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1. Understand SCEPTRE

a. Read the paper

Barry et al. 2021: SCEPTRE

Some key ideas:

  • A key confounder for perturb-seq is the read depth: total gRNA per cell seems to show a non-linear increase as total UMI per cell increases.

  • To overcome this, Barry et al. adopted two main strategies:
    • Include depth as a covariate when doing negative binomial (NB) regression for gRNA and expression.
    • Generate an empirical null distribution by randomization test.

Pipeline:

  • Negative binomial regression to get z-values for each gRNA-Expression pairs.
  • Logistic regression to get gRNA detection probabilities for each gRNA in each cell.
  • Conditional randomization test to get the empirical null distribution (when doing randomization, use the gRNA detection probabilities as the conditions).
  • Fit a skew-t distribution to the null distribution and then calculate p-values from z-values.

c. Install the package

Code: sceptre.sh

d. Tutorial

SCEPTRE tutorial

e. QQ plot function

Code: qqunif.plot.R
Ref: Code Sample: Generating QQ Plots in R

2. Manual regression

Aim: To better understand SCEPTRE, I’ll first perform naive negative binomial regression with and without total UMI as a covariate. This is mainly to check whether the negative controls have inflated p-values.

gRNA-gene pairs:

a. Naive NB regression

  • No UMI covariate.
  • No empirical null distribution.

Code: naive_NB.R

b. NB regression w/ total UMI cov

  • Total UMI counts for each cell in gene_matrix was used as a covariate.

Code: NB_with_covariate.R

c. Compare

Data: p_value.zip

QQ plot

Code: negative_control_qq.R

Observation:

  • After introducing total UMI as a covariate, the inflation for negative controls was significantly reduced.
  • However, inflation is still observed. SCEPTRE’s empirical null distribution for p-value calculation is expected to further reduce the inflation.

Smallest p-values

Code: head_pvalue.R

Observation:

  • For negative controls, the p-values increases after introducing the covariate in NB regression (less significant).
  • For positive controls and candidates, the p_values decreases after introducing the covariate in NB regression (more significant).
[1] "The smallest p-values in [negative_control_pvalues.csv] :"
     Gene   gRNA      p_value
1   TIMP1  NTC-2 1.893138e-14
2   DAAM1 NTC-10 5.380489e-12
3 ANAPC11 NTC-12 5.970695e-10
4    OST4  NTC-2 7.803250e-10
5  NDUFS5 NTC-12 2.727219e-09
6   MYDGF NTC-12 5.158344e-09

[1] "The smallest p-values in [negative_control_pvalues_with_covariate.csv] :"
     Gene   gRNA      p_value
1   DAAM1 NTC-10 6.169608e-08
2   HSPA8  NTC-3 2.177909e-07
3    TPT1  NTC-3 1.876122e-06
4   TAF10 NTC-10 4.655719e-06
5 ZFAND2A  NTC-3 4.809740e-06
6  VKORC1  NTC-3 7.248893e-06
[1] "The smallest p-values in [positive_control_pvalues.csv] :"
   Gene    gRNA      p_value
1  CD46  CD46-2 1.404819e-32
2 HSPA8 HSPA8-1 3.658421e-15
3  CD52  CD52-1 5.918623e-07
4 HSPA8 HSPA8-2 7.758467e-02
5   NMU   NMU-1 1.077502e-01
6  PPIA  PPIA-1 2.090641e-01

[1] "The smallest p-values in [positive_control_pvalues_with_covariate.csv] :"
   Gene    gRNA      p_value
1 HSPA8 HSPA8-1 7.940576e-42
2  CD46  CD46-2 4.646680e-36
3 HSPA8 HSPA8-2 3.540640e-19
4  CD52  CD52-1 2.594172e-08
5   NMU   NMU-1 5.694585e-02
6  PPIA  PPIA-1 9.576647e-01
[1] "The smallest p-values in [candidate_pvalues.csv] :"
    Gene     gRNA      p_value
1   CR1L SNP-20-2 6.805935e-33
2  PTPRC SNP-14-2 1.010292e-24
3   CTU2 SNP-35-1 2.420409e-20
4   ANK1 SNP-61-1 9.305671e-17
5 KDELR2 SNP-85-1 1.871454e-15
6   PAXX SNP-29-2 6.924731e-14

[1] "The smallest p-values in [candidate_pvalues_with_covariate.csv] :"
    Gene     gRNA      p_value
1   CR1L SNP-20-2 3.905370e-43
2   ANK1 SNP-61-1 2.579855e-31
3  PTPRC SNP-14-2 1.613814e-30
4  GLRX5 SNP-58-2 2.276511e-17
5 PDLIM1 SNP-77-2 1.042396e-12
6  NUDT4 SNP-62-2 1.123020e-12

3. SCEPTRE regression

SCEPTRE Documents

Code: sceptre_example.R

Code

Code: sceptre_package_modified.zip
Code: sceptre_morris.R
Code: qqplot_sceptre_all.R
Code: top_hits.R

Note:

  • I modified the SCEPTRE package source code a little bit and built the package locally.
  • SCEPTRE has very strict requirements for input. For example, every gRNA_group must contain 2 or more gRNAs.
  • I didn’t have the covariate_matrix from the authors, so I just used rnorm() to generate a matrix that I think will minimally affect the results.

Results


Observation

  • SCEPTRE reduced inflation to a minimal level.
  • Top hits from my run were all in Morris’ results (Table S3E).

Future direction

  • According to Nikita, there is indeed no covariate_matrix from the author.
  • Also according to Nikita, after PCA, there was no clear batch separation. We cannot infer batch from the data itself, and we may just drop the batch column in covariate_matrix.
  • Although no covariate_matrix provided by the author, I may generate it by myself. I can calculate the UMI counts, percent-mito, etc (there are 5 covariates in total might be used according to Nikita).
  • After running with the new covariate_matrix, I would expect to see even less inflation in the negative controls and more significant hits.

sessionInfo()
R version 4.2.1 (2022-06-23)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.1 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_1.0.9       compiler_4.2.1   pillar_1.8.0     bslib_0.3.1     
 [5] later_1.3.0      git2r_0.30.1     jquerylib_0.1.4  tools_4.2.1     
 [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_3.6.1   fansi_1.0.3     
[33] rmarkdown_2.14   callr_3.7.0      magrittr_2.0.3   whisker_0.4     
[37] ps_1.7.1         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