From ece04ca1bba9449c3224cb835bfa66659d9a1f03 Mon Sep 17 00:00:00 2001 From: Anoushka Date: Sat, 11 Dec 2021 14:01:21 -0500 Subject: [PATCH] updated vignette w all the new arguments and additions --- vignettes/.DS_Store | Bin 6148 -> 6148 bytes vignettes/.gitignore | 2 + vignettes/scisorseqr-StandardWorkflow.Rmd | 117 +++- vignettes/scisorseqr-StandardWorkflow.html | 723 --------------------- vignettes/scisorseqr-StandardWorkflow.pdf | Bin 372440 -> 0 bytes 5 files changed, 84 insertions(+), 758 deletions(-) create mode 100644 vignettes/.gitignore delete mode 100644 vignettes/scisorseqr-StandardWorkflow.html delete mode 100644 vignettes/scisorseqr-StandardWorkflow.pdf diff --git a/vignettes/.DS_Store b/vignettes/.DS_Store index 63b3783f8a0daa83985d738e44c61f643d08be14..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 100644 GIT binary patch delta 68 zcmZoMXfc=|&Zs)EP;8=}A_oHyFfuR*Z2Tz3K8bPGW_At%4o20DAHOqC<`+>E1WGX^ QfYbm1h~2Q+QRFZ)03Q1eX8-^I delta 179 zcmZoMXfc=|&e%RNQEZ}~q9`K+0|O8XFfimZ6fqPtq%ssT6irN2pKKt)5-AT9NM^_c zl1P#a@j!kFLn1>SLkf^A0+Qii`D}(Xh8!TPoI#Hvh#{9D#WN>AIVmSU31|h-d`5X7 ut??fWHYUomgKXH$&B4RL*uF9GJM(0I5k)~Jpb`ZjP5@#s*z72Bm>B>ch$xBx diff --git a/vignettes/.gitignore b/vignettes/.gitignore new file mode 100644 index 0000000..097b241 --- /dev/null +++ b/vignettes/.gitignore @@ -0,0 +1,2 @@ +*.html +*.R diff --git a/vignettes/scisorseqr-StandardWorkflow.Rmd b/vignettes/scisorseqr-StandardWorkflow.Rmd index 9dc410f..62c00da 100644 --- a/vignettes/scisorseqr-StandardWorkflow.Rmd +++ b/vignettes/scisorseqr-StandardWorkflow.Rmd @@ -1,8 +1,10 @@ --- -title: "scisorseqr-StandardWorkflow" -output: pdf_document -vignette: | - %\VignetteIndexEntry{scisorseqr-StandardWorkflow} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} +title: "scisorseqr: Standard Workflow" +output: rmarkdown::html_vignette +vignette: > + %\VignetteIndexEntry{Vignette Title} + %\VignetteEngine{knitr::rmarkdown} + \usepackage[utf8]{inputenc} --- ```{r setup, include = FALSE} @@ -14,7 +16,9 @@ knitr::opts_chunk$set( The *scisorseqr* package allows you to go from short-read, single cell assignments and a set of fastq files to a full-blown cell-type specific analysis of alternative splicing patterns. -## Software required +## Setup + +### Software required * [STARlong](https://github.com/alexdobin/STAR/) (and corresponding genome index file) * [Minimap2](https://github.com/lh3/minimap2) if not using STARlong @@ -23,7 +27,17 @@ The *scisorseqr* package allows you to go from short-read, single cell assignmen * python version >= 3.7 with the following libraries * pandas * multiprocessing - + + +### Quick cheat for viewing documentation +All the possible arguments, usage, and description for each function can be viewed interactively +by putting a "?" in front of the function name in the R console like so: + +```{r demonstrateMan, include = TRUE, eval = FALSE} +?GetBarcodes +``` + + ## Step 1: Barcode deconvolution The first step is to deconvolve barcodes from either the PacBio or ONT fastq output For that the user needs to specify @@ -31,9 +45,9 @@ For that the user needs to specify - Tab separated Barcode-Celltype file - Directory containing fastq.gz files -### Create a barcode - celltype file assuming analysis using Seurat +### Create a barcode - celltype file +Assuming that short read single-cell analysis has been performed using Seurat: ```{r makeBCfile, eval=FALSE, echo=TRUE} - library(Seurat) library(tidyr) library(dplyr) @@ -79,13 +93,17 @@ You will see two output directories - OutputFiltered - file.CSV (One line per barcoded read with the following structure:) -|ReadName |T9_Status |Strand|T9_position|Barcodes| -|:----------|:-----|:---|:--|:----------------| -|ReadX|poly_T_found|rev|47|GTCGGGTTCCAAAGTC| +|ReadName |T9_Status |Strand|T9_pos|Barcodes|BC_pos|Cluster|UMI|TSO_Status|TSO_pos|Length| +|:----------|:-----|:---|:--|:----------------|:--|:-----------|:----------|:---------|:--|:----| +|ReadX|poly_T_found|rev|47|GTCGGGTTCCAAAGTC|22|Hipp_GranuleNB|ATGGCGGGAT|TSO_found|26|913| +|ReadY|poly_T_found|fwd|48|AGTGCCGATAGTTATG|22|Hipp_Astro|GGGATATGGC|TSO_found|26|1312| -|Barcode_position|Cluster|UMI|TSO_Status|TSO_position|Length| -|:--|:-----------|:----------|:---------|:--|:----| -|22|Hipp_GranuleNB|ATGGCGGGAT|TSO_found|26|583| +
+ +**NOTE 1** If you want to limit the downstream analysis to barcoded reads only, set the 'filterReads' parameter to TRUE. This will output a fastq.gz file in the OutputFiltered directory. +
+ +**NOTE 2** Setting 'concatenate' to FALSE ensures that your input files are treated as separate samples. ## Step 2: Align your fastq files to the reference @@ -97,11 +115,11 @@ We have made two aligners available and compatible with our package ```{r STARalign, eval = FALSE, echo = TRUE} ## Load example data -fastqFolder <- system.file("extdata/", "userInput/FastqFiles", +fastqFolder <- system.file("extdata/", "userInput/FastqFiles/", package = "scisorseqr") STARalign(fqFolder = fastqFolder, starProgPath = '~/bin/Linux_x86_64/STARlong', - refGenomeIndex = '~/starIndex_gencode10_sequins/', + refGenomeIndex = '~/starIndex_gencode10/', numThreads = 24) MMalign(fqFolder = fastqFolder, mmProgPath = '~/minimap2/minimap2', @@ -109,42 +127,66 @@ MMalign(fqFolder = fastqFolder, mmProgPath = '~/minimap2/minimap2', numThreads = 16) ``` -This process will convert all the aligned sam files to bam files, and dump the output into *STARoutput* +This process will convert all the aligned sam files to bam files, and dump the output along with run reports into *STARoutput* or *MMoutput* ## Step 3: Map and filter for full-length, spliced reads We used annotated [CAGE peaks](https://fantom.gsc.riken.jp/5/datafiles/reprocessed/mm10_latest/extra/CAGE_peaks/) and [PolyA sites](https://polyasite.unibas.ch/atlas) for our study -```{r MapAndFilter, eval = FALSE, echo = TRUE} +```{r MapAndFilter, eval = FALSE, echo = TRUE} MapAndFilter(annoGZ='[PATH_TO_annotation.gtf.gz]',numThreads=24, - seqDir = '[PATH_TO_DIR/chr*.fa.gz]', + seqDir = '[PATH_TO_GENOME_DIR/chr*.fa.gz]', filterFullLength=TRUE, cageBed = '[PATH_TO_Cage.bed.gz]', polyABed = '[PATH_TO_PolyA.bed.gz]', - cp_distance=50) + cp_distance=50, genomeVersion="mm10") ``` -**NOTE** the parameter seqDir takes in a reference genome broken down by chromosome so + +**NOTE 1:** the parameter seqDir takes in a reference genome broken down by chromosome so as to parallelize the process. This function has not yet been made generalizable to loading in the full reference +
-This will result in multiple files being stored in yet another output directory: *LRProcessingOutput* +**NOTE 2:** The genome and version is not auto-detected and hence defaults to 'mm10'. It does not affect any processing but might lead to confusion if processing a different species. +
+ +This will result in multiple files being stored in yet another output directory: *LRProcessingOutput*. A lot of these files +are not super relevant and there is an option to remove them during Step 4. ## Step 4: Concatenate all the information collected so far ```{r InfoPerLongRead, eval=FALSE, echo=TRUE} - InfoPerLongRead(barcodeOutputFile = 'OutputFiltered/FilteredDeconvBC_AllFiles.csv', mapAndFilterOut = 'LRProcessingOutput/', - minTimesIsoObserve = 3) + minTimesIsoObserve = 5, rmTmpFolder = TRUE) ``` -This will yield a directory *LongReadInfo* with file **AllInfo** which has the following structure +This will yield a directory *LongReadInfo*. Along with some files containing basic statistics on a per-barcode basis, this folder contains two main files: -|ReadName|Gene|Celltype|Barcode|UMI|IntronChain|Status|# Introns| -|:-------|:---|:-------|:------|:--|:----------|:-----|:--------| +* *AllInfo.gz*: Concatenated information for all poly-exonic, spliced, barcoded, mapped, and 5' **and** 3' complete reads +* *AllInfo_Incomplete.gz*: Same as above, except with a relaxed criterion for the 5' and 3' completeness +
+If annotated CAGE and PolyA peaks are not provided, then only an AllInfo file is generated, and the reads obviously do not contain information on assigned peaks +
+
+ +The *AllInfo.gz* file has the following structure, where Intron Chain contains the assigned TSS and PolyA site: + +|ReadName|Gene|Celltype|Barcode|UMI|IntronChain|ExonChain|Status|# Introns| +|:-------|:---|:-------|:------|:--|:-----------|:-------|:-----|:--------| +
+ +The *AllInfo_Incomplete.gz* file has the following structure, where the intron chain purely contains a string of introns. If a CAGE peak or PolyA site are not assigned, then the status is indicated as "NoTSS" or "NoPolyA" in the 7th and 8th column respectively + +|ReadName|Gene|Celltype|Barcode|UMI|IntronChain|TSS|PolyA|ExonChain|Status|# Introns| +|:-------|:---|:-------|:------|:--|:-----------|:--|:---|:-------|:-----|:--------| + +
+ +**NOTE:** In case you want to retain all the temporary files and / or re-run this step, set the argument 'rmTmpFolder' to FALSE. ## Step 5a: Quantify the various isoforms observed in your data If you provided CAGE and PolyA peaks at the MapAndFilter() stage, you can choose to quantify unique @@ -156,7 +198,7 @@ IsoQuant(AllInfoFile = 'LongReadInfo/AllInfo', ``` -Depending on which modality you chose as *TRUE*, you will see the following files in the *IsoQuantOutput* folder: +Depending on which modality you chose as *TRUE*, you will see the following files in the *IsoQuantOutput* folder: - *Modality*-*Modality*ID.csv - Num*Modality*PerCluster @@ -165,7 +207,7 @@ These will be used for the differential splicing analysis step ## Step 5b: [OPTIONAL] Quantify the various exons observed in your data For ONT reads we don't have too much confidence in this method and recommend using -our sister package IsoQuant. +our sister package [IsoQuant](https://github.com/ablab/isoquant). This function however, works for annotated as well as novel exons ```{r ExonQuant, echo = TRUE, eval= FALSE} @@ -247,14 +289,18 @@ condensedCellTypes = system.file("extdata/", "userInput/condensedCellTypes", package = "scisorseqr") triHeatmap(treeDir = 'TreeTraversal_Iso/', - comparisonList =condensedCellTypes, - outName =condensedCellTypes) + comparisonList = condensedCellTypes, + typeOfTest = "Iso", + outName = condensedCellTypes) ``` -**NOTE** Cell-type labels should correspond to the barcode-celltype list. If you input a cell-type for which pairwise DIE has not been calculated, it will output all zeros +This was not quite designed for visualizing differential exon analysis output and thus may give some errors. Please do not hesitate to reach out or open an issue if there is difficulty with this stuff. + +**NOTE 1:** Cell-type labels should correspond to the barcode-celltype list i.e. the way they appear in the AllInfo files. If you input a cell-type for which pairwise DIE has not been calculated, it will output all zeros (e.g. DivOligo in this case)
-**NOTE** The example data included in this package does not yield the plot below. This is just an example plot generated from the full dataset. + +**NOTE 2:** The example data included in this package does not yield the plot below. This is just an example plot generated from the full dataset. ```{r heatmap, out.width = '60%', echo=F} knitr::include_graphics("../man/figures/condensedCellTypes.png") @@ -273,9 +319,10 @@ knitr::include_graphics("../man/figures/examplePie.png") Each number in the legend corresponds to the number of isoforms needed to cross the deltaPI = 0.1 threshold
-**NOTE** Percentage labels do not always arrange themselves neatly in the figure (requires editing by hand) +**NOTE 1:** Percentage labels do not always arrange themselves neatly in the figure (requires editing by hand)
-**NOTE** The example data included in this package does not yield the plot above. This is just an example plot generated from the full dataset. + +**NOTE 2:** The example data included in this package does not yield the plot above. This is just an example plot generated from the full dataset. ## Done! diff --git a/vignettes/scisorseqr-StandardWorkflow.html b/vignettes/scisorseqr-StandardWorkflow.html deleted file mode 100644 index 3547bc1..0000000 --- a/vignettes/scisorseqr-StandardWorkflow.html +++ /dev/null @@ -1,723 +0,0 @@ - - - - - - - - - - - - - - -scisorseqr-StandardWorkflow - - - - - - - - - - - - - - - - - - - - - - - - -

scisorseqr-StandardWorkflow

- - - -

The scisorseqr package allows you to go from short-read, single cell assignments and a set of fastq files to a full-blown cell-type specific analysis of alternative splicing patterns.

-
-

Software required

- -
-
-

Step 1: Barcode deconvolution

-

The first step is to deconvolve barcodes from either the PacBio or ONT fastq output For that the user needs to specify

- -
-

Create a barcode - celltype file assuming analysis using Seurat

- -

The table should look something like this

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
V1V2
GTAGTCAAGACTTGAAPFC_Vasc
CGGAGCTTCGCGTTTCHipp_Astro
AGGCCGTCACATTCGAHipp_RGL
AGAATAGTCCCAGGTGPFC_InhibNeuron
GCGGGTTAGATAGGAGPFC_Astro
TCAGGATGTAGATTAGPFC_Oligo
CATTCGCGTAGCTTGTPFC_Vasc
CACTCCATCCAAATGCPFC_ExciteNeuron
-
-
-

Use that file to locate barcodes from the fastq files

-

Example files included in the package and loaded as follows:

- -

You will see two output directories

-
    -
  • OutputRaw -
      -
    • file.CSV (One line per read)
    • -
    • file_summary (Stats for run)
    • -
  • -
  • OutputFiltered -
      -
    • file.CSV (One line per barcoded read with the following structure:)
    • -
  • -
- - - - - - - - - - - - - - - - - - - -
ReadNameT9_StatusStrandT9_positionBarcodes
ReadXpoly_T_foundrev47GTCGGGTTCCAAAGTC
- - - - - - - - - - - - - - - - - - - - - -
Barcode_positionClusterUMITSO_StatusTSO_positionLength
22Hipp_GranuleNBATGGCGGGATTSO_found26583
-
-
-
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Step 2: Align your fastq files to the reference

-

We have made two aligners available and compatible with our package

- - -

This process will convert all the aligned sam files to bam files, and dump the output into STARoutput

-
-
-

Step 3: Map and filter for full-length, spliced reads

-

We used annotated CAGE peaks and PolyA sites for our study

- -

NOTE the parameter seqDir takes in a reference genome broken down by chromosome so as to parallelize the process. This function has not yet been made generalizable to loading in the full reference

-

This will result in multiple files being stored in yet another output directory: LRProcessingOutput

-
-
-

Step 4: Concatenate all the information collected so far

- -

This will yield a directory LongReadInfo with file AllInfo which has the following structure

- - - - - - - - - - - - - - - -
ReadNameGeneCelltypeBarcodeUMIIntronChainStatus# Introns
-
-
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Step 5a: Quantify the various isoforms observed in your data

-

If you provided CAGE and PolyA peaks at the MapAndFilter() stage, you can choose to quantify unique cage and polyA sites per gene. Otherwise the default option is False

- -

Depending on which modality you chose as TRUE, you will see the following files in the IsoQuantOutput folder:

- -

These will be used for the differential splicing analysis step

-
-
-

Step 5b: [OPTIONAL] Quantify the various exons observed in your data

-

For ONT reads we don’t have too much confidence in this method and recommend using our sister package IsoQuant.

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This function however, works for annotated as well as novel exons

- -

This will output the quantifications to a separate folder ExonQuantOutput

-
-
-

Step 6: Differential isoform analysis

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This step can be done using various settings for various modalities. Options include

- -

Depending on the modality chosen, this function automatically detects the input

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It also requires a tab-separated config file in the following format to be provided by the user

-

NOTE: Header here just for demonstration

- ------ - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Comparison1CelltypesComparison2Celltypes
HippNeuronHipp_ExciteNeuron,Hipp_InhibNeuronPFCNeuronPFC_ExciteNeuron,PFC_InhibNeuron
HippInhibHipp_InhibNeuronPFCInhibPFC_InhibNeuron
HippExciteHipp_ExciteNeuronHippInhibHipp_InhibNeuron
- -

Assuming you run this command, each comparison (line in the config file) yields a separate sub-directory in the folder TreeTraveral_Iso

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The most important file in that output directory is the results file, which has the following structure:

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
GenepvalsdPImaxDeltaPI_ix1maxDeltaPI_ix2FDR
ENSMUSG00000000001.40.56370286165077300NA1
ENSMUSG00000000078.7100NA1
ENSMUSG00000000088.710.07142857142857141NA1
ENSMUSG00000000326.130.3430301461382440.666666666666667131
-

NOTE Deeper data will yield significant values. These ^ are just a product of the example data subset we have provided with the package

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You can use the above output to plot your significant genes

-
-
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Step 7: Visualizations

-
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Triangular Heatmap

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This function plots a triangular heatmap of percentage DIE genes of pairwise comparisons in a directory It also takes in an untitled file list of cell types to be plotted as follows:

- - - - - - - - - - - - - - - - - - - - - -
Hipp_Astro
Hipp_ChorPlex
Hipp_DivOligo
Hipp_Ependymal
.
.
- -

NOTE Cell-type labels should correspond to the barcode-celltype list. If you input a cell-type for which pairwise DIE has not been calculated, it will output all zeros (e.g. DivOligo in this case)
NOTE The example data included in this package does not yield the plot below. This is just an example plot generated from the full dataset.

-

-
-
-

Pie Chart breaking down the significant genes

- -

-

Each number in the legend corresponds to the number of isoforms needed to cross the deltaPI = 0.1 threshold
NOTE Percentage labels do not always arrange themselves neatly in the figure (requires editing by hand)
NOTE The example data included in this package does not yield the plot above. This is just an example plot generated from the full dataset.

-
-
-
-

Done!

-
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