidyverse packages\u001b[22m ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──\n",
- "\u001b[32m✔\u001b[39m \u001b[34mdplyr \u001b[39m 1.1.4 \u001b[32m✔\u001b[39m \u001b[34mreadr \u001b[39m 2.1.5\n",
- "\u001b[32m✔\u001b[39m \u001b[34mforcats \u001b[39m 1.0.0 \u001b[32m✔\u001b[39m \u001b[34mstringr \u001b[39m 1.5.1\n",
- "\u001b[32m✔\u001b[39m \u001b[34mggplot2 \u001b[39m 3.5.0 \u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 3.2.1\n",
- "\u001b[32m✔\u001b[39m \u001b[34mlubridate\u001b[39m 1.9.3 \u001b[32m✔\u001b[39m \u001b[34mtidyr \u001b[39m 1.3.1\n",
- "\u001b[32m✔\u001b[39m \u001b[34mpurrr \u001b[39m 1.0.2 \n",
- "── \u001b[1mConflicts\u001b[22m ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n",
- "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
- "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m masks \u001b[34mstats\u001b[39m::lag()\n",
- "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n",
+ "── Attaching core tidyverse packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──\n",
+ "✔ dplyr 1.1.4 ✔ readr 2.1.5\n",
+ "✔ forcats 1.0.0 ✔ stringr 1.5.1\n",
+ "✔ ggplot2 3.4.4 ✔ tibble 3.2.1\n",
+ "✔ lubridate 1.9.3 ✔ tidyr 1.3.0\n",
+ "✔ purrr 1.0.2 \n",
+ "── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──\n",
+ "✖ dplyr::filter() masks stats::filter()\n",
+ "✖ dplyr::lag() masks stats::lag()\n",
+ "ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors\n",
"Loading required package: GenomeInfoDb\n",
"\n",
"Loading required package: BiocGenerics\n",
@@ -94,12 +97,8 @@
"\n",
"The following objects are masked from ‘package:base’:\n",
"\n",
- " anyDuplicated, aperm, append, as.data.frame, basename, cbind,\n",
- " colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,\n",
- " get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,\n",
- " match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,\n",
- " Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,\n",
- " table, tapply, union, unique, unsplit, which.max, which.min\n",
+ " anyDuplicated, aperm, 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,\n",
+ " pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which.max, which.min\n",
"\n",
"\n",
"Loading required package: S4Vectors\n",
@@ -205,20 +204,10 @@
"\n",
"The following objects are masked from ‘package:matrixStats’:\n",
"\n",
- " colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse,\n",
- " colCounts, colCummaxs, colCummins, colCumprods, colCumsums,\n",
- " colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,\n",
- " colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,\n",
- " colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,\n",
- " colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,\n",
- " colWeightedMeans, colWeightedMedians, colWeightedSds,\n",
- " colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet,\n",
- " rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods,\n",
- " rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps,\n",
- " rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins,\n",
- " rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,\n",
- " rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,\n",
- " rowWeightedMads, rowWeightedMeans, rowWeightedMedians,\n",
+ " colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians,\n",
+ " colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds,\n",
+ " colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs,\n",
+ " rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians,\n",
" rowWeightedSds, rowWeightedVars\n",
"\n",
"\n",
@@ -226,9 +215,7 @@
"\n",
"Welcome to Bioconductor\n",
"\n",
- " Vignettes contain introductory material; view with\n",
- " 'browseVignettes()'. To cite Bioconductor, see\n",
- " 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
+ " Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
"\n",
"\n",
"\n",
@@ -294,8 +281,7 @@
}
],
"source": [
- "source(\"../src/emote-tk.R\")\n",
- "options(width = 250)"
+ "source(\"../src/emote-tk.R\")"
]
},
{
@@ -304,21 +290,24 @@
"source": [
"## Pre-processing of reads prior mapping\n",
"\n",
- "In this section, we will parde the raw reads to\n",
+ "In this section, we will parse the raw reads to:\n",
"* validate their content\n",
- "* split the fastq into 4 different fastq files, each one of them corresponding to a sample: control without toxin or tRNAs with toxin and replicate 1 or 2\n",
+ "* split the FASTQ file into 4 different FASTQ files, each one of them corresponding to a sample: control without toxin or tRNAs with toxin and replicate 1 or 2\n",
+ "\n",
+ "For more information on the other pre-processing features available, see the notebook on [pre-processing](Pre-processing.ipynb).\n",
"\n",
"The reads are 100 nt long and their structure can be schematized as follows:\n",
"```\n",
- "read pos: 1 2 6 25 40 46\n",
- " N · multiplexing barcode · recognition sequence · UMI · control sequence · 3'-5' tRNA-end reverse complement\n",
- " │ │ │ │ │ └ 55nt long\n",
- " │ │ │ │ └ must be AGATAC\n",
- " │ │ │ └ 15 random nucleotides (A, T, C or G)\n",
- " │ │ └ must be CGGCACCAACCGAGGCTCA\n",
- " │ └ must be one of ACAC, ATTG, GACG or GGTA\n",
- " │\n",
- " â”” one random nucleotide at the beginning\n",
+ "read position: 1 2 6 25 40 46 \n",
+ " N · multiplexing barcode · recognition sequence · UMI · control sequence · 3'-5' tRNA-end reverse complement \n",
+ " │ │ │ │ │ └ 55nt long \n",
+ " │ │ │ │ └ must be AGATAC \n",
+ " │ │ │ └ 15 random nucleotides (A, T, C or G) serving as \n",
+ " │ │ │ Unique Molecule Identifier (UMI) for PCR duplicates removal\n",
+ " │ │ └ must be CGGCACCAACCGAGGCTCA \n",
+ " │ └ must be one of ACAC, ATTG, GACG or GGTA \n",
+ " │ \n",
+ " â”” one random nucleotide at the beginning \n",
"```\n",
"\n",
"Here, we define the authorized barcodes and their associated experimental condition (in same order) so that we can reuse them later."
@@ -334,7 +323,7 @@
},
"outputs": [],
"source": [
- "barcodes <- c(\"ACAC\", \"ATTG\", \"GACG\", \"GGTA\")\n",
+ "barcodes <- c(\"ACAC\", \"ATTG\", \"GACG\", \"GGTA\")\n",
"conditions <- c(\"ctrl.r1\", \"ctrl.r2\", \"MenT1.r1\", \"MenT1.r2\")"
]
},
@@ -344,7 +333,7 @@
"source": [
"The above schematized structure is translated to an `EMOTE_read_features` table that will be used for reads parsing and validation.\n",
"\n",
- "For its intialisation, the user must specify which region on the reads contains the sequence destined to be aligned on the reference sequences."
+ "For its initialization, the user must specify which region on the reads contains the sequence destined to be aligned on the reference sequences."
]
},
{
@@ -432,7 +421,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "The UMI from position 25 to 39 are composed of A, T, C or G, thus is a feature of type 1 which is a string composed of a certain alphabet. This feature will be extracted and stored in the read identifier for future uses in the quantification step, thus the `readid_prepend = TRUE` argument)."
+ "The UMI from position 25 to 39 are composed of A, T, C or G, thus is a feature of type 1 which is a string composed of a certain alphabet. This feature will be extracted and stored in the read identifier for future use in the quantification step, thus the `readid_prepend = TRUE` argument)."
]
},
{
@@ -458,7 +447,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Then, the recognition sequence and the control sequence correspond to constant character strings and will be used to validate the reads. These are feature of type 2."
+ "Then, the recognition sequence and the control sequence correspond to constant character strings and will be used to validate the reads. These are features of type 2."
]
},
{
@@ -551,11 +540,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Once the `EMOTE_parse_read` table is defined, parsing of read can be performed. As we need also to demultiplex the samples here, we will directly call `EMOTE_demultiplex` which will call the `EMOTE_parse_read` internally to parse and validate the reads.\n",
+ "Once the `EMOTE_features` table is defined, parsing of read can be performed. As we need also to demultiplex the samples here, we will directly call `EMOTE_demultiplex` which will call the `EMOTE_parse_read` internally to parse and validate the reads.\n",
"\n",
- "## Demultiplex original raw fastq file(s) into separate fastq files\n",
+ "## Demultiplex original raw FASTQ file(s) into separate FASTQ files\n",
"\n",
- "Reads are validated against the read features and only valid reads are found in the demultiplexed fastq files."
+ "Reads are validated against the read features and only valid reads are found in the demultiplexed FASTQ files."
]
},
{
@@ -672,7 +661,7 @@
"|| Repeat threshold : 100 repeats ||\n",
"|| Gapped index : no ||\n",
"|| ||\n",
- "|| Free / total memory : 11.5GB / 31.3GB ||\n",
+ "|| Free / total memory : 21.5GB / 62.5GB ||\n",
"|| ||\n",
"|| Input files : 1 file in total ||\n",
"|| o Msm.reference.tRNAs.with.CCA.fasta ||\n",
@@ -700,7 +689,7 @@
"|| [ 90.0% finished ] ||\n",
"|| [ 100.0% finished ] ||\n",
"|| ||\n",
- "|| Total running time: 0.1 minutes. ||\n",
+ "|| Total running time: 0.2 minutes. ||\n",
"||Index ../data/MenT1/Msm.reference.tRNAs.with.CCA.fasta.subread/index w ... ||\n",
"|| ||\n",
"\\\\============================================================================//\n",
@@ -712,8 +701,9 @@
"reference_genome <- \"Msm.reference.tRNAs.with.CCA.fasta\"\n",
"reference_fasta <- paste0(data_dir, \"/\", reference_genome)\n",
"reference_index <- paste0(data_dir, \"/\", reference_genome, \".subread\")\n",
- "dir.create(reference_index, showWarnings=FALSE)\n",
- "buildindex(basename = paste0(reference_index, \"/index\"), reference = reference_fasta)"
+ "dir.create(reference_index, showWarnings = FALSE)\n",
+ "buildindex(basename = paste0(reference_index, \"/index\"), \n",
+ " reference = reference_fasta)"
]
},
{
@@ -743,14 +733,14 @@
" reference_genome)\n",
"\n",
"# perform mapping\n",
- "bam_files = mclapply(\n",
+ "bam_files <- mclapply(\n",
" fastq_files,\n",
" EMOTE_map_fastq,\n",
" fasta_name = reference_fasta,\n",
" index_name = reference_index,\n",
" max_map_mismatch = max_mismatch,\n",
" aligner = aligner,\n",
- " #force = TRUE,\n",
+ " force = FALSE,\n",
" bam_dir = bam_dir,\n",
" threads = 4,\n",
" mc.cores = 4\n",
@@ -763,7 +753,7 @@
"source": [
"## Convert bam files to count table with EMOTE_quantify_3prime\n",
"\n",
- "The `EMOTE_quantify_3prime` is called on each BAM file. The `read_features` table is passed to take into account the UMIs that were present in the reads to remove duplicates."
+ "The `EMOTE_quantify_3prime` is called on each BAM file. The `EMOTE_features` table is passed to take into account the UMIs that were present in the reads to remove duplicates."
]
},
{
@@ -839,13 +829,13 @@
],
"source": [
"count_table <- EMOTE_quantify_from_bam_files(bam_files,\n",
- " conditions,\n",
- " EMOTE_quantify_3prime,\n",
- " cores = 4,\n",
- " features = read_features,\n",
- " min_mapped_length = 20,\n",
- " rev_comp_reads = TRUE,\n",
- " keep_UMI = FALSE)\n",
+ " conditions,\n",
+ " EMOTE_quantify_3prime,\n",
+ " cores = 4,\n",
+ " features = read_features,\n",
+ " min_mapped_length = 20,\n",
+ " rev_comp_reads = TRUE,\n",
+ " keep_UMI = FALSE)\n",
"count_table %>%\n",
" head()"
]
@@ -856,7 +846,7 @@
"source": [
"## Downstream analysis: 3'-end post-transciptional modifications exploration\n",
"\n",
- "Pivot count_table to have counts for each sample for the same 3'-end. For noise reduction, we will only keep rows for which there are at least 10 reads in one sample."
+ "Pivot `count_table` to have counts for each sample for the same 3'-end. For noise reduction, we will only keep rows for which there are at least 10 reads in one sample."
]
},
{
@@ -933,7 +923,7 @@
"source": [
"count_table_w <- count_table %>%\n",
" select(-reads) %>%\n",
- " pivot_wider(names_from = \"barcode\", values_from=\"count\", values_fill = 0) %>%\n",
+ " pivot_wider(names_from = \"barcode\", values_from = \"count\", values_fill = 0) %>%\n",
" filter(if_any(all_of(conditions), ~ . >= 10))\n",
"count_table_w %>%\n",
" head()"
@@ -949,7 +939,7 @@
"source": [
"Next, we are going to focus on full length tRNAs. Thus, we will need their length.\n",
"\n",
- "tRNAs with expected length from genome reference fasta file:"
+ "tRNAs with expected length from reference sequences FASTA file:"
]
},
{
@@ -1024,9 +1014,11 @@
}
],
"source": [
- "tRNAs_DNASS <- readDNAStringSet( paste0(data_dir, \"/\", reference_genome))\n",
- "tRNAs <- tibble(rlength=width(tRNAs_DNASS), seq=as.character(tRNAs_DNASS), name=names(tRNAs_DNASS)) %>%\n",
- " mutate(id = str_extract(name, 'trna\\\\d+.\\\\S+'),\n",
+ "tRNAs_DNASS <- readDNAStringSet(paste0(data_dir, \"/\", reference_genome))\n",
+ "tRNAs <- tibble(rlength = width(tRNAs_DNASS),\n",
+ " seq = as.character(tRNAs_DNASS),\n",
+ " name = names(tRNAs_DNASS)) %>%\n",
+ " mutate(id = str_extract(name, \"trna\\\\d+.\\\\S+\"),\n",
" rname = as.factor(str_extract(name, \"^\\\\S+\"))) %>%\n",
" select(rname, rlength, seq)\n",
"tRNAs %>%\n",
@@ -1345,7 +1337,7 @@
"source": [
"## Visualize proportions of post-transcriptional modifications with bar plots\n",
"\n",
- "Longer format to compute proportions per tRNA per condition"
+ "Pivot to longer format to compute proportions per tRNA per condition"
]
},
{
@@ -1472,9 +1464,9 @@
")\n",
"options(repr.plot.width = 7, repr.plot.height = 7)\n",
"\n",
- "tRNAlabs = count_table_l$rname\n",
- "labs = tRNAlabs %>% as.character %>% unique %>% str_sub(5,11)\n",
- "names(labs) = tRNAlabs %>% as.character %>% unique \n",
+ "tRNA_labs = count_table_l$rname\n",
+ "labs = tRNA_labs %>% as.character %>% unique %>% str_sub(5,11)\n",
+ "names(labs) = tRNA_labs %>% as.character %>% unique \n",
"\n",
"count_table_l %>%\n",
" mutate(rname = fct_relevel(rname,\n",
@@ -1492,7 +1484,7 @@
" theme_classic() +\n",
" xlab(NULL) +\n",
" ylab(NULL) +\n",
- " facet_wrap(facets = . ~ rname, ncol = 6, labeller = labeller(rname = labs)) + \n",
+ " facet_wrap(facets = . ~ rname, ncol = 6, labeller = labeller(rname = labs)) +\n",
" labs(fill = \"3'-end modification\")"
]
},
@@ -1506,7 +1498,7 @@
"\n",
"For this, we will compare the proportion of modified tRNAs in the conrtol *vs.* the proportion of modified tRNAs in the samples treated with MenT1.\n",
"\n",
- "In the following, we regroup the modified tRNAs as the \"fraction\" and we sum up unmodfied and modified tRNAs in the \"total\" as expected by `EMOTE_differential_proportion`."
+ "In the following, we regroup the modified tRNAs as the \"fraction\" and we sum up unmodified and modified tRNAs in the \"total\" as expected by `EMOTE_differential_proportion` function."
]
},
{
@@ -1801,14 +1793,14 @@
}
],
"source": [
- "proportion_table <- count_table_l %>% \n",
+ "proportion_table <- count_table_l %>%\n",
" select(-rlength) %>%\n",
- " mutate(count_of = ifelse(downstream_seq==\"\", \"total\", \"fraction\")) %>%\n",
+ " mutate(count_of = ifelse(downstream_seq == \"\", \"total\", \"fraction\")) %>%\n",
" group_by(rname, strand, position, condition, count_of) %>%\n",
" summarise(count = sum(count), .groups = \"keep\") %>%\n",
" separate(condition, into = c(\"group\", \"replicate\")) %>%\n",
- " pivot_wider(names_from=\"count_of\", values_from=\"count\", values_fill=0) %>%\n",
- " mutate(total = total + fraction)%>% # for dss which expects total and fraction\n",
+ " pivot_wider(names_from = \"count_of\", values_from = \"count\", values_fill=0) %>%\n",
+ " mutate(total = total + fraction) %>% # for dss which expects total and fraction\n",
" pivot_longer(cols = c(\"fraction\", \"total\"),\n",
" names_to = \"count_of\",\n",
" values_to = \"count\") %>%\n",
@@ -1821,7 +1813,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Then, we need a `design_matrix` to specify the group and replicate for each `sample_id` and also if the counts in the `count_table` corresponds to the fractin or the total."
+ "Then, we need a `design_matrix` to specify the group and replicate for each `sample_id` and also if the counts in the `count_table` corresponds to the fraction or the total."
]
},
{
@@ -1905,9 +1897,9 @@
"source": [
"design_matrix <- tibble(sample_id = unique(sort(proportion_table$sample_id))) %>%\n",
" separate(sample_id,\n",
- " sep=\"\\\\.\",\n",
- " into=c(\"group\", \"replicate\", \"count_of\"),\n",
- " remove = F)\n",
+ " sep = \"\\\\.\",\n",
+ " into = c(\"group\", \"replicate\", \"count_of\"),\n",
+ " remove = FALSE)\n",
"design_matrix"
]
},
@@ -2124,22 +2116,11 @@
],
"source": [
"diff_prop <- EMOTE_differential_proportion(proportion_table,\n",
- " design_matrix,\n",
- " group1 = \"ctrl\",\n",
- " group2 = \"MenT1\")\n",
+ " design_matrix,\n",
+ " group1 = \"ctrl\",\n",
+ " group2 = \"MenT1\")\n",
"diff_prop"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "vscode": {
- "languageId": "r"
- }
- },
- "outputs": [],
- "source": []
}
],
"metadata": {