The pyphe pipeline is designed to take different fitness proxies as input: endpoint colony sizes, colony growth curves or endpoint colony viability estimates from phloxine B staining (Figure 1). Image acquisition, image analysis, growth-curve analysis, data normalisation and statistical analysis are split into separate tools which can be assembled into a pipeline as required for each experiment and combined with other published tools, e.g. gitter (Wagih and Parts, 2014) for image quantification. Each tool takes and produces human-readable data in text/table format.

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In a typical workflow, images are acquired using pyphe-scan which provides an interface for image acquisition using SANE (Scanner Access Now Easy) on a Linux-type operating system. It handles plate numbering, cropping and flopping, and format conversion functionality for large image stacks. Optionally, image time-series can be recorded. Pyphe-scan was written to work with EPSON V800 scanners, the newer model in the series previously used by others (Takeuchi et al., 2014; Zackrisson et al., 2016).

Colony properties are then quantified from images using pyphe-quantify which can operate in three different modes. In batch mode (for colony-size quantification using grayscale transmission scanning) or redness mode (colony-viability estimation using phloxine B and reflective colour scanning), it separately analyses all images that match the input pattern (by default all jpg images in working directory), producing a csv table and qc image for each. In timecourse mode, colony positions are determined in the last image and the mask is applied to all previous images, extracting background-subtracted sums of pixel intensities for each colony/spot and producing a single table with the growth curves (one per column). Pyphe quantify reports a wide range of colony properties: colony area, overall intensity (an estimator that reflects thickness as well as area), circularity, perimeter and centroid coordinates, making this tool useful in cases where colonies are not arrayed. Image pixel darkness is known to scale non-linearly with true colony thickness/cell number (Zackrisson et al., 2016). Fitness estimates reported by pyphe-analyse are therefore related but not strictly the same as cell counts. If absolute population sizes are required for an experiment, the Scan-o-matic pipeline offers suitable calibration functionalities (Zackrisson et al., 2016). Pyphe-quantify algorithms are described in detail in Appendix 1 and Figure 1—figure supplement 1.

Spatial normalisation is performed for each plate and data across all plates are aggregated using pyphe-analyse to produce a single table for downstream hit calling and further analysis. Pyphe implements a grid normalisation procedure based on the one previously described (Zackrisson et al., 2016) as well as row/column median normalisation. Both strategies produce relative fitness estimates where a value of 1 corresponds to the fitness of the grid strain or the plate median respectively. We propose an improved placement of the grids in 1536 format (Figure 1—figure supplement 2) and implemented checks for missing colonies and normalisation artefacts. The main output is a single long table, containing one row per colony, with all position-, strain-, meta- and fitness-data as well as details about the normalisation. Algorithms are further described in Appendix 2 and Figure 1—figure supplement 2.

Finally, differential fitness is assessed using pyphe-interpret which produces summary statistics and p-values based on the complete data report from pyphe-analyse (Appendix 3). Pyphe-interpret gives users the option to either test for differences between strains in the same condition or between the same strain in different conditions. The latter is the recommended option for testing for condition-specific growth effects compared to a control condition.

Pyphe is designed to use different fitness proxies as input. In particular, it can use either maximum growth rates extracted from growth curves or endpoint colony size measurements. Previous studies have reported that information from growth curves are more precise (Zackrisson et al., 2016), but their acquisition requires substantially higher investment and produces large amounts of image data. While lower precision could be easily compensated by a higher number of replicates, growth curves provide the additional advantage that they capture the entire growth phase instead of a static snapshot. The results obtained in endpoint measurements might, therefore, depend on the timepoint used for the measurement. For example, the fitness of a strain with a long lag phase but high maximum growth rate may be underestimated if an early timepoint is chosen.

To assess the extent to which the choice of the timepoint matters, we recorded image time series for 57 S. pombe wild strains growing in 1536 spots per plate in approximately 20 replicates on 8 different media (Supplementary file 1). The conditions were designed to produce different growth rates and dynamics, and included mixes of different carbon sources with yeast extract as nitrogen source in rich media and different nitrogen sources in minimal media. These strains are genotypically and phenotypically diverse and display a broad range of growth characteristics (Jeffares et al., 2015). First, colony areas were extracted with gitter (Wagih and Parts, 2014), and relative, corrected colony sizes were computed for each image using the grid normalisation implemented in pyphe-analyse and averaged for each strain. We show an exemplary analysis of a single condition (standard rich media) in Figure 2 and a detailed analysis of all conditions in Figure 2—figure supplement 1 and 2. Relative colony sizes remained largely constant after the period of fast growth had come to end at roughly 16 hr (Figure 2A). Concordantly, a correlation matrix of all timepoints showed near perfect correlation of timepoints with the 48 hr end point from 16 hr (Figure 2Biii). Notably, all timepoints were correlated with the initial timepoint, albeit much lower, suggesting a significant bias introduced by the amount of initially deposited biomass. In our hands, this problem is more pronounced with wild strains than with knock-out collections as the former exhibit a variable degree of cell aggregation. However, we overcome this issue by reporting strain fitness as a ratio of growth in an assay condition relative to a control condition, in which case this bias is neutralised. We next analysed image timeseries with pyphe-quantify in timecourse mode, extracted slopes with pyphe-growthcurves (Appendix 4) and applied grid correction in pyphe-analyse. Later timepoints generally showed a much better correlation with maximum growth rate compared to early ones or those taken when growth is most rapid (Figure 2 Bi+ii). Across all conditions, the median correlation of corrected maximum slopes with corrected colony sizes at the final timepoint was 0.95 (Figure 2—figure supplement 3). We conclude that late timepoints should be chosen for endpoint measurements, when the readout is stable and correlates well with the maximum growth rate.

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The choice of timepoint also affects the level of noise. For uncorrected colony sizes, the coefficient of variation (CV, ratio of the standard deviation to the mean) of 96 replicates of the control strain, dispersed evenly in the plate, dropped steadily during the rapid growth phase, reaching a minimum around 20 hr when it started to rise again (Figure 2C). This is likely due to edge and other spatial effects which affect later growth as nutrients deplete and plates start to dry unevenly. After normalisation, the CV was generally lower, and this later rise in noise could be compensated so that the CV remained near its minimum. The CV of the maximum slopes was lower than obtained with endpoints. However, CV values alone are insufficient to judge the effectiveness of a normalisation strategy, as it reflects precision of the reported values but not the method’s ability to delineate differences between strains. As an additional indicator, we therefore used the ratio of the variance of the controls and the variance of the entire dataset, the fraction of unexplained variance (FUV), which indicates the level of noise relative to the biological signal in the data. Overall, the FUV behaved similarly to the CV and was at a minimum at around 20 hr for the uncorrected data. With corrections, this minimal value was largely maintained until the end of the experiment. A lower FUV can be obtained by using maximum slopes rather than individual timepoints. The other, non-standard conditions tested showed similar qualitative dynamics, but with noise levels and timings varying between conditions as expected (Figure 2—figure supplements 1–3).

Although correcting for position and batch effects is essential for high-throughput experiments conducted on agar plates, there is a danger that any normalisation strategy could also create false positives. Specifically, a grid colony positioned next to a rapidly growing colony will be smaller (due to nutrient competition), leading to underestimation of the expected fitness in that area which will further increase the fitness estimate of neighbouring colonies. This argument applies equally the other way around; grid colonies positioned next to slow growers have access to more nutrients. Indeed, after reference grid normalisation, we often observed a (generally weak but detectable) secondary edge effect for colonies positioned in the next inward row/column (Figure 1—figure supplement 2B). We found, however, that this effect can be remedied by an additional row/column median normalisation, if the majority of strains in each row/column has no growth effect (as is usually the case when working with knock-out collections). Being a toolbox (not a black box), pyphe requires the user to think about their strains, choice of control strains as well as plate layout and to choose a suitable normalisation. Users have the option to perform only one of the two implemented normalisations or both (in which case grid normalisation will be done before row/column median normalisation), which allows users to tailor data analysis to their experiments.

To gauge if phenotype exaggeration globally presents a problem in other parts of the plate, we compared raw and final corrected colony sizes and maximum slopes to the respective sum of all its 8 neighbours. For uncorrected fitness values, there was generally a positive correlation (stronger for colony sizes than for slopes), indicating that regional plate effects dominate over competition between neighboring colonies. This bias was removed after correction. Importantly no negative correlation was observed. We conclude that grid correction does not lead to any significant effect exaggeration.

The addition of phloxine B to agar medium stains colonies in different shades of red, reflecting the fraction of dead cells, which can provide an additional phenotype readout from the same colony used for growth measurements. To investigate how colony size and redness relate, we used the pyphe pipeline to characterise 238 S. pombe single-gene deletion strains in 70 conditions in biological triplicates (n = 59,350 total colonies profiled, including controls but excluding grid colonies, Supplementary file 2). The two fitness proxies showed little correlation (Pearson r = −0.088) after correction of colony sizes using the grid approach with subsequent row/column normalisation and correction of redness scores by row/column median normalisation only (Figure 3A). Normalisation strategies for redness images are described in Figure 3—figure supplement 1. Many mutant-condition pairs showed a strong phenotype in only one of the two read-outs. Noise levels of redness scores were very low (CV = 1.04%) and the biological signal strong (FUV = 7.83%). We conclude that the phloxine B redness scores provide robust, precise information on mutant fitness, and serves as a largely orthogonal and independent measure compared to the (well correlated) growth rate or colony size measurements.

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Phloxine B can be toxic if exposed to light (Qi et al., 2011), so we tested whether phloxine B changes growth parameters by determining colony sizes for our mutant set in 7 conditions. Measurements with and without phloxine B were performed in different batches and in different weeks to exclude that batch effects increase the correlation. Within the 14 phenotype vectors measures in total, identical conditions with and without phloxine clearly and consistently clustered together (Figure 3B). The median correlation for the 7 condition pairs with and without phloxine was 0.92, which was substantially higher than that of all possible pairs from the 14 phenotypes (Figure 3C). We conclude that the main driver of the biological signal is the condition and not whether phloxine B is included. We tested for specific gene-condition pairs showing differential growth on media with and without phloxine (Supplementary file 3). This analysis identified a single gene, the trehalose-6-phosphate phosphatase tpp1, as having a small slow-growth phenotype on rich media (ratio of medians of corrected colony sizes 0.89, p_adj = 0.028) and a moderate effect on minimal media (ratio of medians = 0.79, p_adj = 0.049). In order to account for such genotype-specific effects, differential fitness should generally be assessed against a control condition also containing phloxine B.

Finally, we tested whether and how the colony redness score relates to the viability of cells in the colony. We determined colony composition and viability status at the single cell level using ImageStream flow cytometry. Across 23 samples, obtained from colonies with varying redness scores (Figure 4A), phloxine B staining classified cells into three populations (Figure 4B and C, Figure 4—figure supplement 1, Supplementary file 4): live cells which showed a background level of staining, dead cells which were brightly stained, and lysed or damaged cells which showed no staining. The fraction of live cells (alive/(dead+lysed+alive)) was inversely correlated (Pearson r = −0.88, with some grouping of strains) with colony redness scores obtained with pyphe-quantify and row/column median corrected by pyphe-analyse (Figure 4D). This correlation was stronger than the correlation of colony redness scores with the fraction of live and lysed cells (lysed+alive /(dead+lysed+alive), r = −0.78, Figure 4—figure supplement 2), suggesting that lysed cells, while not stained in the FACS, do contribute to colony redness. This is explained by the dye not being washed out in colonies, unlike in cells resuspended in PBS for flow cytometry analysis. We next asked how well phloxine B staining agrees with a distinct, established dead-cell stain (LIVE/DEAD). In wild-type cells, staining with both dyes agreed closely (accuracy 99.3% using LIVE/DEAD classification as ground truth, Figure 4E). We conclude that phloxine B staining, combined with our imaging and analysis pipeline, provides a sensitive and accurate readout reflecting the proportion of live/dead cells in a colony.

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We have shown that for colony sizes similar results are obtained even if the plates are incubated for a few days after rapid growth has ended. The same is not necessarily expected for colony redness scores. In fact, colonies might appear red due to strains producing dead cells during growth or due to death when non-dividing cells reach the end of their chronological lifespan, which is temporally decoupled from growth. Certainly, if colonies are left for a very long time, cells will age, with striking physiological adaptations and eventually cell death (Váchová and Palková, 2018). To investigate how much the choice of timepoint matters with colony redness scores, we acquired colour images every 20 min for 48 hr on standard rich media for the set of 238 S. pombe single gene knock-outs. Each image from the experiment was analysed with pyphe-quantify in redness mode. In general, we do not recommend analysing images of young, small colonies for redness. All colonies showed a background signal unspecific to the dye and this increased with colony thickness. During early timepoints, we therefore detect an increase in raw, uncorrected redness (Figure 5A).

Figure 5

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Timepoints generally correlated well after rapid growth had ended for a period of at least 24 hr (Figure 5B). The CVs and FUVs were stable over this time as well (Figure 5C). These robust characteristics thus allow sufficient time for scanning without the need to hit a certain ‘sweet spot’. For our work with knock-out libraries, we imaged plates soon after growth had slowed down. We identified a group of mutants with the strongest redness phenotype in the set (corrected colony redness >1.05). These colonies showed a clear and strong increase in redness during growth (Figure 5D), suggesting that here redness was not temporally decoupled from growth. As for colony growth, we conclude that the exact timepoint to determine colony redness is not that critical, as long as colonies are not growing rapidly anymore.

Pyphe is open software published under a permissive license. We welcome bug reports, feature requests and code contributions through https://github.com/Bahler-Lab/pyphe. Pyphe is also available through the Python Package Index at https://pypi.org/project/pyphe/.

An overnight liquid culture of strain 972 h- in YES medium was pinned in 96-colony (8 × 12) format on YES agar medium, using a RoToR HDA pinning robot (Singer Instruments) and grown for two days at 32°C. This grid was combined with randomly arranged plates of the 57 wild strains in 1536 (32 × 48) format and grown for 2 days at 32°C. Strains were then copied onto fresh assay plates, using the 1536 short pinning tool at low pressure. Plates were placed in scanners (EPSON V800) in an incubator at 32°C and images were acquired every 20 min for 48 hr using pyphe-scan-timecourse. Growth curves were extracted using pyphe-quantify in timecourse mode with the following settings: --s 0.1. Growth curve parameters were extracted with pyphe-growthcurves with the --fitrange 12 option. Individual images were analysed with gitter using the following settings: --inverse TRUE --remove.noise TRUE. Grid correction of maximum slopes and individual timepoints was performed in pyphe-analyse.

238 mutants, broadly spanning GO Biological Function categories plus several uncharacterised genes, were selected from a prototroph derivative of the Bioneer deletion library (Sideri et al., 2015). Strains were arranged in 384-colony (16 × 24) format with a single 96 grid placed in the top left position, so that the grid includes one colony in every fourth position within the 384-colony array. To prepare replicates, this plate was independently pinned 3 times from the cryostock on solid YES media for each batch. From these plates, colonies were then spotted on assay plates containing various toxins, drugs or nutrients. The conditions used in Figure 3B are: EtOH10 is YES+10% (v/v) ethanol, VPA10 is YES+10 mM valproic acid, MMS0.005 is YES+0.005% (v/v) methyl methanesulfonate, Forma2.5 is YES+2.5% (v/v) formamide, Diamide2 is YES+2 mM diamide, EMM is standard Edinburgh Minimal Medium, YES is standard Yeast extract with supplements and 3% glucose. Assay plates were usually grown for 2 days at 32°C but this varied according to the strength of the stress slowing the growth of the colonies. After incubation, images were acquired using EPSON V800 scanners and pyphe-scan and quantified with gitter (see options above) or pyphe-quantify in redness mode. Grid correction and subsequent row/column median normalisation of maximum slopes and individual timepoints was performed in pyphe-analyse. Row/column median normalisation was applied to redness data plates. For the size data set, 0-sized colonies and colonies with a circularity below 0.85 were set to NA. Plates with a CV > 0.2 or FUV >1 were removed as those most likely represent conditions in which the stress was too strong or where technical errors occurred.

We picked 23 colonies with varying redness from the collection of 238 S. pombe deletion strains grown on solid YES with 5 mg/L phloxine B for 3 days at 32°C and resuspended in 1 mL of water. For analysis of phloxine B staining, 500 µL of this cell suspension were centrifuged at 4000 g for 2 min, the supernatant was removed and the pellet resuspended in 75 µL of PBS. For analysis of phloxine B and LIVE/DEAD co-staining, 500 µL of the same suspension were centrifuged at 4000 g for 2 min, the supernatant was removed and the pellet resuspended in 300 µL of LIVE/DEAD solution (LIVE/DEAD Fixable Far Red Dead Cell Stain Kit, for 633 or 635 nm excitation, ThermoFisher Scientific, Cat. no. L34974). LIVE/DEAD solution was prepared according to manufacturer’s instructions (1:1000 dilution in H2O from a stock solution dissolved in 50 uL of DMSO). The pellet was resuspended and incubated for 30 min in the dark. Cells were then spun down and resuspended in 75 µL of PBS.

Immediately prior to analysis, samples were sonicated for 20 s at 50W (JSP Ultrasonic Cleaner model US21), and transferred to a two-camera ImageStreamX Mk II (ISX MKII) imaging flow cytometer (LUMINEX Corporation, Austin, Texas) for automated sample acquisition and captured using the ISX INSPIRE data acquisition software. Images of 5000–12,000 single focused cells were acquired at 60x magnification and low flow rates, using the 488 nm excitation laser at 90 mW to capture phloxine B on channel 3; 642 nm excitation laser at 150 mW to capture LIVE/DEAD cells on channel 11; bright field (BF) images were captured on channels 1 and 9, and side scatter (SSC) on channel 6. For co-stained cell analysis, to generate a compensation matrix, cells stained either with phloxine B or with LIVE/DEAD dye individually were captured without brightfield illumination (BF and SSC channels were OFF). The compensation coefficients were calculated automatically using the compensation wizard in the Image Data Exploration and Analysis Software (IDEAS) package (v6.2). Populations of interest (single focused cells) were gated in IDEAS and the features of interest (dye intensities) were then exported for further analysis using Python. Intensity values were subtracted by their minimum over all samples (which was slightly below zero) and added to 1 prior to log10 transformation. Thresholds for the three populations were set manually based on the intensity histogram across all samples.

The mutant collection was woken up from the cryostock on YES media and copied onto fresh YES with 5 mg/L phloxine B. Images were acquired every 20 mins with pyphe-scan-timecourse. Images were analysed with pyphe-quantify redness with --s 0.1. Timepoints were grid corrected using pyphe-analyse.

Pyphe-quantify is a command line tool for the analysis of images containing microbial colonies based on scikit-image (van der Walt et al., 2014). By default, it analyses all. jpg image files in the directory where it is executed. Alternatively, the user can set a pattern to specify input images and all image formats supported by scikit-image can be used (e.g. tiff, jpg, png). Pyphe-quantify can operate in three distinct modes: batch (analyse colony areas in each image separately), redness (analyse colony redness in each image separately) and timecourse (analyse colony area and thickness in a stack of images from a timeseries) mode. Pyphe-quantify produces simple csv files (one per plate for batch and redness mode) which can be directly processed further by pyphe-analyse or analysed using other software.

Common to all three modes is the need to match identified colonies in the image to their row-column position, which is configured using the --grid option. Pyphe-quantify implements an automatic grid detection (used by setting --grid auto_384 or --grid auto_1536) which identifies peaks in rows and columns pixel intensities. This is done using the find_peaks function from scipy’s signal module (using the image dimensions and number of colonies per row/column to define a suitable minimal distance between peaks). The distance between peaks is then determined using an outlier-robust, trimmed mean. The maximum pixel position of the first colony is then determined as (image dimension - (mean distance between colonies * (colonies per row/column - 1)). A cosine function with the same periodicity as the distance between colonies is created. The fit of that function to the data (row/column mean intensities) is then evaluated by taking the sum of squared differences for each possible pixel offset from 0 to the maximum previously determined. The expected positions of all colonies are then computed using the start position and the mean distance.

Automatic grid detection is in our hands the most common reason for image analysis tools to fail. We have therefore given the user full control over defining expected colony position. If all plates were scanned with the same fixture taped firmly to the scanners, colony positions are actually highly consistent across images. This means grid definitions can be ‘hard-wired’. The fixture provided by us has been preconfigured and is done using the -grid pp_384 or --grid pp_1536 options. Simultaneously, with the goal of maximum flexibility, pyphe-quantify offers the possibility of manually defining grid positions. In that case, the argument has to be in the form of 6 integer numbers separated by ‘-':<number of rows>-<number of columns>-<x position of the top left colony>-<y position of the top left colony>-<x position of the bottom right colony>-<y position of the bottom right colony>. Positions must be integers and are the distance in number of pixels from the image origin in each dimension (x is width dimension, y is height dimension). The image's origin is, in line with scikit-image convention, in the top left corner. Getting those coordinates is trivial and can be done, for example in Microsoft Paint. The option to manually define grid positions is important in our experience, as automatic gridding is the step where most image analysis tools typically fail (especially if plates have many missing colonies or images are rotated).

In batch mode, pyphe-quantify will analyse colony sizes in each image individually. First, morphological components are identified by thresholding the image. By default the Otsu method is used to find the threshold (Otsu, 1979), but this can be tuned by the user by providing a coefficient to be used with this threshold or an absolute threshold. Components are then filtered by size to exclude erroneous identification of small particles, such as dust, as colonies. Border components are removed. Components are then matched to grid positions. By default, a component is assigned to a particular position if it is less than a third of the distance between two grid positions away from a position. This threshold can be set by the user. This means that in case of missing colonies, there will be no data for the corresponding grid correction, and this position will be missing from the output file (i.e. it will not be 0). Similarly, two components can be assigned to the same grid position in the case of contaminations. This can be disabled by the user to retain only the component nearest to the grid position. When reporting all colonies, pyphe-quantify can be used for plates with non-arrayed colonies. An output table is saved which contains colony area, mean intensity (an estimator that reflects thickness), circularity, perimeter and centroid coordinates. Area measurements are in very close agreement with those obtained with gitter (Figure 1—figure supplement 1A). Mean intensity measurements reported by pyphe-quantify are dependent on colony area as colonies get thicker as they grow (Figure 1—figure supplement 1B). Pyphe-quantify exports a qc image for every image analysed indicating the identified colonies and their assigned grid positions.

In timecourse mode, the final image of the time course is analysed as described above and the obtained mask (indicating the position of each colony) is then applied to all previous images of the timecourse. The background subtracted sum of pixel intensities (the mean intensity times the number of pixels) for all images combined, that is the growth curves, is reported in a single file.

Finally, pyphe-quantify can analyse colony redness (Figure 1—figure supplement 1C). Phloxine B stains dead cells within the colonies and these are usually not homogeneously distributed upon close inspection. However, for simplicity, we have developed an image analysis workflow which extracts the mean redness of colonies in high-density arrays, providing a single quantitative readout. We decided to use reflective scanning with our Epson V800 scanners (implemented in pyphe-scan). This is fast and produces images with consistent properties, but with the caveat that the focus position is just above the scanner glass and colonies are therefore somewhat out of focus. Additionally, there is a strong, uneven background signal from the media and colour artefacts (appearing as bright stripes between colony columns) which required a different image analysis approach. The images are first adjusted to make colony redness more visible by multiplying the red, green and blue channels by 0, 0.5 and 1, respectively and their sum is taken to produce single-channel/grayscale images. The background value for each pixel is estimated by blurring the image with a Gaussian filter with a standard deviation of the number of pixels in the image divided by 10000. The background is subtracted from the image which is then inverted. Colonies are detected by local thresholding and processed further as described for batch mode above. The mean intensity for each colony is computed from the processed image and reported in a similar file as described for batch mode. The produced QC images allow to verify grid placement and visualise the colour readout on the actual image.

This text describes the steps performed by pyphe-analyse during the analysis of a typical batch of plates. All functions and objects are also available for use as a python package. At the core of pyphe is the Experiment object which is initialised from the Experimental Design Table (EDT). The EDT is first checked for obvious errors, including the uniqueness of plate IDs and paths to data files and if these files exist. Data from the image analysis output files is then loaded using appropriate parsers. Layout files are loaded if set by the user.

Spatial normalisation is then performed if requested by the user. Pyphe implements a grid correction procedure similar to that used previously (Zackrisson et al., 2016). In that paper, the authors use 1536 format arrays and place a 384 grid in the top left position of the plate. This means one quarter of plate positions are taken up by the grid. It also creates a problem because the right and lower edge of the plate are not covered by the reference grid. We have developed a small improvement of this technique by placing two 96 grids in opposite (top left and bottom right corners, Figure 1—figure supplement 2A). This leaves only two small corners of the plate (bottom left and top right) not covered by the interpolated grid surface. We solve this by extrapolating the grid by estimating the theoretical colony size of a grid strain in those corners using a linear model and the colony sizes of the two neighbouring grid colonies as input. Model parameters are determined for each experiment based on all plates using regression. We typically achieve accuracies of >90% (Figure 1—figure supplement 2C). This allows us to use grid correction over the entire plate without loss of data. Pyphe-analyse specifically looks for grid colonies with a colony size zero (this is reported by gitter if no colony has been detected), flags those as pinning errors and marks all neighbouring colonies NA in the final output. The grid correction itself is done using scipy.interpolate’s griddata function, fitting a piecewise cubic, continuously differentiable, approximately curvature-minimizing polynomial surface to the grid positions (real grid positions and extrapolated corners). The surface created in that way represents the expected fitness for each position if the strain growing there was the same as the grid strain. The observed fitness is then divided by this expected value, producing the corrected and relative (to the grid strain) fitness of each colony.

We have noticed that doing the grid correction this way slightly over-corrects for the edge effect for strains in the row neighbouring the edge (Figure 1—figure supplement 2B). This is because the edge effect is usually restricted to the outermost edge only. But the values of the reference grid in the next row/column will be most strongly determined by the grid colony on the edge, leading to an over-correction (underestimation of fitness) in these positions. We therefore often perform an additional row/column median normalisation after grid correction to remedy this. For this, pyphe-analyse computes median values for each row and column and divides the data by both. The data is then re-scaled to a median of 1 by dividing by the overall plate median. Note that a median correction is not valid if the median is not a good estimate of the null effect. For this reason, we strongly discourage row/column median normalisation for plates in 96-colony format (where the median is computed from only 8 or 12 values). If plates contain a large number of slow or fast growers a median normalisation is also unsuitable, especially if these are distributed non-randomly in the plate. For work with knock-out libraries, where most gene knock-outs have no effect in any given condition, and with wild strains which show a median-centred distribution of subtle growth phenotypes, the additional row/column median normalisation effectively neutralises the secondary edge effect.

In some cases, the normalisation procedures can lead to artefacts. Grid normalisation can result in negative corrected fitness values if grid colonies are very small in a region (Figure 1—figure supplement 2D). Row/column median normalisation can produce infinite values if more than half the colonies in a row/column have size 0. These artefacts are detected by pyphe and set to NA.

Pyphe gives the option to produce QC plots for each plate in the experiment in which case a pdf file will be generated containing heat maps for all numerical data associated with that plate. Finally, all data is collated in a single table which contains position information, layout information, all metadata provided by the user in the EDT, raw and corrected fitness values, and details about the grid correction.

Pyphe-interpret is a tool for statistical analysis of fitness data. It takes data reports produced by pyphe-analyse (or other data in a suitable tidy format) which contain a single line per colony, listing strain, condition and fitness information. The column names in which to find each of these are set by the user. The tool first checks the input and prints a summary to the command line listing the number of strains, conditions, plates and total number of data points. Next, QC filters based on circularity and 0 fitness are applied if set by the user. The number of excluded data points is reported. The tool then produces a table which lists all replicates for each strain-condition pair in wide format (see documentation folder on GitHub for an example). This table (which is also exported in csv format) allows to perform t-tests highly efficiently in a vectorised manner using the ttest_ind function for masked arrays from the scipy’s mstats_basic module. Pyphe-interpret requires the user to define a grouping (variable to use as the grouping variable for t-test) and the ‘axis’ column across which to apply multiple t-tests. This may initially seem complicated but enables the use of pyphe-interpret in two distinct scenarios: (1) Check for each condition separately (--grouping_column <condition_column>) if there is a significant difference in means between a mutant strain and a control strain (--axis_column <strain_id_column>); or (2) check for each strain separately (--grouping_column <strain_id_column>) if there is a significant difference in the means of the strain in the assay condition vs the control condition (--axis_column <condition_column>). We normally use the second option as it tests for condition-specific growth differences and it does not return significant results if a strain is consistently faster or slower growing than the grid strain. We use Welch’s t-test (equal_var = False) as this does not assume homogeneity of variances. P-values are corrected by the Benjamini-Hochberg method across the specified axis (i.e. across all strains in scenario 2 or across all conditions in scenario 1). The tool produces a table listing summary statistics (mean_fitness, mean_fitness_log2, median_fitness median_fitness_log2, observation_count stdev_fitness) and a statistical assessment of differential fitness (mean_effect_size, mean_effect_size_log2, median_effect_size, median_effect_size_log2, p_Welch p_Welch_BH p_Welch_BH_-log10).

Pyphe-growthcurves is a simple tool for non-parametric growth curve analysis. It was written to directly use data produced by pyphe-quantify timecourse as input but other types of growth data can normally easily be adapted. There are various packages dedicated to growth curve analysis (Fernandez-Ricaud et al., 2016; Kahm et al., 2010; Veríssimo et al., 2013) which have more functionalities, but the goal here was to provide a simple solution integrated into pyphe which works well with data typically handled within the pyphe pipeline. The input data needs to be in csv format and contain one column per growth curve containing the population sizes in the right order (top to bottom). The first column must contain the timepoints and those must be numerical (i.e. not ‘1 hr’, ‘2 hr’, but 1.0, 2.0). A single input csv is analysed every time pyphe-growthcurves is run. Maximum slopes of growth curves are determined by linear regressions to a sliding window of size d. The regression with the highest slope is retained and the slope is reported together with the timepoint at which it occurred (center of window), the R² of the regression, as well as its y- and x-intercepts. Lag phases can be determined by the absolute or relative method. The absolute method simply returns the timepoint at which the population size crosses the user-defined threshold. For the relative method (which is the default), the average of the first n timepoints is taken as the initial biomass. The algorithm then returns the timepoint at which the population sizes exceeds p*initial biomass, where p defaults to 2 (i.e. the time taken for the first population doubling is reported). Please note that no interpolation of population sizes between timepoints is currently implemented, but simply the timepoint where the threshold is crossed is returned. A table in csv format is created which lists all above-mentioned parameters and this can be read directly into pyphe-analyse. If the --plots option is set, the tool produces a pdf showing all growth curves and visualising the extracted parameters.

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Mimoza Hoti helped with the wet lab part of phenotyping the 238 knock-out mutants. This research was funded by Wellcome Trust Senior Investigator Awards to JB [grant number 095598/Z/11/Z] and to MR [grant number 200829/Z/16/Z], as well as a BBSRC Project Grant to JB [grant number BB/R009597/1]. This work was also supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001134), the UK Medical Research Council (FC001134) and the Wellcome Trust (FC001134).

1. Received: January 14, 2020
2. Accepted: May 21, 2020
3. Version of Record published: June 16, 2020 (version 1)

© 2020, Kamrad et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.