Changelog

sceptre 0.10.3 (2025-08-28)

In Version 0.10.3, we added standard errors to fold change estimates; see new fold_change and se_fold_change columns of association analysis outputs. In addition, we fixed bugs reported in issues #162 and #164, as well as a bug reported via email that arose when grna_target_data_frame contained NA values in the grna_id or grna_target columns.

sceptre 0.10.2 (2025-01-17)

Version 0.10.2 is a minor update in which we replaced the larger, real example data by smaller, simulated example data. Furthermore, the example data now live in the sceptre package, rather than in the external sceptredata package. Finally, the R-CMD-CHECK badge was restored on the website.

sceptre 0.10.1 (2024-04-30)

Version 0.10.1 includes minor updates to v0.10.0. A new gene position data frame gene_position_data_frame_grch37 was added.

sceptre 0.10.0 (2024-04-02)

Version 0.10.0 is a major update to the sceptre package. This version provides support for the analysis of large-scale single-cell CRISPR screen data. It also includes several other, minor updates.

We have introduced the ondisc-backed sceptre_object, which is a special kind of sceptre_object in which the data are stored on-disk as opposed to in-memory.
We have made the sceptre R package compatible with the sceptre Nextflow pipeline.
We have added the function plot_response_grna_target_pair(), which creates a violin plot of the expression level of a specified gene across treatment and control groups of a specified target.
We have added the function get_grna_assignments(), which facilitates obtaining the gRNA-to-cell assignments.
We have updated the sceptre e-book, adding two new parts: a part on at-scale sceptre and a part on the methodology underlying sceptre.
We have made the discovery pairs data frame and the positive control pairs data frame optional arguments to the set_analysis_parameters() function.
We have added a comprehensive suite of unit tests to help verify correctness of the code.
We have fleshed out the man pages, for example by adding a runable example to each.
We have moved the example data within the sceptre package into the companion sceptredata package.
We have issued minor bug fixes.

Please note that v0.10.0 is a higher version number than v0.9.2. Also, note that you will need to recreate your sceptre_object and rerun your analysis to use version 0.10.0. However, you should be able to use the exact same code to do so (assuming you currently are using v0.9.0 or higher).

sceptre 0.9.2 (2023-12-08)

Version 0.9.2 is a minor update to version 0.9.0.

Add an n_processors argument to the functions that enable parallelization to allow users to select the number of processors to use. (The default, n_processors = "auto", selects the number of processors to use based on the number of processors available on the machine.)
Add a log_dir argument, enabling users to specify the directory in which to write the log files.
Accelerate the plot_assign_grnas() function.
Fix the functionality for identifying mitochondrial genes; now, genes prefixed by “MT-” or “mt-” are considered mitochondrial.

sceptre 0.9.1 (2023-10-24)

Version 0.9.1 is a minor update to version 0.9.0.

We have added an experimental import_data_from_parse() function to import data from the output of Parse Biosciences CRISPR Detect.
We have added support for the "bonferroni" gRNA integration strategy.

sceptre 0.9.0 (2023-10-20)

Version 0.9.0 is a total rework of the sceptre package. The new version of the package has a fresh user interface and is faster, more memory-efficient, and more fully featured than previous versions. We summarize key updates here.

We have added a sceptre_object class to represent the single-cell CRISPR screen data.
We have unified low-MOI and high-MOI analysis into a single interface.
We have written a manual to guide users through the entire process of analyzing their single-cell CRISPR screen data.
We have added a new mixture model method for assigning gRNAs to cells in a principled way.
We have made the experimental high-MOI functionality (from version 0.3.0) the default functionality for high-MOI analysis.
We have added functionality to carry out cell-wise QC.
Mac and Linux users now can run sceptre in parallel.
We have added a suite of plotting functions to help users visualize the output of the different steps of the sceptre pipeline.
We have added helper functions to facilitate the construction of cis and trans discovery sets.
We have added a function to import data into sceptre from the output of one or more calls to CellRanger count.

sceptre 0.3.0 (2023-07-13)

Version 0.3.0 introduces a new, experimental high MOI function. We expect the experimental high MOI function to be faster, more memory efficient, and more powerful than the current high MOI function on most datasets. The current high MOI function likely will be deprecated in the next version of the package in favor of the experimental function. Please let us know about your experience using the experimental high MOI function, in particular whether you run into any bugs.

We also have added a new plotting function, namely plot_resampling_distribution. Small changes to the API of the run_sceptre_lowmoi function are detailed in the function documentation.

sceptre 0.2.0 (2023-04-03)

Version 0.2.0 is our biggest update yet. We have added functionality for low MOI analysis! The low MOI module is based new statistical methods and computational algorithms.

sceptre 0.1.0 (2022-03-10)

Version 0.1.0 is a major update to sceptre. Usability and speed are improved considerably.

Usability

The function run_sceptre_gRNA_gene_pair, which was redundant, is now deprecated.
run_sceptre_high_moi(previously called run_sceptre_in_memory) is simpler to use: the function now has only four required arguments: gene_matrix (previously called expression_matrix), gRNA_matrix (previously called expression_matrix), covariate_matrix, and gene_gRNA_pairs. The formerly required arguments storage_dir and side are now set to tempdir() and “both” by default. Additionally, the argument pod_sizes is removed entirely (and handled internally).
run_sceptre_high_moi has the additional optional arguments full_output and parallel. full_output controls the complexity of the data frame outputted by the method. When full_output is set to FALSE (the default), run_sceptre_high_moi outputs a data frame with four columns only, all of which are easy to interpret: gene_id, gRNA_id, p_value, and z_value. parallel controls whether the function is parallelized (TRUE; default) or not (FALSE).
run_sceptre_high_moi now accepts a raw (i.e., unthresholded) gRNA matrix or a user-thresholded gRNA matrix.
A new auxiliary function combine_gRNAs combines gRNAs that target the same chromosomal site.
Numerous checks have been added to run_sceptre_high_moi ensure that the input is valid. For example, run_sceptre_high_moi checks that gene IDs and gRNA IDs in gene_gRNA_pairs are in fact subsets of the row names of gene_matrix and gRNA_matrix, respectively.

Speed

Two accelerations have been implemented to improve speed. These accelerations do not affect the API of the package.

First, the test statistic used in the conditional randomization test has changed. Previously, the test statistic was a z-score derived from a Wald test of a fitted negative binomial GLM. Now, the test statistic is a z-score derived from a score test of the same negative binomial GLM, which is asymptotically equivalent to the former but more robust in finite samples. Additionally, this score test-based z-score is computed via an explicit formula, sidestepping the need to fit a GLM, as was done previously. Overall, the new test statistic is faster to compute and more robust than the previous test statistic.
The synthetic perturbation indicators are now generated as part of the gRNA precomputation, factoring out this somewhat time-intensive step from the pairwise tests of association.