dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-cell 3′-biased RNA-sequencing data preprocessing FASTQ files for each single cell were generated using bcl2fastq (version 2. 20) with default parameters and the following read configuration: Y8N*, I8, Y63N*. Read 1 corresponds to a cell-specific barcode, index read corresponds to an i7 index sequence from each cDNA pool and read 2 corresponds to the cDNA molecule. PolyA tails were trimmed from demultiplexed FASTQ files with TrimGalore (version 0. 4. 1). Reads were then aligned to the human genome (hg19) using STAR (version 2. 4. 2a), and counts for each gene were obtained with FeatureCounts (version 1. 4. 5-p1; options-primary). Counts were then normalized by dividing each gene count by the total library size of each cell and multiplying this value by the median library size of all cells processed, as implemented in the 'normalize_UMIs' function from the SingCellaR package 74 (version 1.
2. 1; https://github. com/supatt-lab/SingCellaR). A summary of the preprocessing pipeline can be found at https:// github. com/albarmeira/TARGET-seq-WTA. Quality control was performed using the following parameters: number of genes detected >500, percentage of ERCC-derived reads <35%, percentage of mitochondrial reads <0. 25% and percentage of unmapped reads <75%. Cells with less than 2,000 reads in batch1, 5,000 reads in batch2 and 20,000 reads in batch3 were further excluded. This QC step was performed independently for each sequencing batch owing to differences in sequencing depth (mean library size: 42,949 batch 1, 93,580 batch 2 and 171,393 batch 3). After these QC steps, 7,123 cells passed QC for batch 1, 5,779 for batch 2 and 6,319 for batch 3 (79. 3%, https://doi. org/10. 1038/s41588-023-01480-1 68.
Identification of highly variable genes Highly variable genes above technical noise were identified by fitting a gamma generalized linear model (GLM) of the log 2 (mean expression level) and coefficient of variation for each gene, using the 'get_vari-able_genes_by_fitting_GLM_model' from SingCellaR package and the following options: mean_expr_cutoff = 1, disp_zscore_cutoff = 0. 1, quantile_genes_expr_for_fitting = 0. 6 and quantile_genes_cv2_for_ fitting = 0. 2. Those genes with a coefficient of variation above the fitted model and expression cut-off were selected for further analysis, excluding those annotated as ribosomal or mitochondrial genes.
CNA inference from single-cell transcriptomes InferCNV (v1. 0. 4) was used to identify CNAs in single-cell transcriptomes  75  (https://github. com/broadinstitute/inferCNV/wiki). Briefly, inferCNV creates genomic bins from gene expression matrices and computes the average level of expression for each of these bins. The expression across each bin is then compared to a set of normal control cells, and CNAs are predicted using a hidden Markov model. For each patient, inferCNV was performed with the following parameters: 'cutoff = 0. 1, denoise = T, HMM = T', compared to the same set of normal donor control cells (n = 992). To identify CNA subclones, inferCNV in analysis_mode = 'subclusters' was used. CNAs identified by inferCNV were manually curated by removing those with size <10 kb, merging adjacent CNA calls with identical CNA status into larger CNA intervals and comparing them to SNP-Array bulk CNA calls.
Dimensionality reduction, data integration and clustering PCA was performed using 'runPCA' function from the SingCellaR R package, and Force-directed graph analysis was subsequently performed using the 'runFA2_ForceDirectedGraph' with the top 30 PCA dimensions to generate the plots in Extended Data Fig. 4a. For the analysis of patient IF0131 presented in Extended Data Fig. 3m , PCA was performed using 'runPCA' function from the SingCellaR R package and then UMAP was performed using the 'runUMAP' function with the top ten PCA dimensions and the following options: n. neighbors = 20, uwot. metric = 'correlation', uwot. min. dist = 0. 30, n. seed = 1. Integration of TARGET-seq single-cell transcriptomes from 10,459 cells corresponding to 14 TP53-sAML samples was performed using 'runHarmony' function implemented in the SingCellaR package, using the patient ID as covariate and the following options: n.
Pseudotime trajectory analysis Monocle3 (ref. 76; https://cole-trapnell-lab. github. io/monocle3/) was used to infer differentiation trajectories from single-cell transcriptomes. Raw UMI count matrix and clustering annotations were extracted from the SingCellaR object to build a Monocle3 'cds' object. Next, we retrieved the first two components of the diffusion map (DC1 and DC2), and the 'learn_graph' function was used to calculate the trajectory on the two-dimensional diffusion map, using TP53-WT preleukemic cell cluster as the root node. Pseudotime was calculated using 'order_cells' function and overlayed on the diffusion map embeddings to generate the plot in Fig. 2b.
Differential expression analysis Differentially expressed genes from TARGET-seq datasets were identified using a combination of nonparametric Wilcoxon test, to compare the expression values for each group, and Fisher's exact test, to compare the frequency of expression for each group, as previously described  17. Logged normalized counts were used as input for this comparison, including genes expressed in at least two cells. Combined P values were calculated using Fisher's method and adjusted P values were derived using Benjamini-Hochberg procedure. Significance level was set at P-adjusted < 0. 05. For the analysis presented in Extended Data Fig. 4b  and Supplementary Table  2 , the top 100 differentially expressed genes with log 2 (FC) > 0. 3 and at least 20% expressing cells are shown. Differentially expressed genes identified between TP53-multihit versus TP53-WT cells were further assessed for the enrichment of known p53 target genes (337 curated p53 target genes from ref.
77) for the analysis presented in Extended Data Fig. 4c. We assessed the extent of overlap of these gene lists using the R package GeneOverlap. The overlapping genes were further assessed for the enrichment of p53-related pathways using the R package clusterProfiler. For the analysis presented in Fig. 2k ,l, only genes overexpressed in TP53 multihit cells and log 2 (FC) > 0. 75 were included; for Fig. 4d , only those with log 2 (FC) > 1 were considered. Violin plots (Fig. 4e  and Extended Data Fig. 9n ) from selected differentially expressed genes were generated using 'ggplot2' package in R.
Gene-set enrichment analysis For analysis involving <600 cells (Fig. 4c  and Supplementary Table  5 ), GSEA was performed using GSEA software version 4. 0. 3 (www. gsea-msigdb. org/gsea/index. jsp) with default parameters and 1,000 permutations on the phenotype, using log 2 (normalized counts). For analysis involving >600 cells per group (Fig. 3k  and Extended Data Figs. 4d  and 9o ), GSEA was performed with 'identifyGSEAPre-rankedGene' function from SingCellaR R package with default options. Briefly, differential expression analysis was performed between two cell populations using the Wilcoxon rank sum test, and the resulting P values were adjusted for multiple testing using the Benjamini-Hochberg approach. Before the differential expression analysis, down-sampling was performed so that both cell populations had the same number of cells.
Next, -log 10 (P value) transformation was performed and the resulting P values were multiplied by +1 or -1 if the corresponding log 2 (FC) was >0. 1 or <-0. 1, respectively. The gene list was ranked using this statistic in ascending order and used as input for GSEA analysis using 'fgsea' function from the fgsea R package with default options. MSigDB HALLMARK v7. 4 50-gene sets or previously published signatures (https://www. gsea-msigdb. org/gsea/msigdb/cards/ GENTLES_LEUKEMIC_STEM_CELL_UP) were used for all analysis. Normalized enrichment scores were displayed in a heatmap using pheatmap R package. Gene sets with false discovery rate (FDR) q value lower than 0. 25 were considered significant.
Projection of single-cell transcriptomes A previously published human hematopoietic atlas was downloaded from https://github. com/GreenleafLab/MPAL-Single-Cell-2019 and used as a normal hematopoietic reference to project TP53-sAML and de novo AML transcriptions using Latent Semantic Index Projection  24. Common genes to all datasets were selected, and then TP53-sAML or previously published de novo AML cells  25  were projected using 'projectLSI' function for the analysis presented in Fig. 2c, d. A previously published human MF atlas  78  was used as a reference to project TP53-sAML multihit cells in the analysis presented in Extended Data Fig. 5d, e , using previously defined force-directed graph embeddings. https://doi. org/10. 1038/s41588-023-01480-1.
Velocyto analysis Loom files were generated for each single cell using velocyto (v0. 17. 13) with options -c and -U, to indicate that each BAM represents an independent cell and reads are counted instead of molecules (UMIs), respectively  79. The individual loom files were subsequently merged using the combine function from the loompy Python module. HDs with at least 300 cells with RNA-sequencing data and patients with at least 300 cells consisting of >50 preleukemic (TP53 WT) cells and >50 TP53 multihit cells were included for analysis. For each individual, the Seurat object was created from the merged loom file and processed for downstream RNA-velocity analysis  80. Specifically, for each patient, the spliced RNA counts were normalized using regularized negative binomial regression with the SCTransform function  81.
Next, linear dimension reduction was performed using RunPCA function and the first 30 principal components were further used to perform nonlinear dimension reduction using the RunUMAP function. Ninety-six multiple rate kinetics (MURK) genes previously shown to possess coordinated step-change in transcription and hence violate the assumptions behind scVelo were removed  82. The processed and MURK gene-filtered Seurat object was then saved in h5Seurat format using the SaveH5Seurat function and finally converted to h5ad format using the 'Convert' function. AnnData object was created from the h5ad file using the scvelo python module for RNA velocity analysis  83. Highly variable genes were identified and the corresponding spliced and unspliced RNA counts were normalized and log 2 -transformed using the scvelo.
Analysis of bulk BeatAML and TCGA gene expression datasets Data retrieval and preprocessing. Two publicly available AML cohorts with genetic mutation and RNA-sequencing data available were used to validate findings from our single-cell analysis, namely BeatAML 26 and TCGA  27. Gene expression values in FPKM (fragments per kilobase of transcript per million mapped reads) were retrieved from the National Cancer Institute (NIH) Genomic Data Commons (GDC)  84. Gene expression values were then offset by 1 and log 2 transformed. TP53 point mutation status was retrieved from the cBio Cancer Genomics Portal (cBioPortal)  85. Clinical data including survival data for BeatAML and TCGA were retrieved from the BeatAML data viewer (Vizome) and NIH GDC, respectively. We selected samples from the BeatAML cohort with an AML diag nosis (540 de novo AML and 96 secondary AML) collected within 1 month of the patient's enrollment in the study, and with both TP53 mutation status and RNA-sequencing data available.
The TCGA cohort consisted of 200 de novo AML patients represented by one sample each, of which 151 patients had TP53 mutation status (140 TP53 WT and 11 TP53 mutant) and RNA-sequencing data available, and were included for analysis. Of these, 132 patients had concomitant survival data available (124 TP53 WT and 8 TP53 mutant). Cell lineage gene signature scores. For each sample, a given cell lineage gene signature score was computed as the mean expression values of the individual genes belonging to the cell lineage gene signature. Here the gene signature scores for two cell lineages were computed, namely myeloid and erythroid populations. Two gene sets for each cell lineage were compiled. The first gene set was based on cell lineage markers previously reported in the literature, whereas the second gene set was based on cell lineage markers derived from analyzing a published single-cell dataset  24.
Genes from each score are described in Supplementary Table  3. For the former approach, six erythroid genes (KLF1, GATA1, ZFPM1, GATA2, GYPA and TFRC; Fig. 2e  and Extended Data Figs. 5k,m and seven myeloid genes (FLI1, SPI1, CEBPA, CEBPB, CD33, MPO and IRF8; Fig. 2f ) were identified. For the latter approach, the expression values of erythroid and myeloid cell clusters were first compared separately against all other cell clusters using Wilcoxon ranked sum test. The erythroid cluster consisted of the early and late erythroid populations, while the myeloid cluster consisted of granulocyte, monocyte and dendritic cell populations. Erythroid and myeloid-specific gene signatures were defined as genes having FDR values <0. 05 and log 2 (FC) > 0. 5 in ≥20 and 17 comparisons, respectively. In total, 100 erythroid genes and 135 myeloid genes were identified from this single-cell dataset (Supplementary Table  3 ) and were used to compute the scores presented in Extended Data Fig.
Prognostic signatures and Cox-regression survival models LSC signature score. The 17-gene LSC17 gene set was retrieved from ref. 31. For each sample, the LSC17 score was defined as the linear combination of gene expression values weighted by their respective regression coefficients. To identify TP53-sAML LSC signatures from our TARGET single-cell dataset, two different approaches were used. First, differentially expressed genes were identified as overexpressed in all Lin -CD34 + TP53-multihit cells regardless of their transcriptional classification (p53-all-cells) versus MF, HD and TP53-WT preleukemic cells; this gene set consists of 29 genes (Supplementary Table  4a ). For the second approach, the same analysis was performed, but TP53-multihit cells transcriptionally defined as LSCs (falling in the LSC-like cluster; Fig.
2a , middle) were specifically selected; this gene-set is comprised of 51 genes (p53LSC; Supplementary Table  4a ). Next, lasso cox regression with tenfold cross-validation implemented in the glmnet R package (version 4. 1-1) was used to identify p53-all-cells and p53-LSC genes that were associated with survival and to estimate their respective regression coefficients  86. Specifically, Harrel's concordance measure (C-index) was used to assess the performance of each fitted model during cross-validation. The best model was defined as the fitted model with the highest C-index. Subsequently, the coefficient for each gene estimated using the best model was used to compute the gene signature scores. Only genes with nonzero coefficient values were included in the final gene set. In total, 9 and 44 genes were retained from the p53-all-cells and p53-LSC gene sets, respectively.
Survival analysis. For each gene expression signature, patients were first split using the median gene expression signature score. This resulted in two groups of patients, namely patients with high expression scores (greater than or equal to the median) and patients with low expression scores (lower than the median), exemplified in Extended Data Fig. 6a, b. The Cox proportional hazards regression model implemented by the survival R package (version 3. 5-5) was fitted to estimate the hazard ratio associated with each feature. The log-rank test was used to test the differences between survival curves. The features analyzed here were LSC17, p53-all-cells and p53-LSC signatures. Patients with low gene expression signature scores (below median) and patients with TP53 WT status were specified as the reference groups in the model.
Evaluation of cell morphology Cell morphology from PDX models (Extended Data Fig. 3d ) and in vitro LTC-IC cultures (Extended Data Fig. 7f ) was assessed after cytospin of 50-100,000 cells onto a glass slide (5 min at 500 r. p. m. ) and May-Grünwald Giemsa staining, according to standard protocols. Images were obtained using an AxioPhot microscope (Zeiss).
Within each round, injections were spaced one or two days apart. Blood cell counts and analysis of PB chimerism along with mature lymphoid and myeloid populations were performed every 2-4 weeks by submandibular sampling of mice, while BM chimerism and HSPC populations were analyzed 18-20 weeks after transplantation. The antibodies used are detailed in Supplementary Table  7d. For dead cell exclusion, 7-AAD (Sigma-Aldrich) or DAPI (BD Biosciences) were used. FACS analyses were carried out on BD Fortessa or BD Fortessa X20 (BD Biosciences) and profiles were later analyzed using FlowJo (version 10. 1, BD Biosciences) or Kaluza (version 2. 1, Beckman Coulter) software.
The cells were then incubated with KCl 75 mM for 15 min at 37 °C and spun down. Following this, the cells were fixed in a methanol-acetic acid and then dropped onto glass slides. M-FISH was performed with the 21XMouse-Multicolor FISH probe kit (Metasystem Probes, D-0425-060-DI), following the manufacturer's instructions. For microscopy analysis, slides were mounted in Vectashield Antifade Mounting Medium with DAPI (2BScientific, H-1200). Images were acquired and analyzed using Leica Cytovision software (v7. 3. 1), on an Olympus BX-51 epifluorescence microscope equipped with a JAI CVM4 + progressive-scan 24 fps B&W fluorescence CCD camera. All cells were karyotyped, excluding metaphases severely damaged for technical reasons. The analysis of the M-FISH hybridized cells was blinded. The cells on each slide were scored for the presence of structural aberrations (translocations, and/or derivative chromosomes and fragments) and/or numerical abnormalities.
Acknowledgements We are grateful to patients and donors as without their generosity, this study would not have been possible. We also thank  S. Knapper , clinical https://doi. org/10. 1038/s41588-023-01480-1 study teams and other investigators involved in supporting sample collection, and  King's Health Partners Biobank  for providing access to samples. We thank  Z. Ren ,  T. Denaes  and  H. Duparc  for their help with mouse experiments and  S. -A. Clark  for help with sorting. We also thank  C. Soblechero  for the help with computational analysis. This work was funded by a  Medical Research Council (MRC)   Senior Clinical Fellowship  ( MR/L006340/1  to A. J. M. ),  CRUK   Senior Cancer Research Fellowship  ( C42639/A26988  to A. J. M. ),  Cancer Research UK (CRUK)   DPhil Prize Studentship ( C5255/A20936  to A.
The authors acknowledge the contributions of  N. Ashley  at the  MRC Weatherall Institute of Molecular Medicine (WIMM) Single Cell Facility  and MRC-funded  Oxford Consortium   for Single-Cell Biology  ( MR/M00919X/1 ). The authors would also like to acknowledge the contribution of the  WIMM Sequencing Facility , supported by the  MRC Human Immunology Unit  and by the  EPA  fund ( CF268 ), the  Gustave Roussy  flow cytometry platform and mouse facility. We also thank the  Oxford Genomics Centre at the Wellcome Centre for Human Genetics  (funded by  Wellcome Trust  grant reference  203141/Z/16/Z ) for the generation and initial processing of the OmniExpress SNP array data. The results published here are in whole or part based upon data generated by the TCGA Research Network (https://www. cancer.
Code availability Scripts to reproduce data preprocessing and all figures are available on GitHub (https://github. com/albarmeira/p53-transformation) and source data to reproduce the scripts is available at: https://zenodo. org/ record/8038152 (https://doi. org/10. 5281/zenodo. 8038152; data preprocessing) and https://zenodo. org/record/8060602 (https://doi. org/ 10. 5281/zenodo. 8060602; source data for figures).
Participants There were 361 participants in this preregistered, online study (https://osf. io/pfy7r). Participants were university students and gained course credits for their participation. Following careful quality control of participant data (see below in the section Quality control of data), the final sample consisted of 268 participants (age: mean = 21. 46 years; SD = 2. 20 years; 77. 61% female, 22. 39% male). Participants were randomly divided into three groups (15 s break, 30 s break, and self-paced). Participants in the three groups did not differ in age, education, sex, handedness, or working memory performance (Table  1 ). All participants had normal or corrected-to-normal vision, and none of them reported a history of any neurologic or psychiatric condition. Participants provided informed consent, and all study procedures were approved by the Research Ethics Committee of the Eötvös Loránd University (Budapest, Hungary) and was conducted in accordance with the Declaration of Helsinki.
Alternating serial reaction time task We used the ASRT task to measure implicit statistical and general skill learning separately. The ASRT task was programmed in JavaScript using the jsPsych framework  [de Leeuw, 2015 ; code is openly available on GitHub: https://github. com/vekteo/ASRT_rapid_consolidation (see also https://doi. org/10. 5281/zenodo. 7124730)]. During the ASRT task, a visual stimulus appeared on the screen (a drawing of the head of a dog) in one of four horizontal locations. Participants must have indicated the location of the target stimulus by pressing the corresponding key on the keyboard (from left to right, the S, F, J, and L keys on a computer keyboard). Participants were instructed to use their left and right middle and index fingers to respond to the targets. Unknown to the participants, the stimuli followed a probabilistic eight-element sequence, where pattern and random elements alternated with each other (e.
Procedure We used the Gorilla Experiment Builder (https://www. gorilla. sc) to host our experiment  (Anwyl-Irvine et al. , 2020) , which allows accurate stimulus and response timing in online experiments  (Anwyl-Irvine et al. , 2021). Data were collected between April 13, 2021, and October 31, 2021 (experiment material is available on Gorilla Open Materials, https://app. gorilla. sc/openmaterials/397611). Participants were randomly assigned to one of three versions of the task, which differed only in the duration of between-block rest periods. The between-block rest periods were either (1) 15 s breaks, (2) 30 s breaks, or (3) self-paced (i. e. , participants were allowed to continue the task with the next block whenever they were ready). The participants performed two practice blocks, then continued with 25 learning blocks, which took ;25 min to complete.
Each block consisted of 80 trials, corresponding to the eight-element sequence repeated 10 times. Accuracy and RT were recorded for each trial. After accomplishing the ASRT task we tested the participants' awareness of the hidden structure with a short questionnaire and a task based on the process dissociation procedure, which enables us to differentiate explicit and implicit processes in memory tasks  (Jacoby, 1991). Finally, they performed 0-back and 2-back tasks  (Kirchner, 1958)  to assess their working memory capacity (see https://doi. org/ 10. 5281/zenodo. 7100178). Data are available on OSF (https://osf. io/ukbfz/).
22% of all trials). To facilitate data processing and filter out noise, the blocks of ASRT were organized into units of five consecutive blocks  (Bennett et al. , 2007; Barnes et al. , 2008; Nemeth et al. , 2010) , for which we calculated statistical learning and general skill learning scores. Each task trial was categorized as the third element of a low-probability or a high-probability triplet (except the first two trials of each block that could not have been categorized as the third element of a high-probability or low-probability triplet). To measure the degree of implicit statistical learning, we calculated a statistical learning score by subtracting the median RT of the high-probability triplets from the median RT of the low-probability triplets. Then, to control the difference in base RTs between groups, we divided this learning score by the mean RT (standardized statistical learning scores).