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1. Understand RNA-seq

a. Read about RNA-seq analysis

Yalamanchili et al. 2017: RNA-seq analysis pipeline

Some key points:

Protocol-1 (differential expression of genes):

  • demuxed raw reads (FastQC)
  • trimming reads (awk)
  • aligning reads (TopHat2)
  • counting reads (HTSeq; may filter out genes with low counts before next step)
  • detect DE using counted reads (DEseq2)
  • more QC (PCA/correlation heatmap)

Protocol-2 (differential usage of isoforms):

  • Protocol-1
  • counting isoforms (Kallisto, also check cell ranger)
  • detect DU using counted isoforms (Sleuth)
  • more QC (aslo PCA/correlation heatmap)

Protocol-3 (crypic splicing):

  • Protocol-1
  • detect differential junstions (CrypSplice)

b. Read more about RNA-seq analysis

Luecken et al. 2019: RNA-seq analysis pipeline

Some key points:

c. Read the Morris paper

Morris et al. 2021: STING-Seq

Some key points:

Some key ideas:

  • STING-seq: Systematic Targeting and Inbition of Noncoding GWAS loci with scRNA-seq
  • prioritizes candidate cis-regulatory elements (cCREs, 1kb<distance to TSS<1Mb) using fine-mapped GWAS
  • selected 88 variants (in 56 loci) with enhancer activity
  • dual CRISPR inhibition: dCas9 as the GPS, MeCP2 and KRAB as the repressors
  • confirming dual CRISPRi efficacy: gRNAs target TSS of MRPS23, CTSB, FSCN1
  • CRIPSRi on the 88 variants: two gRNAs for each variant, both within 200bp of the variant
  • ECCITE-seq: captures gRNAs and epitopes

Some data processing steps and results:

  • QC: remove cells with low total reads or excessive mitochondrial reads, gRNA assignment UMI>5 (9,343 cells after QC)
  • Kallisto: counting read more on the official website
  • Seurat: QC and reference mapping? read more on the official website
  • SCEPTRE: gRNA_to_gene-expression pairwise test
  • non-targeting gRNA-gene pairs: not significant (negative ctrl)
  • TSS-targeting gRNA-gene pairs: expression significantly decreased (positive ctrl)
  • 37 of the 88 variants were significant
  • Trans-regulatory elements: I'll come back later

Note:

2. Prelim QC for raw STING-seq data

a. Download all data

Code: download.sh

b. Perform FastQC on all fastq files

Code: fastqc.sh

SRR14141135:

SRR14141136:

SRR14141137:

SRR14141138:

SRR14141139:

SRR14141140:

SRR14141141:

SRR14141142:

SRR14141143:

SRR14141144:

SRR14141145:

SRR14141146:

A brief summary:

  • length: 26bp or 57bp (trimmed?)
  • depth: 30-35x
  • overall quality: good (within ~40 bp)

d. Kallisto | bustools pipeline

Code: pip3-kb.sh
Code: anaconda_kallisto.sh

3. Analyze QC’ed STING-seq data

a. Install packages

Code: seurat.sh

b. Data overview

Code: overview.R
Note: about sparse matrix

The [Expression] matrix has: 
- 35,606 rows/genes/targets 
- 686,612 columns/barcodes/cells 
- 24,447,506,872 values in total 
- 82,507,471 values that are non-zero 
- 50,421,358 values that are 1 
- 32,086,113 values that are bigger than 1 
- 3,370,699 values that are bigger than 10 
- 259,734 values that are bigger than 100 
- 2,515 values that are bigger than 1,000 
- 0 values that are bigger than 10,000 
- 0 values that are bigger than 100,000 
 
The [gRNA] matrix has: 
- 210 rows/genes/targets 
- 137,347 columns/barcodes/cells 
- 28,842,870 values in total 
- 2,506,474 values that are non-zero 
- 1,510,919 values that are 1 
- 995,555 values that are bigger than 1 
- 121,554 values that are bigger than 10 
- 41,071 values that are bigger than 100 
- 2,232 values that are bigger than 1,000 
- 20 values that are bigger than 10,000 
- 0 values that are bigger than 100,000 
 
The [Hashtag] matrix has: 
- 4 rows/genes/targets 
- 410,228 columns/barcodes/cells 
- 1,640,912 values in total 
- 739,820 values that are non-zero 
- 409,830 values that are 1 
- 329,990 values that are bigger than 1 
- 218,280 values that are bigger than 10 
- 8,155 values that are bigger than 100 
- 282 values that are bigger than 1,000 
- 46 values that are bigger than 10,000 
- 0 values that are bigger than 100,000

d. Expression (cDNA) dataset

i) Expression barcodes

Code: Expression_barcode_dist_plot.R

(Figures: pending)

ii) Expression targets

Code: Expression_target_dist_plot.R

Comment: The highest (mean) expressed gene is WDR45-like (WDR45L) pseudogene (high UMI counts in all cells)

Comment: The lowest (mean) expressed gene is RP4-669L17.1 pseudogene (zero UMI counts in all cells)

Comment: Non-zero UMI counts for all genes (~35k, including mito genes; 686,612 cells intotal)

Comment: CDF plot: ~80% genes have < ~5000 UMI counts in all cells (not all genes captured in each cell, but I guess still a lot)

Comment: PDF plot: same conclusion as above

e. gRNA dataset

i) gRNA barcodes

Code: gRNA_barcode_dist_plot.R

Comment: The cell with highest overall (mean) gRNAs, and it has 15 gRNAs

Comment: The cell with lowest overall (mean) gRNAs (transfection/transduction failed in this cell)

Comment: Non-zero UMI counts in all cells

Comment: CDF plot: ~80% cells have < ~40 UMI counts for each gRNA (note: the authors mentioned MOI ~ 10)

Comment: PDF plot: same conclusion as above

ii) gRNA targets

Code: gRNA_target_dist_plot.R

Comment: The highest (mean) gRNA in all cells (gRNA targeting PPIA-2, which is a control)

Comment: The lowest (mean) gRNA in all cells (likely it’s a low score gRNA site but the authors didn’t have better choices)

Comment: Non-zero UMI counst for all gRNAs (I’d say the transfection/transduction efficiency varies among gRNAs. The authors designed all the gRNAs within 200bp of the targeted variants,there must be limitations in terms of gRNA selection)

Comment: CDF plot: ~80% gRNAs have < ~20,000 UMI counts in all cells (137,347 cells in total, ~15% transfection/transduction success rate, acceptable)

Comment: PDF plot: same conclusion as above

f. Hashtag dataset

i) Hashtag barcodes

Code: Hashtag_barcode_dist_plot.R

Comment: The cell with highest (mean) Hashtags (note: the authors used only 4 Hashtags, I might check which antibodies they are when performing association)

Comment: The cell with lowest (mean) Hashtags (not tagged by any of the antibodies)

Comment: This figure is not an error. All cells have 1/2/3/4 UMI counts, and because many of them have 4, it looks like a block when it’s such dense

Comment: CDF plot: ~80% cells have < ~2 UMI counts for each Hashtag (It make sense to me because the authors are likely trying to label different cell types)

Comment: PDF plot: same conclusion as above

i) Hashtag targets

Code: Hashtag_target_dist_plot.R

Comment: The highest (mean) Hashtag (HTO23) in all cells (I would guess this is the relatively more common cell type, also, there were some non-specific antibody binding)

Comment: The lowest (mean) Hashtag (HTO25) in all cells (I would guess this is the relatively less common cell type, also, it dosen’t seem to overlap with HTO25, which is a good thing)

Comment: Non-zero UMI counts for the 4 Hashtags (I’d say the 4 cell types are relatively even)

Comment: CDF plot: ~80% Hashtags have < ~200,000 UMI counts in all cells (410,228 cells in total, I thinking the antibody binding efficiency is pretty good)

Comment: PDF plot: same conclusion as above


sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3     bslib_0.3.1      compiler_4.2.0   pillar_1.7.0    
 [5] later_1.3.0      git2r_0.30.1     jquerylib_0.1.4  tools_4.2.0     
 [9] getPass_0.2-2    digest_0.6.29    jsonlite_1.8.0   evaluate_0.15   
[13] tibble_3.1.7     lifecycle_1.0.1  pkgconfig_2.0.3  rlang_1.0.2     
[17] cli_3.3.0        rstudioapi_0.13  yaml_2.3.5       xfun_0.31       
[21] fastmap_1.1.0    httr_1.4.3       stringr_1.4.0    knitr_1.39      
[25] sass_0.4.1       fs_1.5.2         vctrs_0.4.1      rprojroot_2.0.3 
[29] glue_1.6.2       R6_2.5.1         processx_3.6.0   fansi_1.0.3     
[33] rmarkdown_2.14   callr_3.7.0      magrittr_2.0.3   whisker_0.4     
[37] ps_1.7.0         promises_1.2.0.1 htmltools_0.5.2  ellipsis_0.3.2  
[41] httpuv_1.6.5     utf8_1.2.2       stringi_1.7.6    crayon_1.5.1